2013

Borsa İstanbul Finance & Economics Conference (BIFEC) 2013

“Policy Issues and Challenges in the Global Financial System and Economies”

www.bifec.com

September 30 – October 1, 2013İstanbul

Book of Abstracts

Borsa İstanbul Finance & Economics Conference (BIFEC) 2013

“Policy Issues and Challenges in the Global Financial System and Economies”

September 30 – October 1, 2013

www.bifec.com

ISBN: 978-605-5076-04-7

BIFEC Book of Abstracts & Proceedings

Volume I Issue II

(Proceedings)

March 2014, İstanbul

The views expressed in this book are those of the author(s) and do not necessarily represent the official views of Borsa İstanbul or its members. The authors of the individual papers are responsible for

technical, content, and linguistic correctness. The refereeing process is managed by the Research and Business Development Department of Borsa İstanbul.

This e-book is published by Borsa İstanbul

Copyright © 2014

i BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Contents (Volume I Issue II) - Proceedings

Formal and Informal Regulations for Credit Card Payment Services ................................ 1

Güzin Gülsün AKIN & Ahmet Faruk AYSAN & Gültekin GÖLLÜ & Levent YILDIRAN

Assembling International Equity Datasets – Review of Studies on the Cross-Section of Common Stocks .......................................................................................................... 34

Antonina WASZCZUK

Trading Puzzle, Puzzling Trade .......................................................................................... 66

Orhan ERDEM & Evren ARIK & Serkan YÜKSEL

The effect of investors' confidence on monetary policy- economic growth relationship: a Multivariate GARCH approach................................................................................. 82

Chiara GUERELLO

Agency and Transparency in Financial Markets ............................................................... 110

Sadettin Haluk ÇİTÇİ

Inflation Dynamics and Business Cycles ........................................................................... 121

Süleyman Hilmi KAL & Nuran ARSLANER & Ferhat ARSLANER

Comovement and Polarization of Interest Rate and Stock Market in Turkey ................130

Ahmet DURAN & Burhaneddin İZGİ

The Effect of Global Shocks and Volatility on Herd Behavior in Borsa Istanbul ............ 142

Mehmet BALCILAR & Rıza DEMIRER

Volatility and Transparency of Financial Markets in the MENA Region ......................... 173

Hamid MOHTADI & Stefan RUEDIGER

Hedging Strategy for Electricity Market Price Volatility: The Case of Turkish Electricity Market ...................................................................................................... 196

Sezer Bozkuş KAHYAOĞLU & M. Vedat PAZARLIOĞLU

The Impact of Financial Innovation on Firm Stability ..................................................... 211

Fabian Kuehnhausen

Index of Authors ............................................................................................................... 240

121 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Süleyman Hilmi KAL; Central Bank of the Republic of Turkey

Nuran ARSLANER; Central Bank of the Republic of Turkey

Ferhat ARSLANER; Borsa İstanbul

The paper aims to investigate whether the effect of the backward-looking inflation expectations,

nominal effective exchange rate, money supply, gross domestic product and import prices on

inflation depends on business cycle. For this purpose, a two states Markov Switching Auto

Regression model with time varying transition probabilities to a generic inflation model is

implemented for the period 2003-2013. In this model the states are assigned whether output gap is

positive or negative. The inflation forecasting in-sample and out-of-sample is also utilized by

adopting mean squared error and Diebold Mariano test to measure explanatory and forecasting

power of our model. Our main finding provides that the determinants of inflation have different

dynamics during boom periods as compared to recessions.

Keywords: Inflation; Output Gap; Exchange Rate Pass-Through; Markov Switching

Autoregressions; Business Cycles

JEL: C32, E30, E31, E37, E58

1. Motivation

After 2007-2008 financial crises and ensuing the Great Recession, despite the fact that money

supply increased substantially by the central banks especially in the United States, in the

European Union, and in the other countries, the inflationary pressures still nonexistent; instead

there are concerns of deflation emerged in these regions. This anomaly leads us to make the

following argument that there may be some other variables influence the relationship between

inflation and the factors that determine the inflation such as money supply, exchange rate pass

trough and output. In economic literature, these variables are usually named as state variables. In

this paper, we will attempt to evaluate whether the output gap as one of the possible factors

determining the different dynamics, which take important role in explaining inflation during

various phases of the economy, i.e. the business cycles.

Inflation Dynamics and Business Cycles

122 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Figure 1. The Keynesian View

Our motivation comes from the Keynesian view that when the economy is below its potential (YFE

in Figure 1) , a shift in aggregate demand or in money supply may not have an important impact

on price changes. So, basically, this is the theoretical framework and the motivation behind our

work.

2. Short Literature Review

Unfortunately, there is not much literature about the effects of business cycle on inflationary

factors even though there is considerable litreture on exchange rate pass-through. Joey Chew et al

(2011) find for Singaporean economy that there is asymmetric exchange rate pass through. They

documented that the exchange rate pass-through occurs more during recessionary periods than it

occurs during expansionary periods.

Nidhaleddine (2012) detected that there is nonlinear exchange rate pass through for 12 EU

Countries. Higher exchange rate pass-through is detected when inflation exceeds a certain

threshold.

Oinonen et al (2013)’s study provided evidence that economic dynamics of inflation US has

changed and output gap became more important in US since 1990.

In the following graphs (Figure 2, 3 and 4), bar graphs are output gap, red line is the rate of

inflation and blue line is broad definition of money supply which is M2, import price index and

manufacturing industrial production index. One can see from the figures that, there is no strong

123 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

linear relationship between inflation and money supply. Similarly, there is not any linear pattern

between inflation and import prices and between inflation and industrial production index. We

analyze the relationships among those variables using Markov switching regression method.

124 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

3. Methodology

Markov switching regression model was first used pretty early in 1953 by Quant and Goldfeld and

became popular by Hamilton’s work in 1990s

Markov switching regime models are basically space models. The relationship between two

variables depends on observed state variable. The unobserved state variable follows a Markov

chain.

Markov regime switching model assume that the observed changes in a variable between two-

consecutive periods are a random draw from two distributions. The unobserved state variable

evolves according to Markov chain. Probabilities of switching from one state to another -

transition probability- is not exogenous but endogenous. Basically we use two normal

distributions each of which has a different mean and standard deviation in this article.

State variables (St) determine the distribution for the period. In a two-state case, when St =1

the observed changes are a random draw from: yt /st= 1~ N ( μ1 ,σ21)

When St=2 from: yt /st= 2~ N ( μ2 ,σ

22)

125 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Mean and the variance of the yt depend on the state. Density of yt is conditional on St and is as

follows:

4. Model

We use a generic inflation model (Goldberg and Knetter, 1997) by running monthly data for the

period 200369 and 2014 which is as follows:

inft = α1infet + α2Δmst+ α3Δprodt + α4Δneert + α5Δimpt + εt

where

inft : The rate of inflation in consumer prices,

infet : Backward looking inflation expectation

Δms : Change in money supply,

Δprod : Change in industrial production index,

Δneer : Change in nominal effective exchange rate ,

Δimp : Change in import price index,

εt : Error Term.

If output gap is used as state variables,

State 1: When output gap is positive.

State 2: When output gap is negative

69

2003 is especially picked for the beginning of the analysis period because it is the start of inflation targeting regime in Turkey.

)2

)y(exp(

2

1)sy(

2

st-ttt

st

if

126 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

5. Results

Markov regime switching regression has two sets of coefficients; one for each state. In other

words coefficients for the recessionary periods (negative output gap) are different than the

coefficients for the expansionary periods (positive output gap).

According to the results, when the output gap is negative which is State 1, a change in money

supply interestingly reduces the rate of inflation as opposed to the expectations. Moreover, an

increase in manufacturing industrial production increases the inflation. They are statistically

highly significant. However, regarding to nominal effective exchange rate and import price index,

they are not statistically significant. This is just we found the opposite of Singaporean economy.

During the recessionary times, we do not see exchange rate pass through for the Turkish economy

when the output gap is negative whereas the powerful pass-through exists when the output gap is

positive that is to say, during the boom times, expansionary periods. Money supply and industrial

production are still statistically insignificant, yet if you look at the pass-through coefficient,

nominal effective exchange rate and import prices are highly significant. Their total pass-through

effect is 0.07.

Table 1. Coefficients Estimated by Two-State Markov Switching Process

States Variables Coefficients t-ratios

Negative INFt-1 α11 1.0535 31.1791***

Output ΔM2 α12 -0.0307 -2.4326***

Gap ΔPROD α13 0.1019 3.4213***

(State 1) ΔNEER α14 -0.0114 -0.9241

ΔIMP α15 -0.0196 -1.3885*

Positive INFt-1 α21 0.8948 17.6955***

Output ΔM2 α22 0.0212 1.2409

Gap ΔPROD α23 -0.0296 -1.0038

(State 2) ΔNEER α24 0.0268 2.2324***

ΔIMP α25 0.0456 2.6861*** *, ** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

inf2t

= α21

infe

t + α

22Δms

t+ α

23Δprod

t + α

24Δneer

t + α

25Δimp

t + ε

2t

inf1t

= α11

infe

t + α

12Δms

t+ α

13Δprod

t + α

14Δneer

t + α

15Δimp

t + ε

1t

127 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Additionally, Wald test is implemented in order to see whether the coefficients in both states are

statistically different from each other or not. Wald test results are encouraging. According to

these results, all the coefficients are significant. Therefore, in each state for all the variables are

statistically significantly different from each other.

Table 2. Wald Test Results

Variables Test Results

INFt-1 8.0107 ΔM2 7.9264

ΔPROD 15.1797 ΔNEER 4.194 ΔIMP 10.0332

Note: All numbers indicate that a variable is significant at 5% significance level according to the χ²-distribution.

We also did in-sample estimation in order to understand the success of our model. In-sample

estimation mimics the realized inflation. We also used random walk and ordinary least square

estimation of the same model whether the model is success. In terms of mean square errors,

Markov processed model produces significantly lower errors than that of OLS and also the

random walk.

Figure 5. Coefficients Estimated by Two-State Markov Switching Process

Graph 4 shows the transition probabilities of the output gap which we will be derived with the HP

filter, nominal exchange rate and money supply of State 1. In addition to in-sample estimation,

0 50

100 0

0.1

0.2

Realized Inflation

0 50 100 0

0.1

0.2

In-sample Estimation

0 50

100 -20

0

20

Output Gap

0 50 100 -0.5

0

0.5

Nominal Exchange Rate

0 50

100 0

0.5

1

Transition Probability of State 1

0 50 100 -0.5

0

0.5

Money Supply

2003 2005 2007 2009 2011 2013 2003 2005 2007 2009 2011 2013

128 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

out-of-sample estimation is also done. It is seen that the model successfully predicts mimics of

ups and downs the rate of inflation. We used 16 periods as out-of-sample observations (Figure

5&6). The R2 of the model is 80 per cent. We take out the previous inflation from the model is still

around above 30 per cent. Therefore, the model from that point of view seems to be promising.

Figure 6. Realized Inflation and Out-of-Sample Estimation

6. Conclusions

Our main conclusion is that inflation dynamics depends on the business cycles. So, the

determinants of inflation have different dynamics during boom periods as compared to

recessions.

Agenda for future research is that states may be determined whether the expected inflation is

realized or not and reel effective exchange rate is overvalued or undervalued. Cross country

analysis can also be done by using the same model and methodology.

0 2 4 6 8 10 12 14 160.06

0.08

0.1

0.12Gerceklesen Enflasyon

0 2 4 6 8 10 12 14 160.04

0.06

0.08

0.1

0.12Ornekleme Disi Enflasyon Tahmini

Realized Inflation

Out-of-Sample Inflation Estimation

2003 2005 2007 2009 2011 2013

2003 2005 2007 2009 2011 2013

129 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Reference

Goldberg, L., and M. M. Knetter. 1997. “Goods Prices and Exchange Rates. What have We

Learned?” Journal of Economic Literature 35, 1243-72.

Goldfeld, S. M. and R. E. Quandt 1973. “A Markov Model for Switching Regressions”, Journal

of Econometrics 1, pp. 3-16.

Hamilton, James D. 1990. “Analysis of Time Series Subject to Changes in Regime”, Journal of

Econometrics 45, pp. 39-70.Joey Chew et all, 2011

Nidhaleddine, B. C. 2012. “Asymmetric exchange rate pass-through in the Euro area: New

evidence from smooth transition models”, Economics - The Open-Access, Open-Assessment

E-Journal, Kiel Institute for the World Economy, vol. 6(39), pages 1-28.

Oinonen, S., M. Paloviita and L. Vilmi 2013. “How have inflation dynamics changed over time?

Evidence from the euro area and USA”, Bank of Finland Research Discussion Papers 6

Quandt, Richard E. 1958. “The Estimation of Parameters of Linear Regression System Obeying

Two Separate Regimes”, Journal of the American Statistical Association 55, pp. 873-880.

240 BIFEC Book of Abstracts & Proceedings (Volume I Issue II)

Index of Authors

Ahmet DURAN, 130

Ahmet Faruk AYSAN, 1

Antonina WASZCZUK, 34

Burhaneddin İZGİ, 130

Chiara GUERELLO, 82

Evren ARIK, 66

Ferhat ARSLANER, 121

Gültekin GÖLLÜ, 1

Güzin Gülsün AKIN, 1

Hamid MOHTADI, 173

Levent YILDIRAN, 1

M. Vedat PAZARLIOĞLU, 196

Mehmet BALCILAR, 142

Nuran ARSLANER, 121

Orhan ERDEM, 66

Rıza DEMIRER, 142

Sadettin Haluk ÇİTÇİ, 110

Serkan YÜKSEL, 66

Sezer Bozkuş KAHYAOĞLU, 196

Stefan RUEDIGER, 173

Süleyman Hilmi KAL, 121