is bitcoin like gold? an examination of the hedging and safe haven properties of the virtual...

Is Bitcoin like Gold?

An Examination of the Hedging and Safe Haven

Properties of the Virtual Currency

Report on research presented in partial fulfilment of the requirements

of the examination for

MASTER OF SCIENCE IN FINANCE

Cormac Ennis

Supervisor: Dr. Constantin Gurdgiev

Trinity College Dublin

August 2013

i

Abstract

Whilst there has been significant focus on the virtual currency bitcoin in the

financial media, to date there has been limited academic discourse in the

economic and financial literature. This dissertation is a first attempt at

exploring the developing correlations between bitcoin and more traditional

asset classes. In particular I attempt to answer a specific question: Is bitcoin

like gold? I qualitatively discuss the theory underlying this idea with reference

to bitcoin’s decentralised nature, fixed money supply and creation by “mining”.

In addition I focus on gold’s role as a hedge and a safe haven for stocks,

bonds and currencies with a view to determining whether bitcoin could play a

similar role. Using a GARCH analysis for the US and EU I find that for the

most part bitcoin returns are statistically independent of equity and bond

markets due to the low correlations estimated. An interesting finding of my

research is that bitcoin may play a weak hedge and safe haven role for

sovereign debt markets in the US and Europe, which could be explained by

its exogeneity to the global network of financial institutions and governments.

I also find that bitcoin may be a hedge for the euro, but not for the dollar. In

addition the estimated coefficients for currency markets are consistently

larger than those representing equities or bonds, supporting the idea that

bitcoin is an alternative monetary asset.

ii

Executive Summary

Designed and implemented by an anonymous programmer (or team of

programmers), bitcoin is to date the most successful and widely used

example of a virtual currency. It is a “…digital, decentralised, partially

anonymous currency not backed by any government or other legal entity, and

not redeemable for gold or any other commodity” (Grinberg, 2011, p. 160). In

the short time since its inception in 2010 the price of one bitcoin has

fluctuated dramatically against the US dollar from an initial price of $0.05 to a

high of over $260. This exponential growth has attracted attention from

investors and widespread coverage in the financial media. Despite the

significant media coverage, to date there has been an absence of extensive

academic discourse within the economic and financial literature on the topic

of bitcoin. My primary research focus is on exploring bitcoin as an exotic

asset and investigating the developing correlations and informational

dependencies between bitcoin and other markets. In particular, I address the

anecdotal evidence, often featured in the financial media, that bitcoin may

behave like a form of virtual gold. This comparison has led to the idea that:

like gold, bitcoin may perform as a hedge or a safe haven against more

conventional securities.

The dissertation begins by providing an overview of the relevant academic

literature with a focus on the questions: what is bitcoin? And how is bitcoin

like gold? The two most interesting aspects of bitcoin as a currency system

are its decentralised nature and its fixed money supply. Bitcoin is

decentralised as it is not issued or managed by any one central authority.

Bitcoins are instead created by a process called ‘mining’. This involves users

running a program on their computers to solve complex mathematical

problems with the help of other computers on the network. For each problem

solved miners are rewarded with a fixed amount of newly created bitcoins as

an incentive for supporting the security and integrity of the network. The

amount of money created by mining is determined by an algorithm which

ensures that the supply of bitcoins is fixed. The total number of bitcoins in

existence will approach, but never reach, 21 million by approximately the year

iii

2040. This built in scarcity, along with the use of the term mining, has led to

frequent comparisons with gold, and bitcoin’s description as a “synthetic

commodity money” (Selgin, 2013). The fixed supply means that as long as

demand is increasing, through increased adoption by new users, the virtual

currency will be intrinsically deflationary. Whilst this poses problems for its

use as a currency, it is attractive to investors pursuing high returns.

Gold is generally found to be a monetary asset and performs as an effective

hedge against inflation and US dollar devaluations. I argue that bitcoin may

play a similar role due to its deflationary nature and its insulation against the

activities of central banks, which may from time to time debase the currencies

they manage. In addition as it is essentially an alternative currency, intuitively

one would expect that the relationship between bitcoin and currencies to be

the strongest which would support the idea that bitcoin is a monetary asset.

The relationship between gold and equity and bond markets is also well

documented. Generally gold is found to have a clear negative relationship

with stocks and to have little relationship with bonds. A reason put forward for

this by Ciner, Gurdgiev et al. (2010, revised 2012) is that “gold is a market

sentiment proxy that is more likely to impact riskier assets than fixed income

securities.” Due to the low participation in the bitcoin market by institutional

investors, there is a lack of clearly defined capital flows between the virtual

currency and traditional capital markets which intuitively would lead to low

correlations. Bitcoin’s exogeneity to the conventional financial system may

however allow it to play a hedge role against sovereign debt markets as it

does not rely on the reputation or supervision of any national or supranational

body.

Gold’s role as a safe haven in times of extreme negative returns is also well

documented. Historically it has been found to be a safe haven for equities,

bonds, and the dollar. However, in more recent studies the idea of gold as a

safe haven has been challenged with a possible reason being that the rise in

popularity of gold linked products “has caused a decline in its primary

attraction for many financial market participants, which is the notion that gold

iv

can be trusted as a safe haven against [the] equity market volatility” (Ciner,

Gurdgiev et al., 2010, revised 2012).

In order to determine whether bitcoin is a hedge or a safe haven for various

asset classes I use two econometric models based on those used by Baur

and Lucey (2010) and Ciner, Gurdgiev et al. (2010, revised 2012). For

estimation I include variables to represent stocks, bonds and currencies for

both US and EU markets. The equity market proxies are major indices from

both regions, the bond market proxies are benchmark ten year government

bond yields (using German bonds for the EU) and the currencies are the US

dollar and the euro. I estimate two GARCH (1, 1) models, one to determine

bitcoin’s correlations with each asset class, and one focusing on correlations

with periods of extreme negative returns. If bitcoin is a hedge it will be

negatively correlated with other assets on average and if it is a safe haven it

will be negatively correlated with periods of extreme negative returns. For

extreme negative shocks I use quantiles representing the bottom 5 and 10%

of returns for stocks and currencies, and the top 5 and 10% of spikes in yields

for bonds.

My results show that bitcoin does not play a hedge or a safe haven role for

US equity markets and is positively correlated with stock returns on average.

For European markets bitcoin returns are negatively correlated with stock

returns on average, meaning bitcoin could possibly play a hedge role. It is

important to note that the estimated coefficients are small and of varying

significance indicating that bitcoin returns are largely orthogonal to equity

markets in both the EU and US, and suggesting that bitcoin may have a role

as a diversifier.

For US and EU bond markets I find that bitcoin is a hedge as it is positively

correlated with yields. In addition, I find that bitcoin is a safe haven for EU

markets for both quantiles and a safe haven for US markets for the most

extreme periods of spikes in yields (top 5% quantile). The coefficient

estimates for the effect of changes in bond yields on bitcoin are small

indicating a weak relationship between the two markets. Despite the weak

v

relationship, the fact that bitcoin is negatively correlated with bonds on

average (positively correlated with yields) is interesting as this indicates that it

may play a different role in financial markets to that played by gold.

For currency markets, the coefficient estimates representing bitcoin’s

relationship with the dollar are consistently positive, whilst those with the euro

are consistently negative, implying that bitcoin may serve as a hedge against

the euro but not the dollar. I also find that bitcoin may play a safe haven role

for extreme negative returns on the dollar but not on the euro. It is interesting

to note that the estimated coefficients for the currency markets are

consistently larger than those representing equity or bond markets. This is

supportive of the idea that bitcoin is a monetary asset, a similarity it shares

with gold.

My main conclusion from estimation is that currently bitcoin returns are largely

orthogonal to returns in other markets. It is important to realise that as an

asset bitcoin is still in an early development stage. As a result of this the

correlations between it and other markets are much more volatile than those

between more mature asset classes. If bitcoin adoption continues at the

current rate and a clear regulatory framework is put in place, it is likely that

more institutional investors will enter the market, strengthening the

correlations between bitcoin and other more traditional investment vehicles.

Further research could focus on the dynamic nature of these developing

relationships, perhaps using a dynamic conditional correlation (DCC)

methodology similar to that used by Ciner, Gurdgiev et al. (2010, revised

2012). In addition the results from estimating bitcoin’s relationship to bond

markets could be studied further. For the Eurozone in particular the effect of

changes in the spread between core and periphery bond yields on bitcoin

could be examined as these are likely to better reflect negative shocks to the

European bond market.

vi

Acknowledgements

I would like to express my deepest appreciation to all those who provided me

with help in writing my masters dissertation, without whom it would not have

been possible.

In particular, to my supervisor Dr. Constantin Gurdgiev I would like to say

спасибо (thank you) for his advice, guidance and inspiration throughout my

research. Furthermore I would like to thank my parents, Frank and Irene, for

their continued support.

vii

Table of Contents

1: Introduction ............................................................................................................... 1

2: Literature Review ..................................................................................................... 2

2.1: What is bitcoin? ................................................................................................. 2

2.2: How is bitcoin like gold? ................................................................................... 8

3: Data and Descriptive Statistics ............................................................................... 13

3.1: Data .................................................................................................................. 13

3.2: Descriptive Statistics........................................................................................ 14

3.3: Basic Correlation Study ................................................................................... 16

4: Econometric Model................................................................................................. 18

4.1: Hypotheses and Definitions ............................................................................. 18

4.2: Model 1 ............................................................................................................ 18

4.3: Model 2 ............................................................................................................ 20

5: Empirical Analysis .................................................................................................. 23

5.1: Model 1 ............................................................................................................ 23

5.1.1: US Market ................................................................................................. 23

5.1.2: EU Market................................................................................................. 26

5.2: Model 2 ............................................................................................................ 28

5.2.1: US Market ................................................................................................. 28

5.2.2: EU Market................................................................................................. 30

6: Conclusion .............................................................................................................. 33

References ................................................................................................................... 36

Appendix ..................................................................................................................... 39

viii

List of Tables

Table 1: Summary Results for Model 1 of US market ................................... 24

Table 2: Summary Results for Model 1 of EU market ................................... 27

Table 3: Summary Results for Model 2 of US market ................................... 29

Table 4: Summary Results for Model 2 of EU market ................................... 31

Table 5: Avg daily volume traded by currency .............................................. 39

Table 6: Descriptive statistics for US market data ......................................... 47

Table 7: Descriptive statistics for EU market data and bitcoin ....................... 48

Table 8: Summary statistics for rolling 30-day correlations ........................... 50

Table 9: Result of GARCH, T-GARCH and IGARCH estimation for Model 1.1

US market (S&P 500) ................................................................................... 51


US market (Dow Jones) ............................................................................... 52


US market (Russell 2000)............................................................................. 53


EU market (Eurostoxx 50) ............................................................................ 54


EU market (Dax) ........................................................................................... 55


US market (S&P 500) ................................................................................... 56


US market (Dow Jones) ............................................................................... 57


US market (Russell 2000)............................................................................. 58


EU market (Eurostoxx 50) ............................................................................ 59


EU market (Dax) ........................................................................................... 60

ix

List of Figures

Figure 1: Volume of bitcoin traded by currency ............................................. 39

Figure 2: Expected total no. of bitcoin over time ........................................... 40

Figure 3: Breakdown of virtual currency scheme types ................................. 40

Figure 4: Illustration of bitcoin transactions ................................................... 41

Figure 5: Plot of bitcoin price (Linear scale) .................................................. 41

Figure 6: Plot of bitcoin price (Logarithmic scale) ......................................... 42

Figure 7: Plot of level of S&P 500 Index ....................................................... 42

Figure 8: Plot of level of Dow Jones index .................................................... 43

Figure 9: Plot of level of Russell 2000 index ................................................. 43

Figure 10: Plot of US government ten year bond yield .................................. 44

Figure 11: Plot of US 10 year yield (left axis) and S&P 500 (right axis) ......... 44

Figure 12: Plot of level of US Dollar index .................................................... 45

Figure 13: Plot of level of Eurostoxx 50 index ............................................... 45

Figure 14: Plot of level of German dax index ................................................ 46

Figure 15: Plot of German government ten year bond yield .......................... 46

Figure 16: Plot of level of euro currency index .............................................. 47

Figure 17: Rolling 30-day correlations with US equity market ....................... 48

Figure 18: Rolling 30-day correlations with EU equity market ....................... 49

Figure 19: Rolling 30-day correlations with bond yields ................................ 49

Figure 20: Rolling 30-day correlations with currency markets ....................... 50

1

1: Introduction

The objective of this dissertation is to conduct an in-depth economic and

financial study of the virtual currency Bitcoin. A comprehensive econometric

analysis of the currency will be undertaken so as to determine bitcoin’s

position within the universe of investable assets and to contribute to the

limited academic literature on the topic. The study of informational

dependencies across asset classes is of benefit to market participants in that

it can guide and inform their investment strategies. Bitcoin as it exists

currently is an exotic asset unlikely to feature in many investors portfolios.

However, already since its inception it has seen exponential growth along

with widespread media coverage. This dissertation is therefore a first attempt

at exploring the developing correlations that exist between bitcoin and other

more traditional investment vehicles. In particular, I address the anecdotal

evidence, often featured in the financial media, that bitcoin may behave like a

form of virtual gold. This comparison is often based on commentator’s

opinions that part of bitcoin’s value as an investment, like gold, is a result of

its ability to perform as a hedge or a safe haven against more conventional

securities. I formally address this issue empirically through the use of

econometric techniques.

The remainder of this dissertation is organised as follows: Chapter 2 will

provide an overview of the academic literature relevant to bitcoin attempting

to answer the questions: What is bitcoin? and how is bitcoin like gold?

Chapter 3 will discuss the data to be used in estimation and the summary

descriptive statistics. Chapter 4 will discuss the econometric methodology

used. Chapter 5 will outline and infer meaning from the results of estimation

and finally Chapter 6 will contain my concluding remarks.

2

2: Literature Review

In this chapter I will discuss most of the relevant scholarly literature that exists

on the topic of bitcoin. As previously stated as a motivation for research,

whilst there has been significant coverage of bitcoin in the financial media; the

quantity of academic discourse on the topic has been limited. As a result I

have been forced to widen the scope of what are acceptable sources for

review, with a good degree of material coming from within the online bitcoin

community. Due to the source of much of my material, care has been taken to

screen for any bias which may be present.

2.1: What is bitcoin?

Bitcoin is to date the most successful and widely used example of a virtual

currency. It is “…a digital, decentralized, partially anonymous currency not

backed by any government or other legal entity, and not redeemable for gold

or any other commodity” (Grinberg, 2011, p. 160). It was developed and

implemented by an anonymous programmer (or team of programmers) under

the pseudonym Satoshi Nakamoto in 2009 and has since achieved a certain

degree of international recognition as a result of widespread media coverage.

The bitcoin system operates at a global level and enables users to purchase

both real and virtual goods and services in exchange for units of the currency,

thereby competing with traditional currencies such as the US dollar and the

euro. Bitcoin is not pegged to any currency, with the exchange rate being set

by supply and demand in the market. All major currencies can be exchanged

for bitcoins on a number of online exchanges with Mt.Gox the most popular,

handling approximately 65% of all Bitcoin exchange trading1. As of writing,

the majority of all Bitcoin exchange transactions are with the US dollar2. An

interesting development in the distribution of bitcoin exchange transactions is

the digital currencies surge in popularity in China. In the first two quarters of

2013 the Chinese Yuan’s share has risen from approximately 1% of global

1 Source: www.bitcoincharts.com

2 See Figure 1 and Table 4 in appendix. Approximately 80% of all bitcoin exchange

transactions are with the US dollar at time of writing. (Source: www.bitcoincharts.com)

3

exchange volume to close to 6%. This is largely attributed to a short

documentary on bitcoin which aired on state-run broadcaster CCTV at the

end of March. Some bitcoin commentators have viewed this as a deliberate

move by the Chinese government to undermine the dollar’s reserve status, as

they have repeatedly called for a move away from the current dollar

dominated system3.

Since its inception in 2009 the value of one bitcoin has fluctuated dramatically

against the US dollar from an initial price of approximately $0.05 for one

bitcoin to a high of over $260. As of the time of writing over 11 million bitcoins

have been created and the current “market cap” of all bitcoins is close to one

billion US dollars. The price appreciation of bitcoin has been staggering given

that a “coin” has no intrinsic value, making bitcoin in some respects the most

trust based of all currencies. The use of the term “market cap”, taken from a

major bitcoin data publishing website, outlines one of the issues with bitcoin’s

status as a currency. Many users view the bitcoins they hold less as a

currency and more as a share on any and all developments in the whole

bitcoin space. This view of bitcoin as an investment is one of the primary

motivations for this research as a first attempt to estimate bitcoins relative

position within the universe of financial assets.

The “protocol” under which the bitcoin system operates was originally outlined

in a paper published online by Nakamoto (2009) in the aftermath of the global

financial crisis. Trust in the financial system is a recurring theme in the paper

with Nakamoto drawing attention to the “inherent weaknesses of the trust

based model” and stating that “What is needed is an electronic payment

system based on cryptographic proof instead of trust” (p. 1). Whilst Nakamoto

is explicitly referring to the online payments system, many bitcoin proponents

have latched on to the fact that the virtual currency is exogenous to the well-

established global financial system as one of its major strengths, allowing

users to insulate themselves against the interventionist policies of central

banks.

3 See Wall Street Journal article on comments of Governor of Bank of China Zhou Xiaochuan.

(http://online.wsj.com/article/SB123780272456212885.html)

4

The bitcoin system functions similarly to Bit Torrent, the popular peer-to-peer

file sharing software. According to the Oxford English Dictionary Online a

peer-to-peer network is:

“…[a network] in which each computer can act as a server for the others,

allowing shared access to files and peripherals without the need for a central

server.”

The key aspect of this definition as it relates to bitcoin is “without the need for

a central server”. Bitcoin is “designed as a decentralised system where no

central monetary authority is involved” (ECB, 2012, p. 24). This means that

the currency is not issued or managed by any one central organisation such

as the US Federal Reserve or the European Central Bank, nor is there a need

for the involvement of any other financial institutions in bitcoin transactions.

The technical aspects of the bitcoin payment system are difficult to

understand for most people not from a relevant technical background.

However, for my purposes a basic description of the process, drawing on that

featured in the ECB’s (2012) paper, will suffice4. In order to conduct bitcoin

transactions users must download and run the free and open-source software

(the bitcoin client). Users “coins” are then stored on their computers in a file

called a wallet, with all responsibilities regarding securing and backing up

these digital wallets falling to the individual user. Nakamoto (2009) defines an

electronic coin (bitcoin) “as a chain of digital signatures.” Each owner has two

“keys”, one public and one private, which are used to digitally “sign” any

transactions. Each computer on the network contains a ledger (called the

“block chain5”) of all previous transactions, which is one way the system

avoids the need for any trusted third party. Figure 4 in the appendix provides

a simplified illustration of how a bitcoin transaction works. First the future

owner must send his public key to the current owner . The original

4 For a full technical discussion of how the bitcoin system operates see Nakamoto, S. (2009)

‘Bitcoin: A Peer-to-Peer Electronic Cash System’, 5 “A block chain is a transaction database that is shared by all nodes participating in a system

based on the Bitcoin protocol. A full copy of a currency’s block chain contains every

transaction ever executed in the currency.” (https://en.bitcoin.it/wiki/Block_chain)

5

owner then transfers the bitcoins by “digitally signing a hash6 of the previous

transaction and the public key of the next owner” (Nakamoto, 2009, p. 2).

Each individual bitcoin contains a record of all previous transactions, with

each additional transaction becoming embedded as part of this code. All

transactions are then broadcast to the network, which verifies that a bitcoin

transfer has taken place by looking at the “timestamp7” which is contained in

the transaction hash, in a process called “mining”. These verified transactions

then become a matter of public record and are added to the block chain.

The ECB (2012) defines “mining” as:

“…the process of validating transactions by using computing power to find

valid blocks8 (i.e. to solve complicated mathematical problems)”.

“Miners” are incentivised to support the security and integrity of the network

by verifying transactions, as for each new block that is solved the successful

miner is rewarded with a set amount of newly created bitcoins. This process

is how bitcoins are initially distributed in the absence of any central authority.

Nakamoto (2009, p. 4) describes the mining process as “analogous to gold

miners expending resources to add gold to circulation”, with the resources in

question being computational and electrical power. The system is designed

so that as more miners join the network, the “problem difficulty adjusts to

ensure that bitcoins are created at a predetermined rate and not faster or

slower.” (Grinberg, 2011, p. 163) In addition, the algorithm which determines

the rate of creation of bitcoins is set to half four times over the life of the

currency as the total monetary base reaches certain predetermined levels910.

The total number of bitcoins in existence will approach, but never reach, 21

6 “A hash, or hash value, is the value returned by an algorithm that maps large data sets to

smaller data sets of fixed length.” (ECB, 2012, p. 23) “The same hash will always result from

the same data, but modifying the data by even one bit will completely change the hash.”

(https://en.bitcoin.it/wiki/Hash) 7 A timestamp is simply the time of day recorded in a digital transaction, so as to determine

transaction order in electronic payment systems. 8 “A block is a record of some or all of the most recent Bitcoin transactions that have not yet

been recorded in any prior blocks.” (https://en.bitcoin.it/wiki/Block_chain) 9 See Fig. 2 in Appendix.

10 The first of these levels was reached in November 2012 when the mining reward halved

from 50 bitcoins per successful block to 25 bitcoins.

6

million by approximately the year 2040, from which point on “miners are

expected to finance themselves through transaction fees.” (ECB, 2012, p. 25)

As a result of the ever-increasing difficulty of successful bitcoin mining

purpose built “mining rigs” have been developed with vastly superior

computational ability to the average PC. In addition, miners have also

organised themselves into profit sharing “pools” to hedge against the risk of

running the bitcoin client for long periods of time and receiving no reward due

to the stochastic element involved in the mining process.

The built-in scarcity in the bitcoin system, along with the frequent

comparisons with gold (mining, etc.) has led to bitcoin’s description as a

“synthetic commodity money” (Selgin, 2013) and is one of the most

interesting aspects of the currency from a monetary point of view. The fixed

money supply means that as long as bitcoins use is growing the currency will

be intrinsically deflationary, resulting in accusations that it may lead to a

deflationary spiral. This deflationary nature also contributes to users views of

bitcoin as more of an asset than a currency and has drawn large numbers of

speculators to bitcoin, resulting in two major boom and bust cycles.

Economists generally agree that constant deflation is a very destructive force

in modern economies, leading to hoarding as the value of money is expected

to increase over time. However, in the case of bitcoin as it “is not the currency

of a country or currency area and is therefore not directly linked to the goods

and services produced in a specific economy” the negative macro-economic

impact of any deflation are unlikely to pose a major issue (ECB, 2012, p.25).

As part of the transaction process users never have to disclose their identity

leading to a high degree of anonymity in the system with the result that bitcoin

is often described as “digital cash” (ECB, 2012, p. 25). Bitcoin has

experienced its fair share of controversy because of this anonymity with early

users latching on to its ability “to facilitate money laundering, tax evasion, and

trade in illegal drugs and child pornography.” (Grinberg, 2011, p. 161) These

illicit activities have of course brought the virtual currency to the attention of

regulatory bodies, such as the ECB and FinCEN (a bureau of the US

Department of Treasury).

7

The ECB paper, which I have used extensively as a resource so far, focuses

largely on the relevance of virtual currency schemes for central banks.

Although the paper refers generally to “virtual currency schemes”, “bitcoin”

appears over 100 times throughout the document and takes up the majority of

the discussion11. The authors identify a number of ways through which bitcoin

may impact central banks key responsibilities of maintaining price and

financial stability. They conclude that bitcoin does not pose a risk to price

stability due to its stable (fixed) money supply, and does not pose a risk to

financial stability due to its limited connection to the real world economy. With

regard to regulation they determine that bitcoin does fall within central banks

responsibilities and that the lack of supervision could pose an issue in the

future, highlighting difficulties arising from the legal uncertainty surrounding

the currency. So far US regulatory bodies have remained relatively quiet

regarding the legality of bitcoin, however, FinCEN “issued guidance clarifying

that bitcoin exchanges are considered Money Transmitters and are required

to obtain all proper licenses to conduct business in the US.” (TGB, 2013, p.

21)

The infrastructure surrounding bitcoin has developed rapidly in the years

since its inception and includes “exchanges, transaction service providers,

market information and chart providers, escrow providers, joint mining

operations and so on.” (Grinberg, 2011, p. 165) A surprising consequence of

this according to a study by Christin and Moore (2013) is that the majority of

“risk Bitcoin holders face stems from interacting with these intermediaries,

who act as de facto central authorities” (p. 9). They find that approximately

45% of bitcoin exchanges have closed often resulting in customers losing

money that they are unable to withdraw. This exchange risk has led to wild

periods of market volatility with both major bitcoin price crashes attributed to

problems with the most popular bitcoin exchange Mt. Gox, however, this

statement is based on anecdotal evidence.

11

The paper also features a second case study of another virtual currency, Linden Dollars,

which are an in-game currency featured in popular online role playing game Second Life.

8

The degree of uncertainty regarding the safety of funds transferred to online

exchanges is a serious barrier to the development of bitcoin as a currency or

an asset, and highlights the need for some form of regulatory framework. Any

regulation is likely to come hand in hand with a tax treatment, which will

remove one of bitcoins major strengths as an asset, its tax free nature. In May

2013 the US Government Accountability Office (GAO) published a report to

the US Senate Committee on Finance in relation to the tax compliance of

virtual currencies. They recommended that the IRS provide additional

guidance regarding online currencies and highlighted uncertainties with how

to characterise income and how to calculate the basis for gains as possible

issues. They find that the uncertainty in relation to how to characterise income

“depends on whether the virtual economy activity or virtual currency unit is to

be treated as property, barter, foreign currency, or a financial instrument”

(GAO, 2013, p. 13). Any form of regulatory or tax treatment represents a

double edged sword for bitcoin proponents who are largely attracted by the

currencies independence from the global financial system, but who also hope

for a more widespread adoption which will only happen should the fears of

more conventional investors be allayed.

2.2: How is bitcoin like gold?

The majority of my discussion so far has been of a descriptive nature

regarding how bitcoin works and the major issues it may face in future.

However, my main research focus is on determining bitcoins relative

relationships with conventional asset classes such as stocks, bonds and

currencies. From my survey of the available academic literature and

publications in the financial media I have come across various comparisons

between bitcoin and gold. The ECB lays the economic foundations of bitcoin

with the Austrian school of economics, well known proponents of a return to

the gold standard, and it can be argued that bitcoin represents an ultimate

form of denationalised money given that it is not controlled by any

government or other central authority. They further develop this relationship

by stating that bitcoin is “inspired by the former gold standard” (2012, p. 22).

Whilst I have briefly discussed the unique monetary aspects of the currency a

9

full discussion of the economic philosophy underlying bitcoin is outside the

scope of this paper. In this section, I intend to develop the comparison

between bitcoin and gold not as a form of money but as an asset class.

Exponents of bitcoin’s virtues argue that as it is an alternative currency and

payment system that does not depend on a network of financial institutions,

but rather is “based on cryptographic proof instead of trust” (Nakamoto, 2009,

p. 1). As a result users are insulated against the risks posed by sovereign

currencies and debt such as inflation and default risk. This is particularly

relevant when we consider bitcoin was created in the aftermath of the global

financial crisis in 2008 when public trust in governments and financial

institutions was at a low. This also gives weight to the comparison between

bitcoin and gold, as gold is in theory used to protect against similar risks.

Capie, Mills and Wood (2005) argue that gold has remained attractive as an

asset as:

“It is durable, divisible, and, for many years over a large part of the world, was

indeed the ultimate standard of value.”

Like gold bitcoin is durable, it cannot be destroyed easily, although it can be

lost or stolen but the same can be said for physical gold. Bitcoins are also

“divisible to eight decimal places enabling their use in any kind of transaction,

regardless of the value” (ECB, 2012, p. 21). Unlike gold however, bitcoin

obviously lacks the historical significance resulting from hundreds of years of

use as a store of value. Interestingly bitcoin has gained significant popularity

among libertarians and others from similar ideological backgrounds that have

traditionally been seen as major “gold bugs”. The philosophical links between

bitcoin and the Austrian school are likely to play a major role here and no

doubt fears about the effects of the unconventional monetary policies being

pursued by central banks since the financial crisis have added fuel to the fire.

One major advantage of investing in bitcoin rather than gold are the much

lower costs involved. To gain exposure to gold investors main options are

physical gold, the futures market and gold-linked funds. As of the time of

10

writing to gain exposure to bitcoin investors can only purchase bitcoins. One

of the main issues with buying physical gold is the cost of storage, for bitcoin

these costs are basically zero. Gold futures contracts involve physical delivery

so to gain exposure investors who only wish to speculate on the market must

enter into and close out positions to avoid taking delivery and having to pay

storage costs. For longer term investors in the futures market, they must roll

out of the expiring month contract and into the next active contract which

compounds the fees they must pay to the exchange. As bitcoin is basically

free to hold this is not an issue for investors and in addition bitcoin exchange

fees are much lower in comparison to those charge by conventional financial

exchanges. Gold-linked funds, such as ETFs, have seen a major surge in

popularity in recent years; however, these also charge transaction and

storage fees. As a result of its low cost nature, if bitcoin could serve a similar

purpose in the markets, i.e. as a hedge and safe-haven, it could offer a much

cheaper alternative to gold, benefitting investors.

Intuitively one would expect that the relationship between bitcoin and

currencies be the strongest, as bitcoin is in essence an alternative currency.

The academic literature on gold’s role as a monetary asset is well developed

and it is consistently found that gold is an effective hedge against inflation12,

and against US dollar devaluations13. One possible explanation given for this

role as provided by Capie, Mills et al. (2005) is that:

“…gold cannot be produced by the authorities that produce currencies. This

means that those who can increase the supply of money and therefore, from

time to time, debase its value cannot by similar means debase the value of

gold.” (p. 351)

The same can be argued for bitcoin as a result of its fixed money supply and

decentralised nature which could make it a natural hedge for currencies.

12

See Fortune (1987), Moore (1990), Taylor (1998), Ghosh, Levin et al. (2004), Worthington

and Pahlavani (2007), Blose (2010), and Wang, Lee et al. (2010). 13

See Johnson and Soenen (1997), Capie, Mills et al. (2005), Tully and Lucey (2007),

Sjaastad (2008), Hammoudeh, Sari et al. (2009), and Ciner, Gurdgiev et al. (2010, revised

2012).

11

Gold’s role as a hedge14 for stock and bond markets has also been tested

extensively. Baur and Lucey (2010), Baur and McDermott (2010), and Ciner,

Gurdgiev et al. (2010, revised 2012) find a clear negative relationship

between gold and stock returns on average indicating that gold is a hedge for

equity markets. However, they find that “there is little relation between gold

and bond prices, in general” (Ciner, Gurdgiev, et al., 2010, revised 2012, p.

9). A potential explanation they provide is that “gold is a market sentiment

proxy that is more likely to impact riskier assets than fixed income securities”

(Ciner, Gurdgiev et al. (2010, revised 2012, p. 9).

Another well documented role that gold plays in financial markets is as a safe

haven in times of extreme negative returns. Baur and Lucey (2010), and Baur

and McDermott (2010) find that gold “can and does act as a safe haven for

bonds as well as stocks” (Lucey, 2011, p. 12). Ciner, Gurdgiev et al. (2010,

revised 2012) also find the same relationship to hold for gold, bonds, and the

dollar. However, they found that “gold in fact does not act as a safe haven for

equities” (p. 11). They present a possible reason for this as being that the rise

in popularity of gold based ETFs and other gold-linked indices “has caused a

decline in its primary attraction for many financial market participants, which is

the notion that gold can be trusted as a safe haven against [the] equity market

volatility” (p. 11).

Although the degree of informational dependency between bitcoin and

traditional capital markets needs to be tested statistically, intuitively a low

correlation is likely. This may be due to the fact that as of yet there are

unlikely to be many institutional investors in the bitcoin market, due to the low

liquidity and lack of regulation, which could make the virtual currency a

natural hedge against equity and bond markets. Whilst bitcoin’s monetary

structure could make it a hedge against currency depreciations and inflation,

its exogeneity to the conventional financial system could allow it to play a

hedge role against sovereign debt markets as it does not rely on the

reputation or supervision of any national or supranational body, and should be

14

See Baur and Lucey (2010), Baur and McDermott (2010) and Ciner, Gurdgiev et al. (2010,

revised 2012).

12

insulated to some degree from the contagion which allowed the spread of the

2008 crisis from the subprime market in the US to the rest of the world.

13

3: Data and Descriptive Statistics

3.1: Data

The data to be examined consists of daily observations and covers the period

between 19 July 2010 and 21 June 2013. As bitcoin was only established in

2010 this is the earliest date from which reliable data is available. The end

date was chosen to both maximise the size of the data set whilst allowing

sufficient time to conduct econometric analyses. I include a broad range of

variables to represent stock, bond, and currency markets. The data are drawn

from both US and European markets.

The Bitcoin data consists of the bitcoin-US dollar exchange rate and is quoted

in dollars per bitcoin. The raw daily data is in seven-day week format;

however, as the data for the other assets are in five-day week format the

weekend data was dropped. All bitcoin data was obtained from

www.bitcoincharts.com, a leading source of Bitcoin exchange rate and

volume data.

To represent the US equity market I use the Standard & Poor’s 500 Index

(S&P 500), the Dow Jones Industrial Average Index (DJIA), and the Russell

2000 Index. The S&P 500 was chosen as it is widely regarded as one of the

best gauges of blue chip companies in the US. The Dow Jones Industrial

Average is one of the longest running stock indices in the world. It represents

30 of the largest US companies drawn from a diverse range of industries. It is

often quoted as a leading index due to the fact that its component companies

are some of the most actively traded in the US equity market. The inclusion of

the Russell 2000 allows estimation of the relationship between bitcoin and the

broader US stock market. Whereas the S&P 500 and the Dow Jones only

feature large-cap companies, the Russell 2000 also features small- and mid-

cap companies.

To represent the European equity market I use the Eurostoxx 50 Index and

the Deutsche Aktien Index (DAX). The Eurostoxx 50 is an index containing 50

http://www.bitcoincharts.com/

14

of the largest blue chip European companies. It covers equities from 12

different Eurozone countries and as such is a good indicator of the overall

European equity market. The German stock index (the Dax) is an index

featuring 30 of the largest companies traded on the Frankfurt stock exchange.

Germany is the largest economy in the Eurozone and so its main stock index

is again a good indicator of the general health of European markets. All equity

index data was obtained from Bloomberg.

To represent the US bond market I use Bloomberg’s US generic government

ten year yield, which provides a rolling, constant maturity series of the yield

on the benchmark US government bond. For the European bond market I use

Bloomberg’s German generic government ten year yield, which again

provides a rolling, constant maturity series of the yield on the benchmark core

Eurozone bond.

To represent currencies I use the Federal Reserve’s trade weighted US dollar

index which is an index of the US dollars relative strength against a basket of

foreign currencies. For the euro I use Bloomberg’s Correlation Weighted

Index which is an index of the euro’s relative strength against a representative

basket of world currencies.

The series were grouped by geographic location with the US equity, bond and

currency data to be used together for group estimation, and the EU equity,

bond and currency data also to be used together. As bitcoin is currently tax

free in most jurisdictions none of the series to be used for estimation are tax

adjusted.

3.2: Descriptive Statistics

Figures 5 to 16 in the Appendix present plots of the price (level) of each asset

over time. Figure 5 is a plot of the price of bitcoin in US dollars over time on a

linear scale. The price of bitcoin was less than $1 until the beginning of 2011.

The first speculative bubble and crash in the virtual currency occurred in June

2011 when the price peaked at around $30. The price then crashed due to an

attack on one of the main exchanges which resulted in the theft of a large

15

quantity of bitcoins. The price of bitcoin began to grow at a parabolic rate at

the beginning of 2013. It eventually reached an all-time high of $266 per

bitcoin before plummeting sharply. It has since levelled off and is trading in a

range of approximately $80 to $100 at the time of writing. Figure 6 presents a

plot of the price of bitcoin against a logarithmic scale. It displays a definite

upward trend with the price seeming to revert to the mean following each of

the bubbles and crashes. It makes more sense to view Bitcoin prices on a log

scale since the price has grown exponentially due to constant adoption by

new users.

US stock prices as measured by the S&P 500, the Dow Jones and the

Russell 2000 have increased over the period of the sample and are all close

to their all-time highs. Equity prices in the US experienced a sharp downward

correction in Q3 2011 due to fears of a Eurozone breakup as a result of talk

that Greece may be the first country forced to leave the monetary union.

Yields on benchmark US treasury notes have fallen over the period of the

sample, due largely to the US Federal Reserve’s policy of ultra-low interest

rates, reaching their all-time low in Q2/3 2012. Figure 11 in the appendix

shows a plot of ten year yield and the S&P 500. In it we can clearly see that

the sharp drop in yields in Q3 2011 corresponds with a fall in the aggregate

level of stock prices (as measured by the S&P) for the same period. This is

representative of “risk-off” behaviour in the markets as investors leave riskier

investments and gravitate towards the perceived lower risk of US

Government securities, bidding up prices and pushing down yields. Figure 12

shows that the US dollar depreciated against a representative basket of

foreign currencies up until Q3 2011, following which it appreciated, again

likely due to the policies of the Fed.

European equity markets as measured by the Eurostoxx 50 have fallen over

the period of the sample and are still well below pre-crisis (2007) highs. The

German Dax index has increased over the period of the sample and is

currently close to its all-time highs similar to the US indices. The spread

between the German and aggregate European equity markets reflects the

economic imbalance between Germany, a core Eurozone country, and the

16

more peripheral nations. Both the Eurostoxx and the Dax experienced a

sharp downward correction in Q3 2011, due to the escalating Eurozone crisis,

with the Eurostoxx correcting most severely. Yields on the ten year German

Bund have fallen over the period of the sample. A sharp move down in Q3

2011 has been followed by a continuing downward trend, with yields reaching

their all-time lows in Q3 2012. The Euro weakened against a representative

basket of foreign currencies up until Q3 2012, following which it appreciated.

Much of this down trend can likely be attributed to the strengthening US dollar

(from Q3 2011) along with the escalating Eurozone crisis.

Tables 6 and 7 in the appendix present a summary of descriptive statistics for

the log returns of each series for both the US and EU markets. If we take the

standard deviation of returns to measure an asset’s riskiness, then Bitcoin is

by far the riskiest over the sample period. This is followed by equities, with the

Russell 2000 index the riskiest and the Dow Jones the least risky. For this

sample EU equity markets are also riskier on average than US markets. The

US dollar is more volatile than the Euro over the sample period. Finally bonds

are the least risky investment as the returns examined represent a change in

yield and not overall returns. A student’s t-test on each of the sample means

indicates that the mean returns of each of the series representing stocks,

bonds and currencies are not statistically different from zero. However, the

mean return on bitcoin is positive and statistically different from zero at the

0.02% level indicating a positive trend in the data.

3.3: Basic Correlation Study

Figures 17 to 20 in the appendix present plots of rolling thirty-day correlations

between the log returns on each asset class and bitcoin. Figure 17 presents

the correlations between bitcoin returns and returns on US equity market

indices. There does not appear to be any clear trend in the data and the

correlation fluctuates from negative to positive over time. The correlation

remains generally low and positive between August 2011 and August 2012,

and appears to be on average more positive than negative. In general the

correlation appears to be negatively skewed, as more extreme negative

17

values are observed than positive. The correlations between bitcoin returns

and returns on European equity markets are presented in figure 18. Again

there does not appear to be any clear trend in the data with the series

fluctuating between negative and positive values. The plot appears to be

positively skewed with more extreme positive values observed than negative

ones.

Figure 19 presents a plot of rolling 30-day correlations between bitcoin

returns and changes in bond yields for the US and Germany. The series

again oscillate between negative and positive values with more positive

values observed, which implies a negative correlation with bond prices,

however, this will need to be tested empirically. Finally figure 20 presents the

rolling 30-day correlations between bitcoin and returns on the US dollar and

euro currency indices. The clear negative correlation between the dollar and

euro is apparent as both series are generally moving in opposite directions.

The relationship between currencies and bitcoin appears to be the most

volatile which intuitively makes sense as bitcoin is in essence an alternative

currency. Also it is interesting to note that while the correlations with both

series fluctuate between negative and positive, the relationship with the euro

seems to be on average more positive, whilst the relationship with the dollar

appears to be more negative.

These plots of rolling correlations are useful for gaining an insight into what, if

any, informational dependencies exist between bitcoin and other asset

classes. It must be noted that the correlations fluctuate a lot over time,

implying weak relationships in general. A possible explanation for this is that

bitcoin is still in its development stage; if more institutional investors enter the

market then its relationships with other asset classes will become more

defined.

18

4: Econometric Model

4.1: Hypotheses and Definitions

This section outlines the econometric models I will use to test whether bitcoin

is a hedge, a diversifier, or a safe haven drawing on the econometric models

featured in papers by Capie, Mills and Wood (2005), Baur and Lucey (2010)

and Ciner, Gurdgiev and Lucey (2010, revised 2012). Through these tests I

hope to address the question: as an asset, does bitcoin behave like gold? My

null and alternate hypotheses are therefore:

(4.1.1)

(4.1.2)

Here the condition “behave like gold” explicitly refers to whether bitcoin works

as a safe haven or a hedge for other assets.

Baur and Lucey (2010) define a hedge as:

“…an asset that is uncorrelated or negatively correlated with another asset or

portfolio on average.” (p. 219)

They go on to define a safe haven as:

“…an asset that is uncorrelated or negatively correlated with another asset or

portfolio in times of market stress or turmoil.” (p. 219)

Finally, it may be the case that bitcoin is a diversifier which they define as:

“…an asset that is positively (but not perfectly) correlated with another asset

or portfolio on average.” (p. 219)

4.2: Model 1

To address these questions I will run two separate models. The first model, to

test whether bitcoin is a hedge or a diversifier against stocks, bonds and

currencies, has a mean equation of the form:

19

(4.2.1)

Where the returns on each asset are calculated by taking the first difference

of the logarithm to ensure each series were stationary 15 and ,

, , and are the returns on bitcoin, stocks, bond yields and

currencies respectively. For this model the hypotheses are therefore:

(4.2.2)

(4.2.3)

An examination of the residuals of an ordinary least squares (OLS) estimation

of the mean equation for each group of series suggested the use of a

GARCH16 model, which is consistent with the work done by previous papers

(Capie et al. 2005 etc.). Financial time series often “exhibit periods of

unusually high volatility followed by more tranquil periods of low volatility”

(Asteriou and Hall, 2006, p. 249); this is known as volatility clustering. When

this is the case the assumption of the OLS methodology, that the variance of

the error term is constant and time-invariant (is homoskedastic) is violated

and we say that the variance is heteroskedastic (Hill, Griffiths and Lim, 2012,

p. 299). When time series exhibit heteroskedasticity it is often best to

dynamically model the variance using the ARCH17 class of models. GARCH

models are generalised ARCH models which allow for the capture of “long

lags in the shocks with only a few parameters” (Hill et al., 2012, p. 526),

allowing for more parsimonious estimation. I estimated a number of different

15

To formally test for stationarity Augmented Dickey Fuller unit root tests were applied to

each series. Cointegration was also tested for as non-stationary series can be related to one

another if they are cointegrated. The logarithm of each series was found to be not cointegrated

with any of the other series. 16

Further testing was performed on the residuals to confirm the presence of autoregressive

heteroskedasticity such as an ARCH-LM test and an examination of a correlogram of the

squared residuals. 17

Auto-Regressive Conditional Heteroskedasticity

20

forms of GARCH model including GARCH (1, 1) 18 , T-GARCH 19 and

IGARCH20. The chosen model was a GARCH (1, 1) with a generalised error

distribution (GED) of the form:

(4.2.4)

The GARCH model and lag length were selected using the Akaike and

Schwartz (Bayesian) information criterion, and the log likelihood function. The

error distribution was chosen from an examination of the residuals21.

4.3: Model 2

The second model, to test whether bitcoin is a safe haven, has mean

equation of the form:

(4.3.1)

This model was chosen using the same criteria as for model 1 and is again a

GARCH (1, 1) model, with variance equation of the same form as equation

(4.2.4) above. The terms , , and are quantiles

used to account for extreme shocks, and represent the bottom 10 and 5

percent of daily returns for stocks and currencies, and the top 10 and 5

percent of changes in bond yields. When estimated simultaneously the

parameters , and can be thought of as vectors. For stocks and

currencies if the return is larger than the q% quantile, the value is zero. For

18

GARCH (1, 1) is the simplest form of GARCH model which allows the variance of the

residual of the estimated mean equation to depend on the “past values of the shocks, which

are captured by the lagged squared residual terms, and on past values of itself” (Asteriou and

Hall, 2006, p. 260). 19

T-GARCH is a threshold GARCH model where “positive and negative news are treated

asymmetrically.” (Hill, Griffiths, and Lim, 2012, p. 527) It adds in a multiplicative dummy

variable into the variance equation to determine whether there is a statistically significant

difference when shocks are negative, as is often the case in financial time series (Asteriou et

al., 2006, p. 267). 20

IGARCH is an integrated GARCH model used when the variance of the residuals is very

persistent. This again is very common for financial returns data. 21

This included looking at a correlogram of the Q-statistics (test for serial correlation), a

Histogram-Normality test and an ARCH-LM heteroskedasticity test.

21

bonds, if the change in yield is smaller than the q% quantile, the value is zero.

Following work done by Baur and Lucey, these quantiles can be used to

analyse the role of bitcoin in times of extreme market stress. Baur and

Lucey’s paper used quantiles of 5%, 2.5% and 1%, however, as there is just

over 3 full years of data available for bitcoin, I chose 10% and 5% in order to

ensure adequate data for estimation. The theory behind the use of quantiles

to examine the safe haven hypothesis is:

“If stocks or bonds exhibit extreme negative returns, investors buy gold

[bitcoin] and bid up the price of gold [bitcoin]. If the price of gold [bitcoin] is not

affected, investors neither purchase nor sell gold [bitcoin] in such adverse

market conditions.” (Baur and Lucey, 2010, p. 220)

The hypotheses tested using model 2 are:

(4.3.2)

(4.3.3)

As far as I am aware, there are no academic papers discussing the

fundamental relationship between bitcoin and other financial assets.

Determining whether there is a relationship and the nature of that relationship

is therefore an important first step in understanding the role bitcoin plays in

the universe of investable assets, although admittedly the role is likely to be a

small one. In particular an econometric evaluation of whether bitcoin has

played a safe haven role, as portrayed in the financial media during the recent

banking crisis in Cyprus, will be examinable through the use of Eurozone data

series.

The following section will discuss the results obtained from estimating the

models discussed above. For Equation 4.2.1 if the estimated of a variable is

zero or negative, and statistically significant, then it implies that bitcoin is a

22

hedge for that asset on average. So if is zero or negative then bitcoin is a

hedge for stocks. For Equation 4.3.1 if the values of the coefficient estimate

for one of the quantiles of an asset class (i.e. for stocks and for

currencies) is non-positive and statistically significant then bitcoin serves as a

safe haven for those assets in times of market turmoil. For bonds, if the value

of is positive and statistically significant then bitcoin is a safe haven. The

reason the inverse of stocks and currencies is true for bonds is due to the use

of yields rather than prices. If we assume that bond yields and prices are

perfectly negatively correlated, then the top 5 or 10% of extreme positive

changes in bond yields represent a spike in yields (sharp drop in prices) and

generally occur when the market risk premium increases.

23

5: Empirical Analysis

This section outlines the results obtained from estimating the models

described in Chapter 4. The results are broken down into those obtained from

each model by market.

5.1: Model 1

5.1.1: US Market

The summary results from estimating model 1 for the US market are

presented in table 1 below. The coefficient estimates for the average effects

of stock returns on bitcoin returns are not statistically significant. However,

they are all positive on average implying that whilst for the sample tested the

relationship between bitcoin and the US equity market is weak the direction of

this relationship may be positive. The coefficient estimates for the average

effect of a change in bond yields on bitcoin returns are significant at the 10%

level only in model 1.1 with the S&P 500 as the equity market proxy. They are

however, consistently low and positive implying a negative relationship

between bitcoin returns and bond prices. This means that bitcoin could serve

as a hedge for the US bond market. The estimates for the average effect of

returns on the US dollar are only significant (at the 5% level) in model 1.2 with

the Dow Jones as the equity market variable. However, the estimates are all

consistently positive and the effect is much stronger than for equities or bonds

which supports the idea that bitcoin is a monetary asset.

The estimated ARCH (1) and GARCH (1) terms in the variance equations are

all positive and highly statistically significant. In addition they are all very

similar in size indicating that the model is relatively robust and is an

appropriate selection for modelling the conditional variance of the system.

24

TABLE 1: SUMMARY RESULTS FOR MODEL 1 OF US MARKET

US Model 1.1 (S&P 500) US Model 1.2 (DJIA) US Model 1.3 (Russell 2000)

Bitcoin Coeff. Est. Std err. P-value Coeff. Est. Std. err. P-value Coeff. Est. Std. err. P-value

0.0022 0.0007 0.0009*** 0.0021 0.0006 0.0004*** 0.0026 0.0007 0.0003*** 0.0771 0.0823 0.3489 0.1228 0.0861 0.1538 0.0749 0.0733 0.3067 0.0481 0.0286 0.0924* 0.0367 0.0276 0.1828 0.0228 0.0319 0.4762 0.2979 0.2133 0.1624 0.3768 0.1964 0.0550* 0.2961 0.2079 0.1544

Var. eq.

0.0001 0.0000 0.0395** 0.0001 0.0000 0.0483** 0.0001 0.0000 0.0448** 0.3232 0.0724 0.0000*** 0.3164 0.0710 0.0000*** 0.3158 0.0692 0.0000*** 0.7559 0.0339 0.0000*** 0.7630 0.0331 0.0000*** 0.7599 0.0328 0.0000***

*, **, *** indicate significance at 10, 5, and 1% respectively.

Table 1 presents a summary of the GARCH (1, 1) estimation results for model 1 of the US market with the S&P 500, the Dow Jones Industrial

Average and the Russell 2000 indices as the equity market variables. No lags were found to be significant. The models were selected using

the Akaike and Schwartz (Bayesian) information criterion, and the log likelihood function. For full results, along with the results of T-GARCH

and IGARCH models see Appendix (Tables 9 – 11). The results indicate that bitcoin is not a hedge for stocks as the estimates are all

positive; however, they are not statistically significant. They also indicate that bitcoin is a hedge for T-Notes; however, only the coefficient

estimate from the S&P model is significant at the 10% level. The results also indicate that bitcoin is not a hedge for the dollar; however, only

the estimated coefficient from the DJIA model is significant at the 10% level. It is important to note that the size of the coefficient estimate for

the effect of the dollar on bitcoin is much larger than those representing equities or bonds, supporting the idea that bitcoin may be a monetary

asset.

25

Overall the results from estimating model 1 for the US market are

inconclusive given the varying significance of the estimated coefficients. I fail

to reject the null hypothesis that bitcoin is not negatively correlated with

equities on average. For model 1.2 the coefficient estimate for the relationship

between bitcoin and the dollar is positive and significant at the 10% level, in

which case I accept the null that bitcoin is not negatively correlated with the

US dollar on average. In the case of bonds, for model 1.1 I reject the null and

accept the alternate hypothesis at the 10% level as bitcoin is positively

correlated with changes in bond yields (negatively correlated with prices).

One positive result from estimation is that the direction and strength of the

relationships between bitcoin returns and the returns on other assets are

relatively consistent. The fact that for the most part the estimated coefficients

are not significant implies that bitcoin returns may be orthogonal to equities,

bonds and the US dollar. This may mean that while bitcoin is not strictly a

hedge, using the definition supplied by Baur and Lucey (2010), it may be a

natural hedge due to the low and insignificant correlations with other asset

classes. In addition the low correlations imply that bitcoin is a diversifier.

26

5.1.2: EU Market

The summary results from estimating model 1 for the EU market are

presented in table 2. The coefficient estimates for the average effect of bitcoin

returns on European equity market returns are never significant implying that

no clear relationship exists. In addition, the relationships represented by the

coefficient estimates from models 1.1 and 1.2 are in opposing directions

making interpretation more difficult. The coefficient estimates for the average

effect of a change in bond yields on bitcoin returns are positive and

statistically significant at the 5% level in both models, and at the 1% level in

model 1.1. These estimates are 0.1645 and 0.0692 for models 1.1 and 1.2

respectively. This implies that bitcoin is a hedge for Eurozone bond markets

on average, as represented by German ten year bond yields. The coefficient

estimates for the average effect of returns on the euro index on bitcoin returns

are not statistically significant. In addition the signs vary across both models

again causing interpretation difficulties. The ARCH and GARCH coefficients

from estimating the variance equation are all positive and statistically

significant at the 1% level.

Overall the results from estimating model 1 for the EU market are

inconclusive regarding equity and currency markets as a result of the

insignificant and contradictory coefficient estimates. I therefore fail to reject

the null hypothesis that bitcoin returns are not negatively correlated with EU

equity markets and the euro on average. However, in the case of European

bond markets I can reject the null, and accept the alternate hypothesis as

bitcoin returns are positively correlated with changes in German bond yields

(negatively correlated with prices) meaning that bitcoin appears to be a hedge

for European bond markets. It is important to note that the strength of this

relationship varies a good deal across both models meaning that whilst the

direction of the relationship may be statistically significant, the degree to

which changes in bond yields affect changes in bitcoin returns is uncertain.

27

TABLE 2: SUMMARY RESULTS FOR MODEL 1 OF EU MARKET

EU Model 1.1 (Eurostoxx) EU Model 1.2 (Dax)

Bitcoin Coeff. Est. Std err. P-value Coeff. Est. Std. err. P-value

0.0035 0.0007 0.0000*** 0.0033 0.0007 0.0000*** -0.0660 0.0414 0.1109 0.0539 0.0792 0.4962 0.1645 0.0292 0.0000*** 0.0693 0.0277 0.0123** -0.2184 0.2318 0.3461 0.2027 0.2434 0.4050

Var. eq.

0.0001 0.0000 0.0352** 0.0001 0.0000 0.0326** 0.3034 0.0701 0.0000*** 0.3136 0.0706 0.0000*** 0.7651 0.0341 0.0000*** 0.7583 0.0342 0.0000***

*, **, *** indicate significance at the 10, 5 and 1% levels respectively.

Table 2 presents a summary of the GARCH (1, 1) estimation results for model 1 of the EU market with the Eurostoxx 50 and the Dax indices

as the equity market variables. No lags were found to be significant. The models were selected using the Akaike and Schwartz (Bayesian)

information criterion, and the log likelihood function. For full results, along with the results of T-GARCH and IGARCH models see appendix

(Tables 12 – 13). The results indicate that bitcoin is not a hedge for European stocks or the euro as the coefficient estimates are not

significant or consistent across both models. They also indicate that bitcoin is a hedge for German ten year bonds as the estimates are

positive (negatively correlated with prices) and significant at the 1% and 5% levels for models 1.1 (Eurostoxx) and 1.2 (Dax) respectively.

28

5.2: Model 2

5.2.1: US Market

The summary results from estimating model 2, to test whether bitcoin serves

as a safe haven for the US market are presented in table 3. The reactions in

the bitcoin market following periods of extreme negative stock returns are

positive22 and not significant23, indicating that bitcoin is not a safe haven for

US equities. The estimated coefficients for the effect of extreme positive

spikes in US bond yields (falls in prices) on the bitcoin market are negative for

the 90% quantile and positive for the 95% quantile. This implies that bitcoin

can function as a safe haven for the most extreme spikes in bond yields;

however, none of the coefficients are statistically significant. The coefficient

estimates for the average effect of extreme dollar devaluations are negative

on average24. This implies that bitcoin may function as a safe haven in times

of extreme returns against the dollar; however, none of these estimates are

statistically significant.

The estimated coefficients for the average effect of returns on equities, bond

yields and the dollar are consistent with those found in model 1 indicating that

the direction of the relationships may be correct, although they are for the

most part insignificant. It is worth noting that for models 2.1 and 2.2 the

coefficients representing the relationship between bitcoin and the dollar are

positive and statistically significant at the 10 and 5% levels respectively. The

effect of a change in the relative strength of the dollar on bitcoin are again

stronger than the effects of changes in equity or fixed income markets, which

supports the notion that bitcoin is a monetary asset.

22

For the model using the Russell 2000 index as the equity market proxy the variable

representing the 5% quantile is negative, however, the sign of this result is not consistent with

the other coefficients estimated. 23

For the model using the Dow Jones index as the equity market proxy the variable

representing the 5% quantile is positive and significant at the 5% level, however, the

significance of this result is not consistent with the other coefficients estimated. 24

Apart from the coefficient estimate of the 5% quantile in the Dow Jones model which is

positive.

29

TABLE 3: SUMMARY RESULTS OF MODEL 2 FOR US MARKET

US Model 2.1 (S&P 500) US Model 2.2 (DJIA) US Model 2.3 (Russell 2000)

Bitcoin Coeff. Est. Std err. P-value Coeff. Est. Std. err. P-value Coeff. Est. Std. err. P-value

0.0021 0.0008 0.0088*** 0.0039 0.0008 0.0000*** 0.0025 0.0008 0.0028***

0.1340 0.0998 0.1792 0.0191 0.1184 0.8720 0.0168 0.0816 0.8372

(10%) 0.0542 0.1756 0.7577 0.1091 0.1504 0.4681 0.1385 0.1929 0.4728

(5%) 0.1360 0.1761 0.4401 0.3225 0.1571 0.0400** -0.0056 0.2167 0.9792

0.0251 0.0369 0.4962 0.0240 0.0325 0.4602 0.0519 0.0306 0.0901*

(90%) -0.1086 0.0963 0.2598 -0.1172 0.0899 0.1922 -0.0679 0.1024 0.5069

(95%) 0.0406 0.1010 0.6874 0.0653 0.0878 0.4570 0.0321 0.1115 0.7732

0.4797 0.2634 0.0686* 0.5250 0.2381 0.0274** 0.3778 0.2721 0.1651

(10%) -0.0223 0.3909 0.9544 -0.4366 0.5429 0.4213 -0.2601 0.5696 0.6479

(5%) -0.2226 0.4620 0.6299 0.2686 0.5383 0.6178 -0.2415 0.6428 0.7072

Var. eq.

0.0001 0.0000 0.0553* 0.0001 0.0000 0.0454** 0.0001 0.0000 0.0520*

0.3223 0.0726 0.0000*** 0.3125 0.0694 0.0000*** 0.3204 0.0713 0.0000***

0.7625 0.0330 0.0000*** 0.7653 0.0326 0.0000*** 0.7626 0.0330 0.0000***


Table 3 presents a summary of the GARCH (1, 1) estimation results for model 2 of the US market with the S&P 500, the Dow Jones Industrial

Average and the Russell 2000 indices as the equity market variables. For full results, along with the results of T-GARCH and IGARCH

models see appendix (Tables 14 – 16). The results suggest that bitcoin has a strong positive correlation with the US dollar (statistically

significant at the 10 and 5% level in models 2.1 and 2.2 respectively). Bitcoin is not strictly a safe haven for any of the markets tested but is

negatively correlated with extreme dollar devaluations indicating some safe haven properties. Bitcoin is also positively correlated with

extreme positive yield changes indicating that it may function as a safe haven for the most extreme periods of increased risk in the bond

market. Bitcoin is not a hedge or a safe haven for equities as it is positively correlated on average and during periods of market turbulence.

30

Overall the results from estimating model 2 for the US market are

inconclusive given the lack of statistical significance of the estimated

coefficients. This supports the idea that bitcoin returns are statistically

independent of returns in equity, bond and currency markets in the US. For

the 5% quantile in model 2.2 I accept the null hypothesis that bitcoin returns

are not negatively correlated with extreme negative stock market returns at

the 5% level indicating that bitcoin is not a safe haven for US equity markets.

For two out of three models estimated, with the S&P and Dow Jones as

equity market proxies, I accept the null hypothesis that bitcoin is not

negatively correlated with the US dollar on average at the 10 and 5% levels

respectively. This implies that bitcoin is not a hedge for the US dollar.

5.2.2: EU Market

The summary results for estimating model 2 for the EU market are presented

in table 4. The reactions in the bitcoin market following extreme negative

reactions in European equity markets are all average positive. For model 2.2,

I accept the null hypothesis that bitcoin is not a safe haven for EU equity

markets as the coefficient estimate for the 5% quantile is positive and

statistically significant at the 5% level. This indicates that bitcoin is not a safe

haven during periods of stock market turbulence.

The estimated coefficients for the effect of extreme positive spikes in EU

(German) bond yields (falls in prices) on the bitcoin market are positive for

both quantiles. This implies that bitcoin may function as a safe haven for the

most extreme spikes in bond yields. For model 2.1 I accept the alternate

hypothesis that bitcoin is a safe haven for EU bond markets as the estimate

for the 95% quantile in the Eurostoxx model is positive and significant at the

5% level.

The coefficient estimates for the average effect of extreme negative returns

on the euro in the 10% quantile are positive for model 2.1 and negative for

model 2.2. These results are contradictory however neither of them are

statistically significant. The results for the 5% quantiles in both models

representing extreme devaluations in the euro of 2.3765 and 2.0739

31

TABLE 4: SUMMARY RESULTS OF MODEL 2 FOR EU MARKET

EU Model 2.1 (Eurostoxx) EU Model 2.2 (Dax)

Bitcoin Coeff. Est. Std err. P-value Coeff. Est. Std. err. P-value

0.0036 0.0010 0.0002*** 0.0026 0.0009 0.0030*** -0.0271 0.0629 0.6672 -0.0012 0.0869 0.9892 (10%) 0.2150 0.1379 0.1191 0.1491 0.2067 0.4706 (5%) 0.0022 0.1179 0.9854 0.3477 0.1440 0.0158** 0.0501 0.0339 0.1395 0.0649 0.0388 0.0946* (90%) 0.0866 0.0635 0.1728 0.0823 0.0986 0.4042 (95%) 0.1205 0.0585 0.0395** 0.0736 0.0936 0.4318 -0.3905 0.2419 0.1065 -0.0457 0.2635 0.8623 (10%) 0.7948 0.8037 0.3227 -0.5668 0.7916 0.4740 (5%) 2.3765 0.9784 0.0151*** 2.0739 0.9901 0.0362**

Var. eq.

0.0001 0.0000 0.0485** 0.0001 0.0000 0.0313** 0.3148 0.0723 0.0000*** 0.3191 0.0725 0.0000***

0.7635 0.0340 0.0000*** 0.7534 0.0353 0.0000***


Table 5 presents a summary of the GARCH (1, 1) estimation results for model 2 of the EU market with the Eurostoxx 50 and Dax indices as

the equity market variables. For full results, along with the results of T-GARCH and IGARCH models see Appendix (Tables 17 – 18). The

results indicate that bitcoin may be a hedge (negatively correlated) but is not a safe haven (positive and significant at 5% level in model 2.2)

for EU equity markets. The results show that bitcoin is a hedge (positive and significant at 10% level in model 2.2) and a safe haven (positive

and significant at 5% level in model 2.1) for EU bond markets. They also indicate that bitcoin may be a hedge for the euro, but is not a safe

haven due to a very strong positive correlation with the 5% quantile of extreme euro devaluations (significant at 5% level in both models).

32

respectively are both statistically significant at the 5% level and indicate that

bitcoin does not function as a safe haven in times of extreme euro

devaluations.

The coefficient estimates for the average effect of returns on equities, bonds

and the euro on the bitcoin market are consistent across the models and with

the results of model 1.1 above. This may give a clearer idea of the general

direction of the relationships tested and could be an indicator that the

contradictory results from model 1.2, with the Dax as the equity market proxy,

are due to a poorly fitting model. For the average effect of equity market

returns on bitcoin returns the estimated coefficients are both small and

negative, however, neither of them are statistically significant. For bond

yields, the estimated coefficients are both small and positive, implying a

negative relationship between bond prices and bitcoin. For model 2.2 the

estimated coefficient is statistically significant at the 10% level indicating that

bitcoin is a hedge for the EU bond market. For euro returns, the values of the

estimated coefficients are both negative implying that bitcoin may serve as a

hedge for the euro, however, they are not statistically significant.

Overall the results from estimating model 2 for the European market provide

further support for the idea that bitcoin returns are orthogonal to returns in

equity markets, given the small size and lack of statistical significance of the

estimated coefficients. I find that bitcoin plays a hedge and a safe haven role

for European bond markets. I also find that bitcoin may play a role as a

hedge, but is not a safe haven for the euro.

33

6: Conclusion

In this dissertation I investigate the correlations between bitcoin and equity,

bond, and currency markets in the US and Europe using daily data. The

purpose of this analysis is to determine bitcoin’s characteristics as an

investable asset. Qualitatively I argue that bitcoin can be viewed as a form of

virtual gold due to a number of similarities in its design such as its durability

and divisibility. Commentators have drawn attention to the similarities

between bitcoin and the former gold standard, namely the fixed supply of

bitcoin which grows at a predetermined rate. This comparison is backed up by

the use of gold related terminology such as “mining” for the creation of new

bitcoins. From reviewing the literature on the topic I find that generally the role

gold plays in financial markets is as a hedge or a safe haven during periods of

market turbulence. I argue that in theory bitcoin could play the same role due

to its exogeneity to the established network of markets and financial

institutions. The investigation of correlations between asset classes is of

importance as it can benefit market participants in guiding and informing their

investment strategies. As bitcoin continues to grow it may play a greater role

in investor’s portfolios as a low cost alternative to gold.

Empirically I find that bitcoin is not a hedge or a safe haven for US equity

markets and is positively correlated with stock returns for every model

estimated, however, all of the coefficients are small and none of the estimates

are statistically significant suggesting a weak relationship. This indicates that

bitcoin may play a role as a diversifier. For EU equity markets three out of the

four coefficients estimated indicate a negative relationship between bitcoin

and stock returns. This suggests that bitcoin could play a hedge role however

none of the estimates are statistically significant. In addition bitcoin is

positively correlated with extreme negative returns on EU equities indicating it

is not a safe haven. The lack of statistical significance and the small size of all

estimated coefficients indicate that bitcoin returns are orthogonal to equity

returns.

34

For the US bond market I find that bitcoin is a hedge as it is positively

correlated with bond yields in all of the models estimated. In addition, it may

serve as a safe haven for the most extreme periods of spikes in yields

(represented by the 95% quantile). The results of the models estimated for

European markets consistently show that bitcoin is a hedge and a safe haven

for bonds. The coefficient estimates for the effect of changes in bond yields

on bitcoin returns are small suggesting a weak relationship between the two

markets. Therefore I find that bitcoin may serve as a hedge against falling

bond markets. This may be due to the inflation protection that the deflationary

bitcoin can afford; however, a longer sample would be needed to test this

idea given that over the lifetime of the virtual currency interest rates and

inflation have been at consistently low levels.

For currency markets, the coefficient estimates representing bitcoin’s

relationship with the dollar are consistently positive and those with the euro

are consistently negative. This implies that bitcoin could serve as a hedge

against euro devaluations, but is not a hedge against the dollar. I also find

that bitcoin may play a safe haven role for extreme negative returns on the

dollar but does not play the same role for the euro. In fact bitcoin has a strong

positive correlation with extreme euro devaluations (as measured by the 5%

quantile). It is interesting to note that the estimated coefficients for the

currency markets, whilst not all significant, are consistently larger than those

representing equity and bond markets. This is supportive of the idea that

bitcoin is a monetary asset, which is a similarity that it shares with gold.

My main conclusion from estimation is that for the sample tested bitcoin

returns appear for the most part to be relatively statistically independent of

equity and bond markets. Where there is a statistically significant relationship

the size of the estimated coefficients is generally small indicating a weak

relationship. Stronger relationships appear to exist with currency markets but

the lack of consistently significant estimates is a major issue. I therefore fail to

reject the null hypothesis that as an asset, bitcoin does not behave like gold,

given that for the sample tested bitcoin returns appear to be relatively

orthogonal to returns on stocks, bonds and the dollar.

35

It is important to note that as bitcoin has only been in existence for a number

of years, its relationships with other markets are more volatile than those

between more mature asset classes. These correlations are based on capital

flows across markets and are often learned over time. If bitcoin adoption

continues at the current rate and a clear regulatory framework is put in place,

it is likely that more institutional investors will enter the market, strengthening

the correlations between bitcoin and other more traditional investment

markets. Future research could focus on the dynamic nature of these

correlations, perhaps using a similar methodology as that pursued by Ciner,

Gurdgiev et al. (2010, revised 2012).

The small sample size was one of the major issues I faced in my empirical

study of the virtual currency. Despite this problem I feel that this dissertation

was worthwhile as a primary investigation of the relationship between bitcoin

and more established markets. In addition it is important to note that the

period since bitcoin’s inception has been one of changing correlations even

across well established markets due to the unprecedented level of

unconventional monetary policy, which no doubt has also had some effect on

the bitcoin market. Future research could extend the data examined not only

through time but also across more markets. For the Eurozone in particular it

may be interesting to examine the effect of changes in the spread between

core and periphery bond yields on bitcoin, as these are likely to better

represent negative shocks to the European bond market.

36

References

Archer, P., G. Schvey, J. Schvey and J. Stacke (2013), ‘2013 Bitcoin Mid-

Year Review and Outlook’, The Genesis Block. Available from:

http://thegenesisblock.com/bitcoin-mid-year-review-and-outlook/

Asteriou, D., and S. Hall (2007), Applied Econometrics: A Modern Approach

using Eviews and Microfit. Published by: Palgrave Macmillan (London)

Batson, A. (2009), ‘China takes aim at the dollar’, Wall Street Journal, (March

24, 2009). Available from:

http://online.wsj.com/article/SB123780272456212885.html

Baur, D., and B. Lucey (2010), ‘Is gold a hedge or a safe haven? An analysis

of stocks, bonds and gold.’ Financial Review 45.2: 217-229. Available from:

http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6288.2010.00244.x/full

Bitcoin Charts, Available at: http://bitcoincharts.com/

Blose, L. (2010), ‘Gold Prices, Cost of Carry, and Expected Inflation’, Journal

of Economics and Business 62(1): 35-47.

Capie, F., C. Mills and G. Wood (2005), ‘Gold as a Hedge against the Dollar.’

Journal of International Financial Markets. Available from:

http://ejournal.narotama.ac.id/files/Gold%20as%20a%20hedge%20against%2

0the%20dollar.pdf

Ciner, C., C. Gurdgiev and B. Lucey (2010; Revised 2012), ‘Hedges and Safe

Havens – An Examination of Stocks, Bonds, Oil, Gold and the Dollar.’ Bonds,

Oil, Gold and the Dollar. Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1679243

Christin, C., and T. Moore (2013), ‘Beware the Middleman: Empirical Analysis

of Bitcoin-Exchange Risk.’ Available from: http://fc13.ifca.ai/proc/1-2.pdf

ECB (2012), ‘Virtual Currency Schemes’. Available from:

http://www.ecb.int/pub/pdf/other/virtualcurrencyschemes201210en.pdf

http://thegenesisblock.com/bitcoin-mid-year-review-and-outlook/

http://online.wsj.com/article/SB123780272456212885.html


http://bitcoincharts.com/

http://ejournal.narotama.ac.id/files/Gold%20as%20a%20hedge%20against%20the%20dollar.pdf

http://ejournal.narotama.ac.id/files/Gold%20as%20a%20hedge%20against%20the%20dollar.pdf


http://fc13.ifca.ai/proc/1-2.pdf


37

Fortune, J. (1987), ‘The Inflation Rate of the Price of Gold, Expected Prices

and Interest Rates’, Journal of Macroeconomics 9(1): 71-82.

GAO (2013), ‘Virtual Economies and Currencies – Additional IRS Guidance

Could Reduce Tax Compliance Risks’. Available from:

http://www.gao.gov/assets/660/654620.pdf

Ghosh, D., E. Levin, P. Macmillan and R. Wright (2004), ‘Gold as an Inflation

Hedge’, Studies in Economics and Finance 22: 1-25.

Grinberg, R. (2011), ‘Bitcoin: An Innovative Alternative Digital Currency’,

Hastings Sci. & Tech. LJ 4: 159. Available from:

http://heinonline.org/HOL/Page?handle=hein.journals/hascietlj4&div=6&g_sen

t=1&collection=journals

Hammoudeh, S., R. Sari and B. Ewing (2009), ‘Relationships among strategic

commodities and with financial variables: A new look’, Contemporary

Economic Policy 27(2): 251-264.

Hill, R., W. Griffiths and G. Lim (2012), Principles of Econometrics (4th

Edition). Published by: John Wiley & Sons (Asia)

http://bitcoin.org/

Johnson, R., and L. Soenen (1997), ‘Gold as an investment asset -

perspectives from different countries’, Journal of Investing 6(3): 94-99.

Lucey, B. (2011), ‘What do academics think they know about gold.’ The

Alchemist 62: 12-14. Available from:

http://www.lbma.org.uk/assets/Alch6203Lucey.pdf

Moore, G. (1990), ‘Gold Prices as a Leading Index of Inflation’, Challenge 33:

52-56.

Nakamoto, S. (2009), ‘Bitcoin: A Peer-to-Peer Electronic Cash System’.

Available from: http://bitcoin.org/bitcoin.pdf

http://www.gao.gov/assets/660/654620.pdf

http://heinonline.org/HOL/Page?handle=hein.journals/hascietlj4&div=6&g_sent=1&collection=journals


http://bitcoin.org/


http://bitcoin.org/bitcoin.pdf

38

Selgin, G. (2013), ‘Synthetic Commodity Money.’ Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract-id=2000118

Sjaastad, L. (2008), ‘The price of gold and the exchange rates: Once again’,

Resources Policy 33(2): 118-124.

Surowiecki, J. (2011), ‘Cryptocurrency’, MIT Technology Review. Available

from: http://www.technologyreview.com/review/425142/cryptocurrency/

Taylor, N. (1998), ‘Precious metals and inflation’, Applied Financial

Economics 8(2): 201-210.

Tully, E. and B. Lucey (2007), ‘A Power GARCH Examination of the Gold

Market’, Research in International Business and Finance 21(2): 316-25.

Wang, K., Y. Lee and T. Thi (2010), ‘Time and place where gold acts as an

inflation hedge: An application of long-run and short-run threshold model’,

Economic Modelling.

Worthington, A. and M. Pahlavani (2007), ‘Gold investment as an inflationary

hedge: Cointegration evidence with allowance for endogenous structural

breaks’, Applied Financial Economics Letters 3(4): 259-262.


http://www.technologyreview.com/review/425142/cryptocurrency/

39

Appendix

FIGURE 1: VOLUME OF BITCOIN TRADED BY CURRENCY

Source: www.bitcoincharts.com

TABLE 5: AVG DAILY VOLUME TRADED BY CURRENCY

Vol. in Bitcoin25 as a %

Total 53,579.86

USD 41,257.52 77.00%

EUR 4,494.64 8.39%

CNY 3,405.23 6.36%

AUD 938.74 1.75%

GBP 655.84 1.22%


25

Calculated as a 15-day EMA of volumes traded for top five most commonly exchanged

currencies.

-

50,000.00

100,000.00

150,000.00

200,000.00

250,000.00

300,000.00

350,000.00

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

EUR CNY AUD GBP USD (right axis)


40

FIGURE 2: EXPECTED TOTAL NO. OF BITCOIN OVER TIME

Source: Grinberg (2011, p. 164)

FIGURE 3: BREAKDOWN OF VIRTUAL CURRENCY SCHEME TYPES

Source: ECB (2012, p.15)

41

FIGURE 4: ILLUSTRATION OF BITCOIN TRANSACTIONS

Source: ECB (2012, p. 23)

FIGURE 5: PLOT OF BITCOIN PRICE (LINEAR SCALE)


0

40

80

120

160

200

240

III IV I II III IV I II III IV I II

2010 2011 2012 2013

Bitcoin

42

FIGURE 6: PLOT OF BITCOIN PRICE (LOGARITHMIC SCALE)


FIGURE 7: PLOT OF S&P 500 INDEX LEVEL

400.00

40.00

4.00

0.40

0.04


2010 2011 2012 2013

Bitcoin

1,000

1,100

1,200

1,300

1,400

1,500

1,600

1,700


2010 2011 2012 2013

S&P 500

43

FIGURE 8: PLOT OF LEVEL OF DOW JONES INDEX

FIGURE 9: PLOT OF LEVEL OF RUSSELL 2000 INDEX

9,000

10,000

11,000

12,000

13,000

14,000

15,000

16,000


2010 2011 2012 2013

Dow Jones Industrial Average

500

600

700

800

900

1,000

1,100


2010 2011 2012 2013

Russell 2000

44

FIGURE 10: PLOT OF US GOVERNMENT TEN YEAR BOND YIELD

FIGURE 11: PLOT OF US 10 YEAR YIELD (LEFT AXIS) AND S&P 500 (RIGHT

AXIS)

1.0

1.5

2.0

2.5

3.0

3.5

4.0


2010 2011 2012 2013

US Government Ten Year Bond Yield

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1,000

1,200

1,400

1,600

1,800


2010 2011 2012 2013

US Government Ten Year Bond Yield

S&P 500

45

FIGURE 12: PLOT OF LEVEL OF US DOLLAR INDEX

FIGURE 13: PLOT OF LEVEL OF EUROSTOXX 50 INDEX

66

68

70

72

74

76

78

80


2010 2011 2012 2013

US Dollar Index

1,800

2,000

2,200

2,400

2,600

2,800

3,000

3,200


2010 2011 2012 2013

Eurostoxx 50

46

FIGURE 14: PLOT OF LEVEL OF GERMAN DAX INDEX

FIGURE 15: PLOT OF GERMAN GOVERNMENT TEN YEAR BOND YIELD

5,000

5,500

6,000

6,500

7,000

7,500

8,000

8,500

9,000


2010 2011 2012 2013

Dax

1.0

1.5

2.0

2.5

3.0

3.5

4.0


2010 2011 2012 2013

German Government Ten Year Bond Yield

47

FIGURE 16: PLOT OF LEVEL OF EURO CURRENCY INDEX

TABLE 6: DESCRIPTIVE STATISTICS FOR US MARKET DATA

S&P 500 DJIA Russell USTY USDX

Mean 0.0006 0.0005 0.0006 -0.0002 0.0000

Median 0.0007 0.0007 0.0014 -0.0019 0.0000

Maximum 0.0463 0.0415 0.0671 0.1053 0.0214

Minimum -0.0690 -0.0571 -0.0933 -0.0988 -0.0176

Std. Dev. 0.0111 0.0100 0.0156 0.0273 0.0045

Skewness -0.4728 -0.4669 -0.2907 0.0978 0.2847

Kurtosis 7.6499 7.1520 6.9031 3.8764 4.7916

Jarque-Bera 672.66 541.07 465.21 24.08 105.58

Probability 0.0000 0.0000 0.0000 0.0000 0.0000

Sum 0.3964 0.3767 0.4523 -0.1544 0.0036

Sum Sq. Dev. 0.0875 0.0715 0.1734 0.5336 0.0144

Observations 717 717 717 717 717

90

92

94

96

98

100

102

104


2010 2011 2012 2013

Euro Currency Index

48

TABLE 7: DESCRIPTIVE STATISTICS FOR EU MARKET DATA AND BITCOIN

Bitcoin Eurostoxx 50 Dax Bund EURX

Mean 0.0101 0.0000 0.0004 -0.0006 0.0000

Median 0.0037 -0.0001 0.0009 -0.0008 0.0001

Maximum 0.4246 0.0590 0.0521 0.1491 0.0135

Minimum -0.4501 -0.0654 -0.0642 -0.1358 -0.0120

Std. Dev. 0.0853 0.0149 0.0141 0.0300 0.0033

Skewness -0.2253 -0.1363 -0.2378 0.1663 -0.1258

Kurtosis 9.2521 5.0582 5.5690 4.9859 3.5804

Jarque-Bera 1173.82 128.77 203.92 121.12 11.95

Probability 0.0000 0.0000 0.0000 0.0000 0.0025

Sum 7.2117 -0.0328 0.2595 -0.4299 -0.0225

Sum Sq. Dev. 5.2043 0.1581 0.1432 0.6459 0.0080

Observations 717 717 717 717 717

FIGURE 17: ROLLING 30-DAY CORRELATIONS WITH US EQUITY MARKET

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

DJIA S&P 500 Russel 2000

49

FIGURE 18: ROLLING 30-DAY CORRELATIONS WITH EU EQUITY MARKET

FIGURE 19: ROLLING 30-DAY CORRELATIONS WITH BOND YIELDS

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

Eurostoxx 50 Dax

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

USTY GTY

50

FIGURE 20: ROLLING 30-DAY CORRELATIONS WITH CURRENCY MARKETS

TABLE 8: SUMMARY STATISTICS FOR ROLLING 30-DAY CORRELATIONS

Obsv. Mean Std. Dev. Maximum Minimum

S&P 500 688 0.0933 0.1691 0.4685 -0.4060

DJIA 688 0.0862 0.1678 0.4890 -0.4109

Russell 688 0.1022 0.1750 0.4677 -0.4844

USTY 688 0.0679 0.1706 0.4990 -0.4467

USDX 688 -0.0541 0.1804 0.3431 -0.5379

Eurostoxx 50 688 0.0687 0.1697 0.5378 -0.3803

Dax 688 0.0680 0.1953 0.5427 -0.3369

Bund 688 0.0759 0.1540 0.3708 -0.4452

EURX 688 0.0535 0.1901 0.5013 -0.5442

-0.6000

-0.4000

-0.2000

0.0000

0.2000

0.4000

0.6000

USDX EURX

51

TABLE 9: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 1.1 US MARKET (S&P 500)

S&P 500

Bitcoin Coeff. est. Std. err. t -stat P -value Coeff. est. Std. err. t -stat P -value Coeff. est. Std. err. t -stat P -value

0.0022 0.0007 3.3202 0.0009 0.0021 0.0006 3.4680 0.0005 0.0020 0.0006 3.1407 0.0017

0.0771 0.0823 0.9367 0.3489 0.1317 0.0764 1.7234 0.0848 0.1341 0.0898 1.4936 0.1353

* 0.0481 0.0286 1.6831 0.0924 0.0334 0.0313 1.0677 0.2857 0.0309 0.0298 1.0383 0.2991

0.2979 0.2133 1.3970 0.1624 0.4158 0.1999 2.0807 0.0375 0.4426 0.1439 3.0766 0.0021

Variance Equation

0.0001 0.0000 2.0588 0.0395 0.0001 0.0000 1.7691 0.0769 - - - -

0.3232 0.0724 4.4668 0.0000 0.3354 0.0797 4.2068 0.0000 0.1475 0.0138 10.7088 0.0000

0.7559 0.0339 22.2653 0.0000 0.7942 0.0291 27.2872 0.0000 0.8525 0.0138 61.9093 0.0000

- - - - -0.1528 0.0840 -1.8185 0.0690 - - - -

GED Par. 0.7542 0.0462 16.3317 0.0000 0.7526 0.0452 16.6615 0.0000 0.7938 0.0411 19.3246 0.0000

Information Criterion

Akaike -2.8812 -2.8842 -2.8536

Schw artz -2.8302 -2.8268 -2.8153

Log-Likelihood 1040.92 1043.00 1029.02

Durbin-Watson 1.760203 1.75896 1.7584

GARCH (1, 1) T-GARCH IGARCH

52

TABLE 10: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 1.2 US MARKET (DOW JONES)

Dow Jones


0.0021 0.0006 3.5240 0.0004 0.0021 0.0006 3.2873 0.0010 0.0021 0.0006 3.4894 0.0005

0.1228 0.0861 1.4262 0.1538 0.1383 0.0480 2.8824 0.0039 0.1193 0.0790 1.5104 0.1309

* 0.0367 0.0276 1.3322 0.1828 0.0343 0.0217 1.5841 0.1132 0.0357 0.0278 1.2855 0.1986

0.3768 0.1964 1.9186 0.0550 0.4042 0.2049 1.9728 0.0485 0.3669 0.1611 2.2779 0.0227

Variance Equation

0.0001 0.0000 1.9751 0.0483 0.0001 0.0000 1.7568 0.0789 - - - -

0.3164 0.0710 4.4558 0.0000 0.3453 0.0825 4.1848 0.0000 0.1467 0.0136 10.7773 0.0000

0.7630 0.0331 23.0262 0.0000 0.7917 0.0294 26.9513 0.0000 0.8533 0.0136 62.7094 0.0000

- - - - -0.1598 0.0863 -1.8528 0.0639 - - - -

GED Par. 0.7447 0.0457 16.3057 0.0000 0.7535 0.0460 16.3869 0.0000 0.8015 0.0415 19.3264 0.0000


Akaike -2.8820 -2.8847 -2.8536

Schw artz -2.8310 -2.8273 -2.8154

Log-Likelihood 1041.21 1043.16 1029.03

Durbin-Watson 1.7596 1.7595 1.7597


53

TABLE 11: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 1.3 US MARKET (RUSSELL 2000)

Russell 2000


0.0026 0.0007 3.5947 0.0003 0.0020 0.0007 3.0691 0.0021 0.0021 0.0006 3.4847 0.0005

0.0749 0.0733 1.0221 0.3067 0.0791 0.0700 1.1304 0.2583 0.0750 0.0670 1.1198 0.2628

* 0.0228 0.0319 0.7124 0.4762 0.0342 0.0321 1.0649 0.2869 0.0526 0.0273 1.9225 0.0545

0.2961 0.2079 1.4241 0.1544 0.3271 0.2058 1.5892 0.1120 0.4314 0.1814 2.3784 0.0174

Variance Equation

0.0001 0.0000 2.0065 0.0448 0.0001 0.0000 1.7640 0.0777 - - - -

0.3158 0.0692 4.5613 0.0000 0.3509 0.0841 4.1746 0.0000 0.1464 0.0137 10.6842 0.0000

0.7599 0.0328 23.1889 0.0000 0.7896 0.0296 26.7201 0.0000 0.8536 0.0137 62.3084 0.0000

- - - - -0.1667 0.0877 -1.9014 0.0573 - - - -

GED Par. 0.7584 0.0461 16.4644 0.0000 0.7512 0.0457 16.4531 0.0000 0.7963 0.0415 19.2012 0.0000


Akaike -2.8792 -2.8825 -2.8522

Schw artz -2.8282 -2.8250 -2.8139

Log-Likelihood 1040.21 1042.36 1028.51

Durbin-Watson 1.7605 1.7583 1.7581


54

TABLE 12: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 1.1 EU MARKET (EUROSTOXX 50)

Eurostoxx

50


0.0035 0.0007 4.9032 0.0000 0.0035 0.0008 4.6944 0.0000 0.0033 0.0006 5.4809 0.0000

-0.0660 0.0414 -1.5940 0.1109 -0.0546 0.0712 -0.7674 0.4429 -0.0529 0.0537 -0.9842 0.3250

* 0.1645 0.0292 5.6366 0.0000 0.1166 0.0311 3.7514 0.0002 0.1391 0.0241 5.7788 0.0000

-0.2184 0.2318 -0.9422 0.3461 0.2184 0.2590 0.8431 0.3992 -0.4251 0.2021 -2.1033 0.0354

Variance

Equation

0.0001 0.0000 2.1058 0.0352 0.0001 0.0000 1.9475 0.0515 - - - -

0.3034 0.0701 4.3309 0.0000 0.3371 0.0800 4.2127 0.0000 0.1460 0.0139 10.4917 0.0000

0.7651 0.0341 22.4195 0.0000 0.7907 0.0295 26.8389 0.0000 0.8540 0.0139 61.3557 0.0000

- - - - -0.1553 0.0826 -1.8802 0.0601 - - - -

GED Par. 0.7494 0.0461 16.2675 0.0000 0.7745 0.0465 16.6697 0.0000 0.7963 0.0410 19.4034 0.0000

Information

Criterion

Akaike -2.8846 -2.8861 -2.8554

Schw artz -2.8336 -2.8287 -2.8171

Log-

Likelihood 1042.14 1043.66 1029.66

Durbin-

Watson 1.7661 1.7666 1.7655


55

TABLE 13: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 1.2 EU MARKET (DAX)

Dax


0.0033 0.0007 4.4617 0.0000 0.0031 0.0008 4.0244 0.0001 0.0035 0.0006 5.4355 0.0000

0.0539 0.0792 0.6804 0.4962 0.0998 0.0751 1.3287 0.1840 -0.0231 0.0443 -0.5212 0.6022

* 0.0693 0.0277 2.5031 0.0123 0.0759 0.0328 2.3139 0.0207 0.1315 0.0236 5.5799 0.0000

0.2027 0.2434 0.8328 0.4050 0.1335 0.2180 0.6124 0.5403 -0.4000 0.2072 -1.9306 0.0535

Variance

Equation

0.0001 0.0000 2.1375 0.0326 0.0001 0.0000 2.0051 0.0450 - - - -

0.3136 0.0706 4.4398 0.0000 0.3355 0.0796 4.2152 0.0000 0.1472 0.0139 10.5917 0.0000

0.7583 0.0342 22.1958 0.0000 0.7878 0.0298 26.4346 0.0000 0.8528 0.0139 61.3431 0.0000

- - - - -0.1499 0.0829 -1.8087 0.0705 - - - -

GED Par. 0.7612 0.0469 16.2182 0.0000 0.7784 0.0469 16.5974 0.0000 0.8019 0.0416 19.2707 0.0000

Information

Criterion

Akaike -2.8829 -2.8848 -2.8543

Schw artz -2.8319 -2.8274 -2.8161

Log-

Likelihood 1041.52 1043.21 1029.28

Durbin-

Watson 1.7658 1.7649 1.7662


56

TABLE 14: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 2.1 US MARKET (S&P 500)

S&P 500


0.0021 0.0008 2.7067 0.0068 0.0027 0.0008 3.2294 0.0012 0.0024 0.0007 3.5663 0.0004

0.1457 0.1057 1.3779 0.1682 0.1557 0.1088 1.4303 0.1526 0.1036 0.0954 1.0861 0.2774

[5%] 0.0535 0.1900 0.2817 0.7782 0.1014 0.2036 0.4983 0.6183 0.2092 0.1652 1.2665 0.2053

[10%] 0.1223 0.2140 0.5715 0.5677 0.0948 0.2027 0.4680 0.6398 0.0372 0.1836 0.2025 0.8395

0.0255 0.0377 0.6762 0.4989 0.0133 0.0397 0.3347 0.7378 0.0502 0.0311 1.6126 0.1068

[90%] -0.1076 0.0966 -1.1137 0.2654 0.0269 0.1043 0.2580 0.7964 0.0360 0.0816 0.4408 0.6594

[95%] 0.0413 0.0917 0.4504 0.6524 -0.0909 0.1072 -0.8482 0.3963 -0.1032 0.0824 -1.2524 0.2104

0.4753 0.2664 1.7846 0.0743 0.4981 0.2682 1.8569 0.0633 0.7874 0.2159 3.6477 0.0003

[5%] -0.0553 0.5347 -0.1035 0.9176 0.0934 0.4908 0.1902 0.8491 -0.4617 0.5019 -0.9200 0.3576

[10%] -0.2125 0.5132 -0.4141 0.6788 -0.3081 0.4467 -0.6897 0.4904 -0.1666 0.5506 -0.3025 0.7623

Variance

Equation

0.0001 0.0000 1.9355 0.0529 0.0000 0.0000 1.7572 0.0789 - - - -

0.3220 0.0725 4.4421 0.0000 0.3399 0.0808 4.2074 0.0000 0.1444 0.0137 10.5115 0.0000

0.7625 0.0331 23.0135 0.0000 0.7958 0.0289 27.5704 0.0000 0.8556 0.0137 62.2608 0.0000

- - - - -0.1656 0.0842 -1.9660 0.0493 - - - -

GED Par. 0.7400 0.0458 16.1435 0.0000 0.7578 0.0459 16.5031 0.0000 0.7872 0.0412 19.0951 0.0000

Schw artz -2.7785 -2.7746 -2.7658

Log-

Likelihood 1042.11 1044.00 1030.99

Durbin-

Watson 1.7609 1.7640 1.7646


57

TABLE 15: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 2.2 US MARKET (DOW JONES)

Dow Jones


0.0039 0.0009 4.4855 0.0000 0.0042 0.0010 4.0582 0.0000 0.0036 0.0009 3.9985 0.0001

0.0189 0.0821 0.2307 0.8176 -0.0296 0.1355 -0.2187 0.8269 -0.0102 0.1259 -0.0814 0.9351

[5%] 0.1099 0.1631 0.6738 0.5004 0.0792 0.2438 0.3249 0.7452 0.1384 0.2256 0.6136 0.5395

[10%] 0.3289 0.1670 1.9697 0.0489 0.3365 0.2810 1.1974 0.2311 0.3237 0.2588 1.2509 0.2110

0.0218 0.0318 0.6865 0.4924 0.0315 0.0431 0.7320 0.4642 0.0444 0.0302 1.4717 0.1411

[90%] -0.1134 0.0979 -1.1582 0.2468 0.0663 0.1045 0.6344 0.5258 0.0628 0.0809 0.7761 0.4377

[95%] 0.0630 0.0960 0.6563 0.5116 -0.0611 0.1099 -0.5557 0.5784 -0.1097 0.0899 -1.2207 0.2222

0.5140 0.2532 2.0302 0.0423 0.1864 0.2934 0.6353 0.5253 0.6985 0.2130 3.2792 0.0010

[5%] -0.4104 0.5420 -0.7571 0.4490 0.0562 0.3166 0.1774 0.8592 -0.6945 0.5190 -1.3382 0.1808

[10%] 0.2528 0.5395 0.4685 0.6394 0.1374 0.5777 0.2378 0.8120 0.2901 0.5839 0.4968 0.6193

Variance

Equation

0.0001 0.0000 2.0127 0.0441 0.0000 0.0000 1.8193 0.0689 - - - -

0.3119 0.0690 4.5183 0.0000 0.3229 0.0744 4.3409 0.0000 0.1438 0.0134 10.7274 0.0000

0.7652 0.0323 23.6605 0.0000 0.8024 0.0277 28.9756 0.0000 0.8562 0.0134 63.8596 0.0000

- - - - -0.1617 0.0774 -2.0881 0.0368 - - - -

GED Par. 0.7509 0.0465 16.1491 0.0000 0.7745 0.0466 16.6110 0.0000 0.7936 0.0414 19.1887 0.0000

Akaike -2.8711 -2.8719 -2.8447

Schw artz -2.7818 -2.7762 -2.7681

Log-

Likelihood 1043.29 1044.58 1031.81

Durbin-

Watson 1.7668 1.7687 1.7675


58

TABLE 16: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 2.3 US MARKET (RUSSELL 2000)

Russell

2000


0.0025 0.0009 2.8098 0.0050 0.0026 0.0010 2.4990 0.0125 0.0022 0.0009 2.4803 0.0131

0.0168 0.0867 0.1941 0.8461 0.0796 0.1006 0.7908 0.4291 0.0805 0.0872 0.9225 0.3563

[5%] 0.1385 0.1949 0.7106 0.4773 0.1992 0.1919 1.0378 0.2994 0.2228 0.1701 1.3100 0.1902

[10%] -0.0056 0.2235 -0.0249 0.9802 -0.0519 0.2350 -0.2211 0.8250 -0.0668 0.2078 -0.3217 0.7477

0.0518 0.0300 1.7250 0.0845 0.0420 0.0317 1.3233 0.1857 0.0443 0.0311 1.4249 0.1542

[90%] -0.0679 0.1024 -0.6636 0.5069 -0.1302 0.0783 -1.6631 0.0963 -0.0289 0.0902 -0.3204 0.7487

[95%] 0.0321 0.1115 0.2882 0.7732 0.1094 0.0920 1.1896 0.2342 -0.0321 0.0961 -0.3347 0.7379

0.3777 0.2723 1.3867 0.1655 0.5768 0.2628 2.1950 0.0282 0.7699 0.2084 3.6944 0.0002

[5%] -0.2601 0.5717 -0.4549 0.6492 -0.1719 0.5737 -0.2997 0.7644 -0.6725 0.4989 -1.3480 0.1777

[10%] -0.2416 0.6436 -0.3754 0.7074 -0.0631 0.6338 -0.0996 0.9207 -0.0451 0.5956 -0.0758 0.9396

Variance

Equation

0.0001 0.0000 1.9592 0.0501 0.0001 0.0000 1.7647 0.0776 - - - -

0.3204 0.0713 4.4910 0.0000 0.3481 0.0828 4.2065 0.0000 0.1470 0.0137 10.7354 0.0000

0.7626 0.0330 23.1361 0.0000 0.7907 0.0294 26.9226 0.0000 0.8530 0.0137 62.2903 0.0000

- - - - -0.1635 0.0868 -1.8846 0.0595 - - - -

GED Par. 0.7504 0.0466 16.0971 0.0000 0.7587 0.0461 16.4533 0.0000 0.7939 0.0411 19.3003 0.0000

Akaike -2.8653 -2.7736 -2.8408

Schw artz -2.7760 -2.8324 -2.7642

Log-

Likelihood 1041.21 1043.66 1030.41

Durbin-

Watson 1.7637 1.7607 1.7618


59

TABLE 17: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 2.1 EU MARKET (EUROSTOXX 50)

Eurostoxx

50


0.0036 0.0010 3.6733 0.0002 0.0034 0.0009 3.6309 0.0003 0.0032 0.0009 3.6748 0.0002

-0.0271 0.0629 -0.4300 0.6672 -0.0810 0.0858 -0.9451 0.3446 -0.0946 0.0848 -1.1162 0.2643

[5%] 0.2150 0.1379 1.5586 0.1191 0.2706 0.1247 2.1709 0.0299 0.3469 0.1353 2.5648 0.0103

[10%] 0.0022 0.1179 0.0183 0.9854 -0.1679 0.1355 -1.2392 0.2153 -0.2635 0.1164 -2.2643 0.0236

0.0501 0.0339 1.4777 0.1395 0.0369 0.0154 2.3976 0.0165 0.0098 0.0283 0.3457 0.7296

[90%] 0.0866 0.0635 1.3634 0.1728 0.1198 0.0881 1.3603 0.1737 0.1087 0.0667 1.6305 0.1030

[95%] 0.1205 0.0585 2.0594 0.0395 0.0581 0.0913 0.6370 0.5242 0.1069 0.0658 1.6251 0.1041

-0.3905 0.2419 -1.6140 0.1065 -0.0574 0.2138 -0.2683 0.7884 0.1110 0.2215 0.5011 0.6163

[5%] 0.7948 0.8037 0.9890 0.3227 -0.3490 0.7842 -0.4450 0.6563 -1.1442 0.5765 -1.9846 0.0472

[10%] 2.3765 0.9784 2.4290 0.0151 3.2482 0.9782 3.3207 0.0009 3.7800 0.7261 5.2059 0.0000

Variance

Equation

0.0001 0.0000 1.9729 0.0485 0.0000 0.0000 1.7584 0.0787 - - - -

0.3148 0.0723 4.3544 0.0000 0.3446 0.0811 4.2511 0.0000 0.1464 0.0139 10.5158 0.0000

0.7635 0.0340 22.4582 0.0000 0.7921 0.0294 26.9669 0.0000 0.8536 0.0139 61.3250 0.0000

- - - - -0.1545 0.0858 -1.8014 0.0716 - - - -

GED Par. 0.7392 0.0465 15.9050 0.0000 0.7511 0.0468 16.0525 0.0000 0.7968 0.0413 19.3047 0.0000

Akaike -2.8825 -2.8880 -2.8592

Schw artz -2.7932 -2.7923 -2.7826

Log-

Likelihood 1047.39 1050.36 1037.02

Durbin-

Watson 1.7556 1.7552 1.7548


60

TABLE 18: RESULT OF GARCH, T-GARCH AND IGARCH ESTIMATION FOR MODEL 2.1 EU MARKET (DAX)

Dax


0.0026 0.0009 2.9721 0.0030 0.0021 0.0008 2.5410 0.0111 0.0025 0.0008 3.2027 0.0014

-0.0012 0.0869 -0.0135 0.9892 0.0874 0.0701 1.2456 0.2129 0.0765 0.0726 1.0541 0.2918

[5%] 0.1491 0.2067 0.7215 0.4706 0.2819 0.1943 1.4507 0.1469 0.2859 0.2033 1.4061 0.1597

[10%] 0.3477 0.1440 2.4137 0.0158 0.0318 0.1402 0.2268 0.8206 -0.0003 0.1327 -0.0020 0.9984

0.0649 0.0388 1.6715 0.0946 0.0446 0.0281 1.5906 0.1117 0.0431 0.0321 1.3411 0.1799

[90%] 0.0823 0.0986 0.8342 0.4042 -0.0061 0.0650 -0.0943 0.9249 0.0620 0.0884 0.7014 0.4831

[95%] 0.0736 0.0936 0.7861 0.4318 0.1906 0.0720 2.6488 0.0081 0.1151 0.0833 1.3816 0.1671

-0.0457 0.2635 -0.1734 0.8623 -0.1969 0.2517 -0.7822 0.4341 -0.0155 0.2328 -0.0664 0.9470

[5%] -0.5668 0.7916 -0.7160 0.4740 -0.9871 0.6991 -1.4119 0.1580 -0.5845 0.7503 -0.7789 0.4360

[10%] 2.0739 0.9901 2.0947 0.0362 3.6083 0.9005 4.0069 0.0001 2.6151 0.9131 2.8640 0.0042

Variance

Equation

0.0001 0.0000 2.1536 0.0313 0.0001 0.0000 1.7326 0.0832 - - - -

0.3191 0.0725 4.4039 0.0000 0.3506 0.0836 4.1927 0.0000 0.1485 0.0139 10.6606 0.0000

0.7534 0.0353 21.3345 0.0000 0.7891 0.0302 26.1692 0.0000 0.8515 0.0139 61.1493 0.0000

- - - - -0.1561 0.0888 -1.7569 0.0789 - - - -

GED Par. 0.7580 0.0469 16.1508 0.0000 0.7448 0.0463 16.0715 0.0000 0.8030 0.0419 19.1819 0.0000

Akaike -2.8823 -2.8872 -2.8545

Schw artz -2.7930 -2.7915 -2.7779

Log-

Likelihood 1047.30 1050.05 1035.33

Durbin-

Watson 1.7592 1.7577 1.7604


a

School of Business

MSc in Finance: Dissertation Proposal

Student Name: Cormac Ennis

Student Number: 12321169

Mobile: +353 879022980

E-mail: [email protected]

Date of Submission: 06/05/2013

Proposed Title

Bitcoin: The

Future of

Money?

Supervisor Name: Dr. Constantin Gurdgiev

Certification of Authorship:

I hereby certify that I am the author of this document and that any assistance I received in its

preparation is fully acknowledged and disclosed in the document. I have also cited all sources

from which I obtained data, ideas or words that are copied directly or paraphrased in the

document. Sources are properly credited according to accepted standards for professional

publications. I also certify that this proposal was prepared by me for the purpose of partial

fulfilment of requirements for the MSc in Finance Programme.

Signature Date

b

1. Aims & Objectives

I. Research Aim

The objective of this dissertation is to conduct an in-depth economic analysis of the virtual currency

Bitcoin and to determine its specific asset characteristics and overall position in the universe of financial

assets.

II. Research Objectives

To conduct an extensive and exhaustive review of all the available literature and material on the

subject.

To contribute to the limited literature on the topic and attempt to answer some of the questions

arising from previous publications.

To undertake a comprehensive analysis of the fundamental economic qualities of the currency, its

origins, growth prospects and sustainability.

To perform a detailed econometric analysis of all existing data on exchange rates and volumes

traded so as to determine bitcoins asset characteristics.

To estimate bitcoin’s correlations with other financial assets so as to determine its asset

characteristics and how it fits into the universe of financial assets.

III. Possible Research Questions

What is bitcoin and how does it work? What are the economic fundamentals of bitcoin as a

currency? As an asset?

What are the general conclusions regarding bitcoin in the literature?

What scope for growth is there in bitcoins development as a currency or asset?

What conclusions can be drawn from an econometric analysis of existing data?

What is the correlation between bitcoin and other financial assets?

Bitcoin has been described as “quasi-commodity money” (Selgin, 2012). In light of this statement

what characteristics does bitcoin share with commodities such as precious metals, oil, etc.?

Is bitcoin a hedge? Is it uncorrelated or negatively correlated with other assets on average?

Is bitcoin a safe-haven? Is it “uncorrelated with [other assets] in a market crash”? (Baur et Lucey,

2010)

c

2. Rationale & Contribution

I. Description of Topic

Bitcoin is the first example of a decentralized virtual currency. It is decentralized as coins are not issued

by any one central organisation but by bitcoin “miners”. To mine for bitcoins users run a program on

their computer which uses an algorithm to solve complex mathematical problems with the help of other

computers on the network. When a puzzle is solved bitcoins are created, this process is also how

transactions are verified and forgery is avoided as each individual “coin” contains a full record of all

previous transactions. Bitcoin provides an alternative to traditional online payment systems as it does

away with the need for any third party with users able to transfer money directly between their online

“wallets”. An ECB report published in 2012 provides this working definition of a virtual currency:

“…a type of unregulated, digital money, which is issued and usually controlled by its developers, and used

and accepted among the members of a specific virtual community.”

The identity of bitcoins creator(s) remains unknown to this day as the infamous “white paper” which

outlined the concepts and design behind the currency was published under the pseudonym of Satoshi

Nakamoto. Attempts to uncover the true identity behind the creation of the currency have proved

fruitless, although an investigative report by The New Yorker (2011) determined that the most likely

candidate was a former cryptology and computer science PhD student from Trinity College, Dublin.

Bitcoin can be exchanged for all major world currencies on a number of online exchanges (with Mt.Gox

the most popular). Its exchange rate is determined by supply and demand in the market. Decentralisation

as a key feature of bitcoin’s design is seen as a way to mitigate against the risks posed by sovereign

currencies such as inflation risk and default risk. However, according to Christin and Moore (2013):

“…an extensive ecosystem of 3rd-party intermediaries now supports Bitcoin transactions: currency

exchanges, escrow services, online wallets, mining pools, investment services, etc.”

meaning that the majority of the

“…risk(s) Bitcoin holders face stems from interacting with these intermediaries, who act as de facto

central authorities.”

These exchanges are often subject to speculative cyber-attacks and to price manipulation. Christin and

Moore found that some 45% of bitcoin exchanges have closed often with the result that customers have

been unable to withdraw their money.


http://www.newyorker.com/reporting/2011/10/10/111010fa_fact_davis

https://mtgox.com/

d

Since its inception in 2010, the value of one bitcoin (BTC) has been subject to extremely high volatility

with a year-to-date closing high of $230 (09/04/2013) and a low of $13.28 (on 01/01/2013). Using

current26 prices, the total “market cap” of all bitcoins in existence is 1,044,104,363 USD.

The use of this terminology, as taken from a major bitcoin data publishing website outlines one of the

issues with bitcoin’s status as a currency. Users view the bitcoins they hold less as money and more as an

ETF on any and all developments in the whole bitcoin space. This is made possible by one of the most

important aspects of bitcoin: its constant deflationary nature. The algorithm which determines the rate of

creation of bitcoins is set to half four times over the life of the currency as the total monetary base

reaches certain levels. Eventually the creation of bitcoins will cease at a level of 21,000,000 BTC. It is this

built-in scarcity which has led to bitcoin’s classification as a “quasi-commodity money”. At the same time

though bitcoin can be thought of as the most fiat of monies, with its value derived not even from the

backing of any state or supranational body. Constant deflation is typically a very destructive force in

modern economies; however in the case of bitcoin such deflation is expected and so may not pose such

an issue as users will have anticipated the change.

Source: Grinberg (2012)

II. Rationale for Choice of Topic

To date there has been an absence of extensive academic discourse within the economic and financial

literature about the virtual online currency bitcoin. Aside from a paper published by the ECB in 2012

there has also been no mention of bitcoin, or other virtual currencies, in the publications of central banks

and other financial institutions. This is understandable as the currency may have initially been viewed as a

curiosity which was unlikely to survive long. Bitcoin has experienced two speculative bubbles followed by

crashes. First in June 2011 when it reached a peak of $33 before falling to just $2.51, then more recently

in April 2013 when it reached an all-time high of $266 before falling to $105 in a matter of hours.

26

Source: www.bitcoincharts.com [Accessed: 03/05/2013]


e

USD/BTC Year to Date

Source: BitcoinCharts.com http://bitcoincharts.com/charts/mtgoxUSD#tgCzm1g10zm2g25

In light of the recent developments in the market I feel that it would be beneficial to undertake a rigorous

analysis of the currency and its possible role (or the role of its successor) in global finance. The purpose

of this dissertation is to contribute to the overall economic literature so as to increase understanding

about the subject. I feel that the examination of such a new and interesting concept as bitcoin is therefore

an important addition to the literature.

As is common with the development of new technologies, bitcoin has been the subject of much heated

debate with opinions polarised on both sides and the lack of a balanced view. This paper would therefore

aim to provide such an even handed view of the positive and negative attributes of the currency.

At a more technical level, bitcoin’s design has led to its description as a “quasi-commodity currency”. As

of this year a number of experienced precious metals dealers have launched online platforms where

bitcoin can be exchanged for gold. This is understandable as whilst sharing many of the more attractive

qualities of precious metals (constant deflation) bitcoin avoids the difficulties arising from problems

relating to storage that are associated with gold and other traditional hedge assets. However, whereas gold

has historically been used as a store of value, bitcoin has no history and as such is likely to suffer from

much greater volatility and changes in market sentiment. In the course of my research I aim to conduct a

comprehensive econometric analysis to compare behaviour in bitcoin to that previously observed in

precious metals so as to determine if there is a significant link between them and whether bitcoin can

indeed be included in the class of assets which are generally used for hedging. Bitcoin may have potential

as an alternative investment and so an important step is to estimate its correlations with other asset

classes.

0

50

100

150

200

250

01/01/2013 01/02/2013 01/03/2013 01/04/2013 01/05/2013

http://bitcoincharts.com/charts/mtgoxUSD#tgCzm1g10zm2g25

f

I feel that the outlined reasons provide a clear outline as to why an academic analysis of the virtual

currency is a relevant topic for research.

III. Literature Review

A. Primary Sources

As previously stated as a motivation for research there are limited reputable sources available on the topic

of bitcoin and virtual currencies. The ECB published a paper titled ‘Virtual Currency Schemes’ in

October 2012 which states that the report “…is a first attempt to provide the basis for a discussion on

virtual currency schemes.” The paper provides a number of useful definitions which outline the

differences between different types of virtual currencies and there qualifying characteristics. It focuses on

the possible risks posed by virtual currencies to the global financial system and in particular to the

reputations of central banks, concluding that at the current level of usage there is no significant risk posed

and that virtual currencies do fall within central banks responsibilities. The report also states that should

usage increase significantly then the current assessment could change and that therefore all developments

should be monitored carefully.

Another primary source used will be ‘Bitcoin: A Peer-to-Peer Electronic Cash System’ published under

the name Satoshi Nakamoto in 2009. This paper outlines the mechanisms behind the bitcoin network and

will provide an essential description of how the system works.

Other primary sources include those arising from within the bitcoin community such as those presented

on www.bitcoin.org and at Bitcoin Wiki. Care must be taken in using these sources however as they are

arising directly from the strong online community surrounding bitcoin and are likely to contain a high

degree of bias.

B. Secondary Sources

There have been a handful of papers published on the topic of virtual currencies and bitcoin. ‘Quasi-

Commodity Money’, Selgin (2012) deals with the reform possibilities presented by currencies such as

Bitcoin, and Selgin argues that such currencies have the potential to “supply the foundation for monetary

regimes that does not require oversight by any monetary authority”.

Another source is the Deutschebank report ‘E-money, Niche Market that Might be Expanding” (2012)

which argues that the potential market for online currencies is huge given the ubiquitous nature of the

internet.

There have also been a number of articles featured in both the print and online blog sections of The

Economist dealing with bitcoin. Articles have also appeared in various other online news publications

such as Bloomberg, Reuters and BBC News.

g

Bitcoin Magazine is dedicated entirely to bitcoin and is regularly the source of the most up to date

information about developments relating to the cryptocurrency. There are also numerous blog posts and

opinion pieces on the subject appearing in places such as www.reddit.com and others. Again it is

important to mention that care must be taken when handling sources of this nature to screen for biases.

3. Methodology

I. Research Methodology

The first step in researching the topic will be to perform a comprehensive review of all the

available information on the topic, taking care to screen for any bias present. Sources used will

fall into one of the categories mentioned above.

The second step will be to undertake an in-depth econometric analysis of the various time series

available with particular attention paid to exchange rates, volume and volatility.

o Possible econometric models to be used include ARIMA and suggested ARCH models.

o Data is available online from:

www.bitcoincharts.com

www.blockchain.info

The objective of both the literature review and econometric testing will be to determine the

fundamental economic qualities of the currency, its origins, growth prospects and sustainability.

II. Data Analysis

Preliminary estimation of correlations between bitcoin and other assets was done on data consisting of

daily observations and covering the period between 20 July 2010 and 1 March 2013. Variables were

included to represent stock, bond, gold and US dollar markets. All the variables were drawn from the US,

as for bitcoin the most data is available for the dollar-bitcoin exchange rate. To represent stocks I used

the S&P 500 index and for bonds I used the Federal Reserve Bank’s ten year treasury constant maturity

rate (yield). For gold I used the spot price in US dollars and to represent the dollar I used the Federal

Reserve Bank’s trade weighted US dollar index. My dollar-bitcoin exchange data is drawn from the

longest running bitcoin exchange Mt.Gox.

Following on from studies done by Capie, Mills and Wood (2005), Baur and Lucey (2010) and Ciner,

Gurdgiev and Lucey (2010; revised 2012) I attempted to establish whether bitcoin can be classified as a

hedge against the above mentioned financial assets. Consistent with the previously mentioned papers I

estimated an IGARCH (1, 1) model of the form:

With variance equation of the form:


http://www.blockchain.info/

h

The results for the model are presented in the following table.

Table 1 – Estimation Results for IGARCH (1, 1)

Bitcoin Coefficient est. Standard error t-stat P-value

0.007017 0.001661 4.225126 0.0000

0.855431 0.213234 4.011703 0.0001

-0.221590 0.067989 -3.259208 0.0011

-0.536879 0.504120 -1.064981 0.2869

Variance Equation

0.064865 0.002276 28.50330 0.0000

0.935135 0.002276 410.9250 0.0000

The model was selected using the Akaike information criterion and the log likelihood ratio test.

From the table we can see that bitcoin is positively correlated with stocks on average. We can also see that

bitcoin is negatively correlated with bond yields (i.e. positively correlated with bond prices) and negatively

correlated with the US dollar. However, the coefficient estimate for the average effect of the US dollar on

bitcoin is not statistically significant. The model has a very low r-squared of just 0.0075, implying that less

than 1% of the variation in bitcoin is as a result of the variation in the explanatory variables selected.

Lagged variables of both the dependent and explanatory variables were estimated, however none were

statistically significant.

I intend to expand my analysis to include more variables such as the Dow Jones, Russell 2000, Eurostoxx

50 and FTSE 100 for stocks and indices representing the relative value of both the euro and yen. I also

intend to follow the previously mentioned works and estimate whether bitcoin is a “safe-haven”. I will

estimate rolling 30 day correlations between bitcoin and the other assets in order to generate additional

data series.

i

III. Indicative Timetable

Task Start date Duration (days) End Date

Literature Review 01/02/2013 149 30/06/2013

Data Gathering 01/03/2013 90 31/05/2013 Start: 01/02/2013

Econometric Analysis 01/05/2013 30 31/05/2013 End: 09/08/2013

Interpretation of Results 01/06/2013 29 30/06/2013

Final Write-up 01/07/2013 38 09/08/2013

j

4. References

Babbage (2011), ‘The Bursting of the Bitcoin Bubble’, The Economist. Available from:

http://www.economist.com/blogs/babbage/2011/10/virtual-currencies [Accessed: 28/02/2013]

Babbage (2011), ‘Virtual Currency: Bits and Bobs’, The Economist, Available from:

http://www.economist.com/blogs/babbage/2011/06/virtual-currency [Accessed: 21/02/2013]

Baddeley, M. (2004), ‘Using E-Cash in the New Economy: An Economic Analysis of Micropayment

Systems’, Journal of Electronic Commerce Research, Vol. 5, No. 4. Available from:

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.5075&rep=rep1&type=pdf [Accessed:

28/02/2013]

Baur, D., and Lucey, B. (2010), ‘Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold.’

Financial Review 45.2: 217-229. Available from: http://onlinelibrary.wiley.com/doi/10.1111/j.1540-

6288.2010.00244.x/full [Accessed: 25/02/2013]

BBC News Online (2011), ‘Bitcoin ‘Will Recover’ from the Crash.’ Available from:

http://www.bbc.co.uk/news/technology-13857192 [Accessed: 30/01/2013]

BBC News Online (2012), ‘Virtual Cash Exchange Becomes Bank’, Available from:

http://www.bbc.co.uk/news/technology-20641465 [Accessed: 28/02/2013].

BBC News Online (2013), ‘Bitcoin Virtual Currency Reaches All-Time High Price’. Available from:

http://www.bbc.co.uk/news/technology-21601608 [Accessed: 28/02/2013]

BIS (2012), ‘Innovations in Retail Payments’. Available from: http://www.bis.org/publ/cpss102.htm

[Accessed: 27/02/2013]

Bitcoin Beta, ‘How do Economists View Bitcoin?’ Available from:

http://bitcoin.stackexchange.com/questions/3747/how-do-economists-view-bitcoin [Accessed:

22/02/2013]

Bitcoin Charts, Available at: http://bitcoincharts.com/ [Accessed: 28/02/2013]

Buhl, T. (2013), ‘Coinlab Bringing Bitcoin to Wall Street with MtGox Deal’, Bitcoin Magazine. Available

from: http://bitcoinmagazine.com/coinlab-bringing-bitcoin-to-wall-street-with-mtgox-deal [Accessed:

27/02/2013]

Buterin, V. (2012), ‘Where is Bitcoin? A Look at Some Analytical Data’, Bitcoin Magazine. Available from:

http://bitcoinmagazine.com/where-is-bitcoin-a-look-at-some-analytical-data/ [Accessed: 28/02/2013]

http://www.economist.com/blogs/babbage/2011/10/virtual-currencies

http://www.economist.com/blogs/babbage/2011/06/virtual-currency

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.92.5075&rep=rep1&type=pdf



http://www.bbc.co.uk/news/technology-13857192



http://www.bis.org/publ/cpss102.htm

http://bitcoin.stackexchange.com/questions/3747/how-do-economists-view-bitcoin

http://bitcoincharts.com/

http://bitcoinmagazine.com/coinlab-bringing-bitcoin-to-wall-street-with-mtgox-deal

http://bitcoinmagazine.com/where-is-bitcoin-a-look-at-some-analytical-data/

k

Buterin, V. (2013), ‘$13 to $26: Exploring the Bitcoin Rally’, Bitcoin Magazine. Available from:

http://bitcoinmagazine.com/13-to-26-exploring-the-bitcoin-rally/ [Accessed: 14/02/2013]

Button, K. (2011), ‘Virtual Currencies, Real Potential’. American Banker, Available from:

http://www.americanbanker.com/btn/24_11/virtual-currencies-real-potential-1043673-

1.html?zkPrintable=1&nopagination=1 [Accessed: 28/02/2013]

Ciner, C., Gurdgiev, C., Lucey, B. (2010; Revised 2012), ‘Hedges and Safe Havens–An Examination of

Stocks, Bonds, Oil, Gold and the Dollar.’ Bonds, Oil, Gold and the Dollar. Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1679243 [Accessed: 25/02/2013]

Cohen, B. (2001), ‘Electronic Money: New Day or False Dawn?’, Review of International Political Economy,

Vol. 8, No. 2 (Summer, 2001), pp. 197-225 Available from: http://www.jstor.org/stable/4177382

[Accessed: 29/01/2013]

Davis, J. (2011), ‘The Crypto-Currency’, The New Yorker. Available from:

http://www.newyorker.com/reporting/2011/10/10/111010fa_fact_davis [Accessed: 28/02/2013]

Dept. of Finance, Ireland (2009), ‘Electronic Money Directive’. Available from:

http://www.finance.gov.ie/documents/publications/other/2010/EMDConsultation.pdf [Accessed:

28/02/2013]

Deutsche Bank Research (2012), ‘E-money – Niche Market that Might be Expanding’, Available from:

http://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000288496/E-

money%3A+Niche+market+that+might+be+expanding.PDF [Accessed: 26/02/2013]

Digital Journal (2013), ‘Bitcoin to Precious Metals Platform has Launched.’ Available from:

http://www.digitaljournal.com/pr/1094280 [Accessed: 26/02/2013]

EC (2009), ‘Electronic Money Directive’, Directive 2009/110/EC, Available from: http://eur-

lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:267:0007:0017:EN:PDF [Accessed:

28/02/2013]

ECB (2012), ‘Virtual Currency Schemes’. Available from:

http://www.ecb.int/pub/pdf/other/virtualcurrencyschemes201210en.pdf [Accessed: 13/01/2013]

Gallippi, T. (2013), ‘How Owning Bitcoin is Owning an ETF on the Whole Bitcoin Space’, Pymnts.com.

Available from: http://www.pymnts.com/briefing-room/PYMNTS-International/2013/02/How-

Owning-Bitcoin-Is-Owning-An-ETF-On-The-Whole-Bitcoin-Space/?utm_campaign=how-owning-

bitcoin-is-owning-an-etf-on-the-whole-bitcoin-space [Accessed: 23/02/2013]

http://bitcoinmagazine.com/13-to-26-exploring-the-bitcoin-rally/

http://www.americanbanker.com/btn/24_11/virtual-currencies-real-potential-1043673-1.html?zkPrintable=1&nopagination=1

http://www.americanbanker.com/btn/24_11/virtual-currencies-real-potential-1043673-1.html?zkPrintable=1&nopagination=1


http://www.jstor.org/stable/4177382

http://www.newyorker.com/reporting/2011/10/10/111010fa_fact_davis

http://www.finance.gov.ie/documents/publications/other/2010/EMDConsultation.pdf

http://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000288496/E-money%3A+Niche+market+that+might+be+expanding.PDF

http://www.dbresearch.com/PROD/DBR_INTERNET_EN-PROD/PROD0000000000288496/E-money%3A+Niche+market+that+might+be+expanding.PDF

http://www.digitaljournal.com/pr/1094280

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:267:0007:0017:EN:PDF

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:267:0007:0017:EN:PDF


http://www.pymnts.com/briefing-room/PYMNTS-International/2013/02/How-Owning-Bitcoin-Is-Owning-An-ETF-On-The-Whole-Bitcoin-Space/?utm_campaign=how-owning-bitcoin-is-owning-an-etf-on-the-whole-bitcoin-space



l

Grinberg, R. (2011), ‘Bitcoin: An Innovative Alternative Digital Currency’, Hastings Sci. & Tech. LJ 4: 159.

Available from:

http://heinonline.org/HOL/Page?handle=hein.journals/hascietlj4&div=6&g_sent=1&collection=journ

als [Accessed: 28/02/2013]

http://bitcoin.org/ [Accessed: 19/01/2013]

http://blockchain.info/ [Accessed: 28/02/2013]

Jacobs, E. (2011) "Bitcoin: A Bit Too Far." Journal of Internet Banking and Commerce, Vol 12.Available from:

http://www.arraydev.com/commerce/jibc/2011-

08/20110704%20JIBC%20Edwin%20JACOBS%20BITCOIN.pdf [Accessed: 28/02/2013]

Kaminska, I. (2013), ‘Turning Mobile Money Into M0’ FT Alphaville. Available from:

http://ftalphaville.ft.com/2013/02/18/1389822/turning-mobile-money-into-m0/ [Accessed:

28/02/2013]

Kaminska, I. (2013), ‘Why Central Banks Should Tank Charge of Their Digital Currencies’ FT Alphaville.

Available from: http://ftalphaville.ft.com/2013/02/18/1383972/why-central-banks-should-take-charge-

of-their-digital-currencies/ [Accessed: 28/02/2013]

Kaplanov, N. (2012), ‘Nerdy Money: Bitcoin, the Private Digital Currency, and the Case Against its

Regulation’, Temple University Legal Studies Research Paper, Available from:


Krugman, P. (2011), ‘Golden Cyberfetters’, The Conscience of a Liberal, New York Times Blog. Available from:

http://krugman.blogs.nytimes.com/2011/09/07/golden-cyberfetters/ [Accessed: 29/01/2013]

Lee, T. (2011), ‘The Bitcoin Crash’, Forbes. Available from:

http://www.forbes.com/sites/timothylee/2011/08/07/the-bitcoin-crash/ [Accessed: 22/02/2013]

Lee, T. (2013), ‘Why I’m Not Ready to Sell My Bitcoins’, Forbes. Available from:

http://www.forbes.com/sites/timothylee/2013/02/14/why-i-dont-think-bitcoins-big-price-rise-is-

another-bubble/ [Accessed: 14/02/2013]

Lucey, B. (2011), ‘What do academics think they know about gold.’ The Alchemist 62: 12-14. Available

from: http://www.lbma.org.uk/assets/Alch6203Lucey.pdf [Accessed: 25/02/2013]

Mt.Gox.com, Available at: https://mtgox.com/ [Accessed: 28/02/2013]

Nakamoto, S. (2008), ‘Bitcoin: A Peer-to-Peer Electronic Cash System’. Available from:

http://bitcoin.org/bitcoin.pdf [Accessed: 25/02/2013]



http://bitcoin.org/

http://blockchain.info/

http://www.arraydev.com/commerce/jibc/2011-08/20110704%20JIBC%20Edwin%20JACOBS%20BITCOIN.pdf

http://www.arraydev.com/commerce/jibc/2011-08/20110704%20JIBC%20Edwin%20JACOBS%20BITCOIN.pdf

http://ftalphaville.ft.com/2013/02/18/1389822/turning-mobile-money-into-m0/

http://ftalphaville.ft.com/2013/02/18/1383972/why-central-banks-should-take-charge-of-their-digital-currencies/

http://ftalphaville.ft.com/2013/02/18/1383972/why-central-banks-should-take-charge-of-their-digital-currencies/


http://krugman.blogs.nytimes.com/2011/09/07/golden-cyberfetters/

http://www.forbes.com/sites/timothylee/2011/08/07/the-bitcoin-crash/

http://www.forbes.com/sites/timothylee/2013/02/14/why-i-dont-think-bitcoins-big-price-rise-is-another-bubble/

http://www.forbes.com/sites/timothylee/2013/02/14/why-i-dont-think-bitcoins-big-price-rise-is-another-bubble/


https://mtgox.com/


m

Raskin, M. (2013), ‘Bitcoin’s Gains May Fuel Central Banks Concerns’, Bloomberg.com, Available from:

http://www.bloomberg.com/news/2013-01-28/bitcoin-s-gains-may-fuel-central-bank-concerns-chart-of-

the-day.html [Accessed: 19/01/2013]

Ron, D. and Shamir, A. (2012), ‘Quantitative Analysis of the Full Bitcoin Transaction Graph.’ Available

from: http://ceclub.technion.ac.il/past.html [Accessed: 28/02/2013]

Roth, D. (2010), ‘The Future of Money: It’s Flexible, Frictionless and (Almost) Free. Wired Magazine.

Available from:

http://innovbfa.viabloga.com/files/Wired___The_Future_of_Money___march_2010.pdf [Accessed:

28/02/2013]

RT.com (2012), ‘Global Banking to be Joined by ‘Google Bank’ and ‘Apple Bank’. Available from:

http://rt.com/business/google-apple-banking-acquisition-795/ [Accessed: 28/02/2013]

Selgin, G. (2012), ‘Quasi-Commodity Money.’ Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract-id=2000118 [Accessed: 25/02/2013]

Surowiecki, J. (2011), ‘Cryptocurrency’, MIT Technology Review. Available from:

http://www.technologyreview.com/review/425142/cryptocurrency/ [Accessed: 29/01/2013]

The Economist (2012), ‘Bitcoin: Monetarists Anonymous.’ Available from:

http://www.economist.com/node/21563752 [Accessed: 21/02/2013]

Turnbull, S. (2013), ‘How Should Regulators Control E-Money?’, 2nd International Conference on

Complementary Currencies Systems, The Hague, the Netherlands. Available from:


Wallace, B. (2011), ‘The Rise and Fall of Bitcoin’, Wired Magazine. Available from:

http://www.wired.com/magazine/2011/11/mf_bitcoin/ [Accessed: 28/02/2013]

WordPress.com (2012), ‘Pay Another Way: Bitcoin’, Available from:

http://en.blog.wordpress.com/2012/11/15/pay-another-way-bitcoin/ [Accessed: 28/02/2013]

Zerohedge.com (2012), ‘Bitcoin Seen Through the Eyes of a Central Banker’. Available from:

http://www.zerohedge.com/news/2012-11-04/bitcoin-seen-through-eyes-central-banker [Accessed:

28/02/2013]

http://www.bloomberg.com/news/2013-01-28/bitcoin-s-gains-may-fuel-central-bank-concerns-chart-of-the-day.html

http://www.bloomberg.com/news/2013-01-28/bitcoin-s-gains-may-fuel-central-bank-concerns-chart-of-the-day.html

http://ceclub.technion.ac.il/past.html

http://innovbfa.viabloga.com/files/Wired___The_Future_of_Money___march_2010.pdf

http://rt.com/business/google-apple-banking-acquisition-795/


http://www.technologyreview.com/review/425142/cryptocurrency/

http://www.economist.com/node/21563752


http://www.wired.com/magazine/2011/11/mf_bitcoin/

http://en.blog.wordpress.com/2012/11/15/pay-another-way-bitcoin/

http://www.zerohedge.com/news/2012-11-04/bitcoin-seen-through-eyes-central-banker