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Marex Spectron Institute | Jump in Commodity Markets

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Background

Our research paper “Jumps in Commodity

Markets” has recently been published in

the Journal of Commodity Markets. Here,

we provide a non-technical, executive

summary, reprinting some parts of the full

manuscript. For the full publication, please

refer to Nguyen & Prokopczuk (2019).

Motivation

Large price movement, i.e. jumps, are of

obvious concern for market participants.

In commodity markets, there are many

potential reasons why prices could jump.

For example, energy prices react heavily

to geopolitical events in the Middle East

(as witnessed when Iraq invaded Kuwait)

or Hurricanes in the Gulf of Mexico

(such as Katrina in 2005). The prices for

agricultural commodities might react

heavily to adverse weather conditions,

political announcements, or surprises

in inventory announcements. Likewise,

metal futures prices might react heavily

to supply disruptions or international

trade disputes. Consideration of jumps

is therefore of great importance when

relying on cross-sectional and cross-

market diversification for risk control.

From a portfolio perspective, it is relevant

whether jumps are highly correlated in the

cross section and across markets or not,

since high correlation would eliminate

diversification benefits. The knowledge

about specific commodities which show

high/low jump correlation thus allows for

a better portfolio allocation in times of

market stress.

We analyze the occurrence of jumps in

a wide variety of commodity markets.

A particular emphasis is placed on the

question whether prices in different

markets move heavily at the same point

in time, i.e. whether they co-jump.

In this guest academic paper, Professor Marcel Prokopczuk and Duc Binh Benno Nguyen

summarise a broader paper than they published earlier this year that analyses large price

movements, or jumps, in commodity markets.


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Findings on Individual Markets

Figure 1 shows the percentage of months

under study in which we identify jumps for the

individual commodities. The commodities

butter and milk exhibit by far the most jumps,

where 20.5% and 7.2% of the months include

jumps. Both futures were introduced rather

recently, so the sample period is relatively

short and the markets are young. The precious

metals gold and silver show also relatively

many jumps with a percentage of 2.6% and

1.9%, respectively. Soybean meal and live

cattle have the least jumps with a proportion

of only 0.1% and 0.2%, respectively.

Table 1 provides more details on the

frequency of jumps and their magnitudes on

a daily basis, distinguishing between positive

and negative jumps. It shows that jumps are

rare and extreme events. For example, only

4% of the daily returns observed in the crude

oil market are classified as (negative) jumps,

while the average absolute magnitude of

these jump returns is very large (-8.64%).

We also analyzed whether the occurrence

of jumps is related to market liquidity and

found that, indeed, lower market liquidity

(calculated as the Amihud (2002) ratio) is

associated with a higher probability of jumps

occurring.

Findings on Co-Jumps

We analyze the likelihood of two commodity

markets jumping at the same time. We

find that the pairwise correlation of jump

occurrence is close to zero and thus much

lower than the average correlation of normal

returns across markets, which is 0.13. Only

about 12 % of the commodity market pairs

exhibit co-jumps at all.

Table 2 presents those pairs of commodities,

for which we identify the highest frequencies

of co-jumping. As is immediately evident,

the energy sector shows high co-movement

of jumps, filling five of the first six rows. Other

pairs with significant jump correlations are

inter alia soybean commodities (soybeans,

soybean meal and soybean oil) and

precious metals (gold, platinum and silver).

Table 3 repeats the above analysis at the

sector level. The correlation of jumps within

certain individual sectors is much higher than


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the average across all commodities (0.03).

The energy, metals and grains sector show

relatively high correlations between 0.07

and 0.25 with 20.00% to remarkably 53.33%

of the pairs showing significant correlations,

while meats and softs commodities show

no jump co-movements within the sectors.

Intuitively, the correlation of jumps across

sectors are rather low, with values between

0.00 and 0.03. Return correlations are much

higher for

both pairs within and across sectors. All pairs

within the metals and grains sector show

significant return correlations with means

of 0.51 and 0.43, respectively. The highest

return correlations are found between the

metals and energy or grains sectors.

We also analyze the jump relationship of

commodity markets with other major asset

classes. Correlation of commodity returns

and stock market returns vary from -0.04 to

0.44, while jumps are mostly uncorrelated

(or sometimes negatively correlated).

Commodity returns and U.S. Dollar returns

are mostly negatively correlated while jumps

are uncorrelated or negatively correlated.

Commodities generally hedge against

Treasury note returns and jumps, even

though there are several exceptions.

Conclusion

To have a diversified cross-market portfolio

allocation, it is important to know to which

extent the jumps of commodities, stocks

and other assets are correlated. If they are

uncorrelated across markets one can protect

oneself against sharp price movements

through diversification. We find that while

jumps in commodity futures occur much

less frequently than in stock markets, some

commodities exhibit relatively many jumps.

The co-jump analysis reveals that commodities

do not tend to jump simultaneously, which is

good news for investors who are concerned

with extreme price movements of diversified

commodity futures portfolios, since it implies

that jump risk can be diversified. Relating

commodity jumps to other assets, we find

that jumps in the stock, currency and bond

markets are generally diversifiable by adding

commodities to the basket.


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Figure 1: Percentage Months with Significant Jumps

This figure presents the percentage of months that contain jumps which are significant

at the 5% level. Jumps are measured by the test statistic of Barndorff-Nielsen & Shephard

(2006) for the 29 commodities using daily observations within each calender month.


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Table 1: Summary Statistics of Daily Returns and Jumps

This table presents the summary statistics of daily returns (in percent). We report the number

of daily observations N, the average daily return, Mean, the average positive (negative) jump

returns, Pos. Jumps (Neg. Jumps) and their proportion.


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Table 2: Pairs of Commodities with Largest Jump Correlation

This table presents the commodity pairs with the highest jump correlation. Jumps are

measured by the test statistic of Barndorff-Nielsen & Shephard (2006). The pairs of

commodities listed here exhibit jump measure correlations with t-statistics higher than

3.0. The last column reports the related correlation coefficient.


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Table 3: Correlation of Jump Measure across Commodity Sectors

This table presents the summary statistics of the correlation of jumps measured by the test

statistic of Barndorff-Nielsen & Shephard (2006) in Panel A and of raw returns in Panel B. There

are 29 commodities divided into 5 sectors. The statistics below are computed within and across

sectors. We report the average time-series correlation coefficient and the percentage of all

correlation coefficients for which the t-statistic is higher than 2.0 in square brackets below.


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References

Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5 (1), 31-56.

Barndorff-Nielsen, O. E., & Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of Financial Econometrics, 4 (1),1-30.

Nguyen, D., & Prokopczuk, M. (2019). Jumps in commodity markets. Journal of Commodity Markets, 13 , 55-70.

Authors

Professor Marcel Prokopczuk

Marcel is a Professor of Finance at Leibniz University Hannover and Visiting Professor of Finance at the ICMA Centre, Henley Business School. He holds a PhD in Finance from the University of Mannheim, Germany and previously graduated from the University of Karlsruhe, Germany with a MSc in Business Engineering. He is a CFA charterholder and holder of the Professional Risk Manager (PRM) designation. Marcel’s main research interests are commodity markets, derivatives, and risk management.

Duc Binh Benno Nguyen

PhD at Leibniz University Hannover - Faculty of Economics and Management. Currently with Mercedes-Benz Bank.

Marex Spectron Institute

Marex Spectron’s educational arm capitalises on the Group’s lead research capabilities and strong ties with the academic world. The Institute offers individuals, and groups, high quality training into particular commodity markets, looking at market structures, challenges and trends, as well as offering insight into the idiosyncratic nature of these markets, and discussing trading and analytics.

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This document has been prepared for information purposes only and is not intended (and should not be considered) to be legal advice on any subject matter.

The views expressed are the views of Duc Binh Benno Nguyen and Professor Marcel Propopczuk at the time of publication. This document may contain forward looking statements, including statements regarding Marex Spectron’s or the authors’ intent, belief of current expectations with respect to market conditions, results of operation and financial condition, capital adequacy, specific provisions and risk management practices, which readers are cautioned not to place undue reliance on as they may be subject to change without notice. Marex Spectron does not give any representation or warranty, whether express or implied, as to the accuracy, completeness, currency or fitness for any purpose or use of any information in this document or as to whether this document reflects the current legislative or regulatory position or any other relevant developments. Marex Spectron assumes no responsibility to update this document based on events after its publication.

Readers are responsible for assessing the relevance and accuracy of the content of this document. Marex Spectron will not be liable for any loss, damage, cost or expense incurred or arising by reason of any person using or relying on information in this document.

Information in this document should not be considered as advice, or as a recommendation or solicitation to purchase or otherwise deal in securities, investments or any other products. It has been prepared for institutional clients, is not directed at retail customers and does not take into account particular investment objectives, risk appetites, financial situations or needs. Recipients of the document should make their own trading or investment decisions based upon their own financial objectives and financial resources.

The information in this presentation is current as at the date of publication. While reasonable care has been taken to ensure that the facts stated are fair, clear and not misleading, Marex Spectron does not warrant or represent (expressly or impliedly) their accuracy or completeness. Any opinions expressed may be subject to change without notice. Marex Spectron accepts no liability whatsoever for any direct, indirect or consequential loss or damage arising out of the use of all or any of the data or information in this presentation.

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guest paper jumps in commodity markets · commodity futures portfolios, since it implies that jump...

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