jasf volumeiii issue2(6) winter2012

7/27/2019 JASF VolumeIII Issue2(6) Winter2012

1/67

Volume III Issue 2(6) Winter 2012

128

AS

ERS

ournal ofAdvanced Studies

in FinanceJ

BiannuallyVolume III

Issue 2(6)

Winter 2012

ISSN 2068 8393


2/67

Journal ofAdvancedStudies in Finance

129

Contents:

1

Globalization financial crisis and contagion time dynamic evidencefrom financial markets of developing countries

Simplice A. ASONGUHEC - Management SchoolUniversity of Lige, Belgium

131

2

Macroeconomic impacts of the interest rate shocks in the selectedEuro area countries

Jlia UROVFaculty of Economics

Technical University of Koice, Slovakia

140

3

Analysis on runs od daily returns in Istambul stock exchange

Ahmet ENSOYIstanbul Stock Exchange, Turkey

151

4

Overnight stock price reversals

Andrey KUDRYAVTSEV

The Economics and Management DepartmentThe Max Stern Yezreel Valley Academic College, Israel

162

5

Marx's theory of crisis in the context of financialization. Analyticalinsights on the current crisis

Nikos STRAVELAKISNational Kapodestrian University of Athens, GreeceDepartment of Economics

171

inter 2012olume III, Issue 2(6)

tor in Chiefaura tefnescu

Spiru HaretUniversity, Romania

-EditorRajmund Mirdala

echnical University of Kosice,lovak Republic

torial Advisory Board

adalina Constantinescupiru HaretUniversity, Romania

osaria Rita Canaleniversity of Naples Parthenope,

aly

rancesco P. Espositollied Irish Bank, Group Market Risk

Management

ean HooiHooi,niversiti Sains Malaysia,Malaysis

KostaJosifidisniversity of Novi Sad, Serbia

van Kitovussian Academy of Sciences,

ussia

iotr Misztalechnical University of Radom,conomic Department, Poland

Andreea Pascucciniversity of Bologna, Italy

achelPrice-Kreitzcole de Management detrasbourg, France

aniel Stavarekilesian University, Czeck Republic

auraUngureanupiru HaretUniversity, Romania

ans-JrgenWeibach, Universityf Applied Sciences - Frankfurt amain, Germany

RS Publishing://www.asers.eu/asers-publishing

N 2068 8393


3/67


130

The Journal aims to publish empirical or theoretical articles which make significant contributions

in all areas of finance, such as: asset pricing, corporate finance, banking and market microstructure, but

also newly developing fields such as law and finance, behavioural finance, and experimental finance.

The Journal will serves as a focal point of communication and debates for its contributors for

better dissemination of information and knowledge on a global scale.

The Editor in Chief would like to invite submissions for the 4thVolume, Issue 1(7), Summer 2013

of the Journal of Advanced Studies in Finance(JASF).

The primary aim of the Journal has been and remains the provision of a forum for the

dissemination of a variety of international issues, empirical research and other matters of interest to

researchers and practitioners in a diversity of subject areas linked to the broad theme of finance.

All papers will first be considered by the Editors for general relevance, originality and

significance. If accepted for review, papers will then be subject to double blind peer review.

Deadline for Submission: 15th June 2013

Expected Publication Date: July 2013

Web: www.asers.eu/journals/jasf/

E-mail: [email protected]

Call for PapersVolume IV, Issue 1(7) Summer 2013

Journal of Advanced Studies in Finance
http://www.asers.eu/journals/jasf/http://www.asers.eu/journals/jasf/mailto:[email protected]:[email protected]://www.asers.eu/journals/jasf/


4/67


131

GLOBALIZATION, FINANCIAL CRISIS AND CONTAGION: TIME -

DYNAMIC EVIDENCE FROM FINANCIAL MARKETS OF

DEVELOPING COUNTRIES

Simplice A. ASONGUHEC - Management School, University of Lige, Belgium

[email protected]

Abstract:Financial integration among economies has the benefit of improving allocation efficiency and diversifying risk.

However the recent global financial crisis, considered as the worst since the Great Depression has re-ignited the fiercedebate about the merits of financial globalization and its implications for growth especially in developing countries.

This paper examines whether equity markets in emerging countries were vulnerable to contagion during the recentfinancial meltdown. Findings show: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but forPeru, Venezuela and Columbia, Latin American countries were least affected; (3) Africa and Middle East emerging marketswere averagely contaminated with the exceptions of Kenya, Namibia, Nigeria, Morocco, Dubai, Jordan, Israel, Oman, Saudi

Arabia and Lebanon. Results have two important policy implications. Firstly, we confirm that Latin America was mostprepared to brace the financial crisis, implying their fiscal and monetary policies are desirous of examination and imitation.Secondly, we have confirmed that strategic opening of the current and capital accounts based on empirical evidence for agiven region/country as practiced by India is a caution against global economic and financial shocks.

Keywords: Globalization; Financial Crisis; Contagion; Developing Countries; Equity Markets

JEL Classification: G10; G15; F30

1. Introduction

During the last decade the concern about regional and global integration of emerging equity markets hasbeen largely debated. The recent global financial meltdown and economic downturn has left many analysts

concerned about whether emerging markets suffered from contagion. Most of these markets were still in theirinfancy at the turn of the millennium, which rendered an examination of the transmission of financial variablemovements from global crisis somewhat impractical. Therefore, regrettably effects of the US stock market crashof 1987, the Mexican peso crisis of 1994, Asian currency crisis of 1997, Russian and LTCM 1 crises of 1998,Brazilian crisis of 1999 and Turkish 2000/2001 crisis have not been fully assessed in all emerging equitymarkets. The recent financial crisis provides a golden opportunity for this investigation.

There are plenty of reasons a paper should be dedicated to studying the extent to which emergingfinancial markets have been affected by the recent global financial turmoil. Results of the study could enableanalysts and policy makers to evaluate benefits of international trade and cross-border investments, andtherefore attractiveness for foreign capital inflows. Findings could also provide some basis on how developingcountries stand to benefit (loss) from long - run investment sources and global financial booms (as a result ofexternal financial shocks) through financial market integration. A natural extension of results could invite policymakers to reconsider Latin American monetary and fiscal strategies in the fight against external financial shocks.Also, the validity of Indias financial liberalization strategy could be of crucial importance to governments in otherdeveloping countries.2 Therefore this study aims to assess the impact of the recent global financial shock onemerging financial markets. The rest of the paper is organized as follows. Section 2 thoroughly reviews related

1 Long-term Capital Management2 Whereas the Indian current account has been opened fully though gradually in the 1990s, a more calibrated approach has

been followed in the opening of the capital account and subsequently the financial sector. This approach is consistent withthe weight of available empirical evidence on the benefits of capital account liberalization for acceleration of economicgrowth, particularly in emerging economies. Evidence suggests that the greatest gains are obtained from openness to

foreign direct investment followed by portfolio investment. Benefits resulting from external debt flows are questionable untilgreater domestic financial market development has taken place (Henry 2007).

DOI: 10.2478/v10259-012-0008-9
mailto:[email protected]:[email protected]:[email protected]


5/67


132

literature. Data and methodology for measuring contagion are presented and outlined respectively in Section 3.Empirical analysis and discussion are covered in Section 4. We conclude with Section 5.

2. Related literature

2.1. Effects of financial market integration

Financial integration between economies is believed to have two main positive impacts: the improvementof capital allocation efficiency and diversification of risks (Demyanyk, and Volosovych 2008; Coulibaly 2009;Kose et al. 2011). However, the recent global financial crisis which is viewed by many analysts and policymakers as worst since the Great Depression has cast a dark shadow on the contagious effect of financialintegration; despite its advantages. There is an extensive economics and finance literature that addresses thepotential benefits of financial integration.

From a theoretical standpoint financial globalization should facilitate efficient international allocation ofcapital and improve international risks sharing (Kose et al. 2011). Kose et al. (2011) posit that the benefits aremuch greater for developing countries because they are relatively scare in capital and rich in labor sources. Ineffect, access to foreign capital should help them grow faster through new sources of investment. They furtherprofess that since developing countries have more volatile output growth than advanced industrial economies;their potential welfare gains from international risk sharing are much greater. Their findings reveal that with

certain identifiable thresholds in variables such as financial depth and institutional quality, the cost-benefit trade-off from financial openness improves significantly once the threshold conditions are met. Much earlier Demyanyk,and Volosovych (2008) in analyzing the benefits of financial integration (resulting from international risk sharing)among 25 European Union (EU) countries presented a case for diversification of risk across EU member states ifthe risks are fully shared. In a nutshell they stressed that the 10 new members joining the EU would have highergains than the long standing 15 members. The most striking indication of financial integration benefits is the caseof South Africa, a country that has experienced financial autarky as a result of the embargo imposed in 1985 andremoved in 1993. With respect to Coulibaly (2009), there was a significant decrease in the rates of investment,capital and output during the embargo period in South Africa as compared to pre-embargo and post - embargoperiods.

During the embargo South Africa could benefit from financial isolation in event of a global financialmeltdown. This implies countries in relative financial autarky as less exposed to international financial shocks.Though a prime advantage of financial integration is risk diversification, paradoxically increased financialglobalization can reduce the scope for risk diversification because integrated markets tend to be moreinterdependent and highly correlated. Another disadvantage of financial integration could be linked to thresholdfactors pointed out earlier by Kose et al. (2011). Their study reveals that countries with low levels of financialdepth and institutional quality do not stand to benefit from financial integration. This perspective is shared bySchmukler (2004) who stresses the importance of sound financial fundamentals and strong macroeconomicinstitutions, the presence of which should enable more effective management of crises and lower the probabilityof crises and contagion. Therefore financial globalization could itself be a source of crises.

2.2. Linkages between financial integration (globalization) and crisesWe have seen that financial globalization has several potential benefits. However the recent stream of

financial crises and contagion owing to growing liberalization of financial systems and integration of financialmarkets around the world, might lead some to suggest that globalization breeds financial volatility and crises.Though domestic factors are mostly at the origin of crises, there are different channels via which financialglobalization could be related to crises.

Firstly, as pointed out by Schmukler (2004) when a countrys financial system is liberalized, it becomes anobject of market discipline exercised by both foreign and domestic investors. In a closed economy, only domesticinvestors monitor and react to unsound fundamentals, whereas in an open domestic and foreign investors mightprompt the country to achieve sound fundamentals. As elucidated earlier, the absence of sound macroeconomic,financial and institutional fundamentals could increase the probability of crises. It logically follows thatantagonistic interests and views between investors (domestic and foreign) on key fundamentals might precipitatecrises and reduce the ability to effectively monitor and manage them.

Secondly, even with sound domestic fundamentals and quality institutions, international financial market

imperfections could also lead to crises. Among other things, these could lead to herding behavior, irrationalbehavior, speculative attacks, bubbles, and crashes. To put this point plainer, regardless of market fundamentals


6/67


133

investors could speculate against a currency if they believe that the exchange rate is unsustainable; this couldlead to self-fulfilling balance of -payments. This thesis illustrated by Obstfeld (1986) has been purported bySchmukler (2004); amongst others.

Thirdly, even in the presence of sound fundamentals and absence of imperfections in international capitalmarkets, crises might still arise owing to external factors (Schmukler 2004) such as determinants of capital flows(Calvo et al. 1996) and foreign interest rates (Frankel, and Rose 1996). For instance if a country becomesdependent on foreign capital, shifts in foreign capital flows could create financial issues and economicdownturns. Frankel, and Rose (1996) clearly point-out the role foreign interest rates play in determining thelikelihood of financial crises in developing countries.

Fourthly, still borrowing from Schmukler (2004) financial globalization could lead to financial crises bycontagion, namely by shocks through real links, financial links and herding - behavior or unexplained highcorrelations. We shall focus on this fourth example3 within our research framework; the elucidation and definitionof which are worthwhile.

2.3. Definitions and channels of contagion

2.3.1. Definitions of contagionAs yet, there is no established definition of contagion by economists. According to the World Bank, there

are three main definitions of the phenomenon. Firstly, from a broad perspective contagion could be identified withthe general process of stock transmission across countries. Therefore, it is worthwhile understanding that thisdefinition does encompass both negative shocks and positive spillover effects. Secondly, contagion could beconceived as the propagation of shocks between two countries in excess of what should be expected, based onthe fundamentals after considering co-movements triggered by common shocks. This second definition issomewhat restrictive only to shocks and presupposes the mastery of what constitutes the underlyingfundamentals, without which an appraisal of excess co-movements is not possible. The last and more restrictivedefinition considers the phenomenon as the change in the transmission mechanisms that take place during aperiod of turmoil and could be appreciated by a significant increase in cross-market correlations. Within theframework of this study, we shall be restricted to the third definition because: (1) our study aims to investigate theglobal financial crisis which is a negative shock and not a positive spill-over (as opposed to the first definition);and (2) we do not master what constitutes underlying fundamentals of co - movements we are about to study (inantagonism to the second definition).

Empirically, the third definition was first proposed by Forbes, and Rigobon (2002). They assessedcontagion as a significant increase in market co-movements after a shock occurred in one country. With respectto this definition, the condition for contagion is a significant increase in co-movements as a result of a shock inone market. This implies, if two markets display a high degree of co-movements during the stability period, evenif they are highly correlated during a crisis, if this crisis-correlation is not significant it does not amount tocontagion. In the absence of a significant correlation during the crisis - period, the term interdependence is usedto qualify the situation between the two markets.

2.3.2. Channels of contagionBorrowing from Schmukler (2004), three mains channels of contagion have been identified in the

literature. (1) Real links which are often associated with trade links. For example if two countries are tradingtogether and compete in the same external market, a devaluation of the exchange rate of one countrydeteriorates the other countrys competitive advantage. In a bid to rebalance its external sectors, the loosingcountry would want to devaluate its own currency; such is the nature of Chino-American commercial relationstoday. (2) Financial links come in when two economies are connected through the international financial system.For instance, lets consider leverage institutions facing margin calls. Should the value of the collateral fall as aresult of a negative shock in one country, in a bid to increase their initial stock these institutions will sell some oftheir holdings in countries not yet affected by the shock. This gives birth to a mechanism that ripples the shocksto other countries. (3) Finally, due to herding behaviors or panics resulting from asymmetric information, financialmarkets might transmit shocks across markets. We shall not elaborate on the mechanics of this third typebecause of obvious reasons (common sense).

3 Example on the link between financial integration and crises.


7/67


134

2.4. Measuring contagionMany methods of measuring contagion have been proposed in the literature to appreciate the spreading

of international shocks across countries. The most widely used are cross - market correlation coefficientsprocedures (King, and Wadhwani 1990; Forbes, and Rigobon 2002; Collins, and Biekpe 2003; Lee et al. 2007;Asongu 2011a b), cross - market co - integration vectors changing techniques (Kanas 1998), and volatilityanalysis based on ARCH and GARCH models (King et al. 1994) and direct estimation of specific transmissionmechanisms (Forbes 2000). With respect to our restrictive definition of contagion we shall adopt Forbes, andRigobon (2002) in the context of Collins, and Biekpe (2003)4.

3. Data and Methodology

3.1. DataThe object of this study is to investigate correlations between the returns of the USA stock index and

stock indexes of emerging countries. Taking the Dow Jones Industrial Average as the base criterion, we analyzeif co-movements between the base criterion and afore mentioned financial markets were significantlystrengthened during the recent global financial crisis. In et al. (2008), MacAndrews (2008), Taylor, and William(2008) and more recently Ji, and In (2010) all use the August 9th 2007 date as the start of the financial crisis5.The sample period is divided into two categories: a 14 month pre-crisis period also known as the tranquil or

stable period and a 15 month crisis or turmoil period.In a bid to make our findings robust, the turmoil period is further divided into three sections 6: the short-run

or four month crisis-period (August 09, 2007 to December 06, 2007); the medium-term or eight months crisis -period (August 09, 2007 to April 10, 2008) and the long - term or 15 months crisis - period (August 09, 2007 toNovember 13, 2008). Weekly data used in the study is obtained from Bloombergs database. We use localcurrency index return because Forbes and Rigobon (2002) have shown that using dollar or local indices willproduce similar outcomes.

3.2. MethodologyContagion is defined by Forbes, and Rigobon (2002) as a significant increase in market co -movements

after a shock occurred in one country7.The correlation coefficient is defined as:

yx

xy

(1)

where: x is the base criterion while y is an emerging equity market.

Borrowing from Forbes, and Rigobon (2002), the correlation coefficient is adjusted in the followingmanner:

])(1[1*

2

(2)

4 The hypothesis testing in Collins, and Biekpe (2003) is slightly different from that of Forbes, and Rigobon (2002) in that, thetest statistics to determine contagion is not calculated using estimated sample variances. Their test statistics (Collins, andBiekpe, 2003) uses exact student statistics based on actual sample correlation coefficients. Contagion is then measuredby the significance of increase in adjusted correlation coefficients during the crisis period as compared with the stableperiod.

5 Date at which, BNP Paribas announced the closure of its funds that held US subprime debts.6 From empirical literature, the tranquil period is always longer than the turmoil period. For instance it is longer by a year, ten

and a half months and nine months in Forbes, and Rigobon (2002), Collins, and Biekpe (2003) and Lee et al. (2007)respectively.

7 With respect to this definition, the presence of high correlation between two markets during the stable period andeventually continuous increase in the high degree of cross market co-movements at the turmoil period does not amount to

contagion. Therefore contagion according to this definition is the presence of significant increase in co-movements after ashock. On the other hand, if the high correlation degree is not significant, the term interdependence is used to describethe event.


8/67


135

where: 1l

xx

h

xx

which appreciates the change in high - period volatility against low -period volatility.

The crisis - period is used as the high volatility period and the tranquil period as the low volatility period inthe calculation of this correlation coefficient adjuster. Contagion is subsequently measured as the significance ofadjusted correlation coefficients in time- varying turmoil periods versus the stability period. In empirical literature,Collins, and Biekpe (2003), and Lee et al. (2007) have applied both the t-test and F-test respectively for thesignificance of difference in correlations. When only one coefficient is to be estimated, both tests have the sameimplications. Following the t-statistics, the significance of increase in correlations during the turmoil period (t) withrespect to the stable(s) period is defined by:

2)(1

4)(

st

st

st

nnt

(3)

where

)4,01.0( st

nnt

with, nt (ns) indicating actual observed weeks during the turmoil (stable) period.

The following hypothesis is then put to test:

0: 21 oH versus 0: 211 H

Whereo

H is the null hypothesis of no contagion and 1H is the alternative hypothesis for the presence

of contagion

4. Empirical Results

4.1. Presentation of resultsEmpirical results are presented below in Tables 1 at page no. 136 and Table 2 at page no. 137.

4.2. Discussion of results

As shown in Tables 1 and 2, contagion results based on significant shifts in conditional (unadjusted)correlation coefficients are robust to adjusted (unconditional) correlations. From a broad point of view thefollowing effects of the financial crisis could be observed: (1) with the exceptions of India and Dhaka, Asianmarkets were worst hit; (2) but for Peru, Venezuela and Columbia, Latin American countries were least affected;(3) Africa and Middle East emerging markets were averagely contaminated with the exceptions of Kenya,Namibia, Nigeria, Morocco, Dubai, Jordan, Israel, Oman, Saudi Arabia and Lebanon.

The somewhat immunity of Latin American countries to the recent global financial meltdown is notunexpected. Given its history of financial crises, this continent was the most prepared. Current conditions showthat Latin America has improved since the Russian crisis, which gave countries in the continent some leeway(particularly in monetary policy) to implement measures that attenuate crisis effect. Latin America and theCaribbean countries have built up to 400 billion dollars in international reserves and they have substantiallyreduced their dollar - denominated debt, especially within the banking system. For instance, lower levels of debt

dollarization has allowed Brazil to loosen monetary policy amid the credit crunch in ways that many countriescould not in the post Russian crisis era. In the wake of the financial crisis, Latin American countries swiftlydepreciated their currencies without entering the turmoil. From a fiscal perspective, many of these countriessaved a considerable amount of their tax income on extra revenue from commodity bonanza at the turn of thecentury. For instance, Chile spent only 34% and kept the rest of increased tax collected in a special fund.Therefore even if the crisis had affected these countries, they still had the leeway of increasing spending whilelowering taxes, so as to easily recover from recession.

Results from Africa are entirely not unexpected. But for Kenya, Namibia, Nigeria and Morocco, Africanstock markets are contaminated in at least one time horizon. This reflects the increasing connection of Africanmarkets with global capital flows. As a matter of fact, African markets are growing in size, liquidity and degree offoreign participation. Though it may be misleading to equate contagion to integration, a logical extension ofresults could make a case for African equity markets global integration.


9/67


136

Table1. International stock indexes returns conditional (unadjusted) correlation coefficient s in2007 financial crisis

Regions Countries Full period Stable period Short-term turmoil period Medium-term turmoil period Long-term turmoil period

t-test Co t-test Co t-test Co

Africa

Botswana -0.040 0.015 0.024 0.014 0.573 0.010 5.641*** Y 0.197 0.008 1.675* Y -0.188 0.013 -2.419** NEgypt 0.336 0.045 0.196 0.034 0.419 0.028 1.968* Y 0.212 0.028 0.154 N 0.353 0.051 1.757* YKenya 0.083 0.034 0.008 0.028 0.049 0.030 0.494 N -0.178 0.038 -1.656 N 0.079 0.038 0.970 NMauritius 0.302 0.030 0.003 0.028 0.001 0.024 0.039 N -0.099 0.027 -0.922 N 0.382 0.031 4.636*** YMorocco 0.059 0.024 0.024 0.025 0.022 0.019 -0.014 N -0.109 0.019 -1.288 N 0.051 0.021 0.294 NNamibia 0.376 0.037 0.417 0.024 0.558 0.034 1.219 N 0.111 0.043 -3.093*** N 0.342 0.045 -0.845 NNigeria 0.027 0.038 0.095 0.032 -0.457 0.027 -5.710*** N -0.410 0.026 -5.617*** N -0.060 0.040 -1.743* NSouth A 0.435 0.030 0.380 0.021 0.674 0.024 2.641** Y 0.238 0.031 -1.378 N 0.428 0.036 0.522 NTunisia 0.258 0.016 0.129 0.014 0.183 0.009 0.462 N 0.165 0.018 0.343 N 0.341 0.018 2.405** Y

MiddleEast

A Dhabi -0.069 0.030 -0.053 0.021 0.246 0.024 2.706*** Y -0.133 0.025 -0.761 N -0.086 0.037 -0.356 NBahrain 0.017 0.015 -0.031 0.013 0.477 0.013 5.069*** Y 0.173 0.012 1.998** Y -0.004 0.017 0.297 NDubai -0.085 0.039 -0.027 0.027 -0.160 0.031 -1.146 N -0.173 0.030 -1.410 N -0.126 0.048 -1.089 NIsrael 0.264 0.028 0.531 0.023 0.697 0.019 1.444 N 0.287 0.025 -2.411** N 0.089 0.032 -5.462*** NJordan 0.015 0.031 0.044 0.020 0.148 0.016 0.893 N 0.034 0.020 -0.105 N 0.011 0.040 -0.381 NKuwait -0.085 0.026 n.a n.a 0.681 0.014 n.a 0.106 0.013 n.a -0.085 0.026 n.aLebanon 0.200 0.033 0.226 0.023 0.145 0.023 -0.710 N 0.181 0.021 -0.441 N 0.213 0.040 -0.155 NOman -0.217 0.031 0.112 0.016 0.013 0.019 -0.865 N -0.261 0.028 -3.867*** N -0.306 0.040 -5.112*** NQatar -0.133 0.040 -0.032 0.030 0.186 0.027 1.930* Y -0.101 0.037 -0.653 N -0.175 0.047 -1.595 NSaudi A 0.012 0.047 0.059 0.041 -0.302 0.027 -3.339*** N -0.113 0.053 -1.681* N -0.002 0.052 0.522 N

Asia

China 0.073 0.056 0.071 0.048 0.528 0.045 4.507*** Y 0.071 0.048 0.064 N 0.063 0.012 -0.582 NDhaka 0.047 0.024 -0.275 0.020 -0.462 0.022 -6.698*** N -0.275 0.020 -4.539*** N -0.132 0.020 -3.289*** YIndia 0.264 0.038 0.252 0.044 0.400 0.042 0.574 N 0.252 0.044 -0.778 N 0.212 0.048 -1.355 NIndonesia 0.057 0.040 0.394 0.054 0.773 0.055 5.268*** Y 0.394 0.054 1.389 N -0.031 0.052 -3.263*** NMalaysia 0.100 0.026 0.457 0.036 0.838 0.034 6.045*** Y 0.457 0.036 1.903* Y 0.015 0.031 -2.832*** NMongolia 0.062 0.046 -0.093 0.044 -0.175 0.056 0.665 N -0.093 0.044 1.538 N 0.049 0.038 3.499*** YPakistan 0.021 0.037 0.330 0.028 0.338 0.033 2.584** Y 0.330 0.028 2.798*** Y -0.031 0.042 -0.898 NPhilippines 0.361 0.040 0.621 0.045 0.855 0.053 7.127*** Y 0.621 0.045 4.229*** Y 0.373 0.048 1.749* YS. Korea 0.469 0.034 0.640 0.041 0.822 0.047 10.324*** Y 0.640 0.041 6.945*** Y 0.502 0.042 5.562*** YSri Lanka 0.204 0.027 0.380 0.019 -0.100 0.021 -0.828 N 0.380 0.019 3.997*** Y 0.288 0.027 3.390*** YTaiwan 0.429 0.035 0.415 0.040 0.836 0.041 18.401*** Y 0.415 0.040 5.315*** Y 0.482 0.043 7.331*** YThailand 0.355 0.037 0.422 0.039 0.715 0.035 5.908*** Y 0.422 0.039 2.722*** Y 0.385 0.046 2.698*** Y

Vietnam 0.204 0.060 0.319 0.056 0.524 0.032 3.842*** Y 0.319 0.056 1.985* Y 0.195 0.068 0.876 N

LatinAmerica

Argentina 0.543 0.041 0.644 0.026 0.752 0.045 0.934 N 0.630 0.037 -0.136 N 0.505 0.051 -1.556 N

Brazil 0.773 0.043 0.797 0.027 0.831 0.043 0.290 N 0.720 0.042 -0.744 N 0.765 0.052 -0.358 N

Chile 0.690 0.034 0.588 0.020 0.721 0.040 1.154 N 0.710 0.040 1.178 N 0.703 0.043 1.281 N

Columbia 0.475 0.032 0.336 0.026 0.381 0.030 0.386 N 0.616 0.034 2.802*** Y 0.504 0.036 1.896* YCosta Rica -0.020 0.028 -0.085 0.031 -0.088 0.019 -0.025 N -0.203 0.023 -1.140 N -0.083 0.021 0.023 N

Ecuador 0.030 0.029 0.085 0.015 0.010 0.005 -0.648 N 0.040 0.049 -0.431 N 0.016 0.037 -0.773 N

Mexico 0.774 0.037 0.721 0.026 0.814 0.037 0.800 N 0.865 0.037 1.391 N 0.784 0.044 0.692 NPeru 0.422 0.052 -0.066 0.029 0.907 0.063 35.962*** Y 0.693 0.059 11.16*** Y 0.478 0.065 7.185*** YVenezuela 0.119 0.034 0.035 0.038 0.193 0.027 1.379 N 0.269 0.034 2.313** Y 0.159 0.030 1.385 N

The table shows the conditional (unadjusted) cross market correlation coefficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period isdefined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoilperiod is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is thestable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). Contagion (Co) occurs (Y) when the test statistics is greater than the critical values. No contagion (N) occurs when the test statistics isless than or equal to the critical value.*, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents thestandard deviation.


10/67


137

Table 2. International stock indexes returns unconditional (adjusted) correlation coefficientin 2007 financial crisis

Regions Countries Full period Stable period Short-term turmoil period Medium-term turmoil period Long-term turmoil period *stp *mtp *ltp * t-test Co * t-test Co * t-test Co

Africa

Botswana -0.040 0.015 0.030 0.034 0.026 0.647 -0.321 6.747*** Y 0.265 -0.466 2.278** Y -0.197 -0.090 -2.538** NEgypt 0.336 0.045 0.219 0.217 0.163 0.459 -0.202 2.133** Y 0.234 -0.189 0.168 N 0.296 0.475 1.498 NKenya 0.083 0.034 -0.008 -0.007 -0.007 0.048 0.062 0.479 N -0.155 0.339 -1.432 N 0.069 0.317 0.845 NMauritius 0.302 0.030 -0.004 -0.003 -0.003 0.001 -0.163 0.043 N -0.102 -0.057 -0.949 N 0.373 0.060 4.502*** YMorocco 0.059 0.024 0.028 0.028 0.026 0.026 -0.250 -0.016 N -0.126 -0.250 -1.489 N 0.055 -0.160 0.320 NNamibia 0.376 0.037 0.366 0.329 0.323 0.499 0.362 1.152 N 0.084 0.745 -2.419** N 0.261 0.809 -0.694 NNigeria 0.027 0.038 0.105 0.106 0.086 -0.492 -0.171 -6.40*** N -0.448 -0.195 -6.38*** N -0.054 0.225 -1.573 NSouth A 0.435 0.030 0.358 0.321 0.302 0.648 0.151 2.604** Y 0.198 0.471 -1.188 N 0.342 0.688 0.446 NTunisia 0.258 0.016 0.166 0.117 0.117 0.233 -0.399 0.582 N 0.150 0.221 0.312 N 0.311 0.228 2.198** Y

MiddleEast

A Dhabi -0.069 0.030 -0.051 -0.050 -0.041 0.235 0.107 2.566** Y -0.124 0.145 -0.713 N -0.066 0.686 -0.275 NBahrain 0.017 0.015 -0.032 -0.033 -0.028 0.483 -0.033 5.160*** Y 0.181 -0.089 2.095** Y -0.004 0.235 0.268 NDubai -0.085 0.039 -0.027 -0.166 -0.021 -0.152 0.110 -1.089 N -0.166 0.094 -0.002 N -0.096 0.727 -0.830 NIsrael 0.264 0.028 0.569 0.522 0.477 0.731 -0.180 1.414 N 0.281 0.052 -2.380** N 0.077 0.338 -4.829 YJordan 0.015 0.031 0.050 0.045 0.032 0.166 -0.204 0.998 N 0.034 -0.017 -0.106 N 0.007 1.009 -0.269 NKuwait -0.085 0.026 n.a -0.007 n.a n.a n.a n.a n.a n.a n.a n.a n.a n.aLebanon 0.200 0.033 0.233 0.239 0.178 0.148 -0.051 -0.727 N 0.191 -0.106 -0.463 N 0.167 0.653 -0.124 NOman -0.217 0.031 0.104 0.087 0.072 0.012 0.181 -0.796 N -0.204 0.680 -2.92*** N -0.201 1.453 -3.14*** NQatar -0.133 0.040 -0.035 -0.030 -0.026 0.198 -0.123 2.063** Y -0.092 0.196 -0.598 N -0.142 0.540 -1.289 NSaudi A 0.012 0.047 0.074 0.052 0.053 -0.366 -0.351 -4.21*** N -0.099 0.294 -1.474 N -0.002 0.267 -0.606 N

Asia

China 0.073 0.056 0.058 0.059 0.052 0.488 0.112 4.108*** Y 0.065 0.165 0.060 N 0.009 0.533 -0.470 NDhaka 0.047 0.024 0.178 0.173 0.171 -0.510 -0.121 -8.05*** N -0.309 -0.224 -5.27*** N -0.148 -0.210 -3.732 YIndia 0.264 0.038 0.223 0.266 0.256 0.272 0.559 0.426 N 0.200 0.637 -0.639 N 0.161 0.773 -1.067 NIndonesia 0.057 0.040 0.107 0.165 0.169 0.490 1.441 3.566*** Y 0.267 1.392 0.983 N -0.021 1.287 -2.142** NMalaysia 0.100 0.026 0.152 0.195 0.208 0.679 0.780 5.338*** Y 0.352 0.872 1.521 N 0.012 0.632 2.222** YMongolia 0.062 0.046 -0.203 -0.253 -0.270 -0.140 0.258 0.543 N -0.094 -0.009 1.544 N 0.053 -0.138 3.782*** YPakistan 0.021 0.037 0.047 0.052 0.043 0.318 0.072 2.420** Y 0.342 -0.077 2.906*** Y -0.026 0.382 -0.764 NPhilippines 0.361 0.040 0.122 0.176 0.171 0.701 0.817 6.113*** Y 0.537 0.545 3.712*** Y 0.299 0.650 1.432 NS. Korea 0.469 0.034 0.020 0.035 0.034 0.527 1.724 5.060*** Y 0.477 1.348 4.734*** Y 0.350 1.410 3.687*** YSri Lanka 0.204 0.027 -0.006 -0.005 -0.004 -0.127 -0.216 -1.056 N 0.434 -0.271 4.687*** Y 0.286 0.017 3.362*** YTaiwan 0.429 0.035 -0.034 -0.050 -0.048 0.639 1.028 7.839*** Y 0.311 0.945 3.711*** Y 0.355 1.105 4.876*** YThailand 0.355 0.037 0.109 0.121 0.111 0.605 0.374 4.908*** Y 0.353 0.527 2.282** Y 0.296 0.815 2.087** Y

Vietnam 0.204 0.060 0.172 0.109 0.098 0.687 -0.327 5.169*** Y 0.299 0.155 1.862* Y 0.165 0.416 0.744 N

LatinAmerica

Argentina 0.543 0.041 0.538 0.579 0.410 0.654 0.746 1.006 N 0.565 0.407 -0.139 N 0.293 0.976 -1.312 N

Brazil 0.773 0.043 0.724 0.728 0.601 0.765 0.586 0.352 N 0.640 0.550 -0.843 N 0.557 0.900 -0.482 N

Chile 0.690 0.034 0.453 0.454 0.326 0.589 1.044 1.174 N 0.577 1.035 1.189 N 0.434 1.228 1.198 NColumbia 0.475 0.032 0.316 0.300 0.252 0.359 0.142 0.370 N 0.567 0.289 2.665*** Y 0.394 0.377 1.591 N

Costa Rica -0.020 0.028 -0.108 -0.097 -0.123 -0.111 -0.376 -0.031 N -0.231 -0.235 -1.294 N -0.120 -0.309 0.033 N

Ecuador 0.030 0.029 0.145 0.047 0.034 0.017 -0.659 -1.106 N 0.022 2.360 -0.236 N 0.006 1.517 -0.308 N

Mexico 0.774 0.037 0.657 0.655 0.537 0.761 0.430 0.898 N 0.820 0.442 1.607 N 0.614 0.715 0.857 NPeru 0.422 0.052 -0.045 -0.046 -0.029 0.824 1.184 15.092*** Y 0.555 1.072 7.210*** Y 0.242 1.268 3.117*** Y

Venezuela 0.119 0.034 0.042 0.037 0.044 0.229 -0.301 1.642 N 0.285 -0.114 2.452** Y 0.201 -0.216 1.760* YThe table shows the unconditional (adjusted) cross market correlation coefficients () and standard deviations for the US and other stock markets. Test statistics is obtained from t-transformations. The stable period isdefined as the 14-month pre-crisis period (June 08, 2006 to August 09, 2007). The short-term turmoil period is defined as the four-month crisis period (August 09, 2007 to December 06, 2007). The medium-term turmoilperiod is defined as the eight months crisis period (August 09, 2007 to April 10, 2008). The long-term turmoil period is defined the fifteen months crisis period (August 09, 2007 to November 13, 2008). The full period is thestable period plus the long-term turmoil period (June 08, 2006 to November 13, 2008). Contagion (Co) occurs (Y) when the test statistics is greater than the critical values. No contagion (N) occurs when the test statistics isless than or equal to the critical value.*, **, ***: represent significance at 10%, 5% and 1% respectively. (nt+ns-4) degrees of freedom for the t-statistics are (66+61-4); (35+61-4);(17+61-4) for the long, medium and short terms respectively. : represents thestandard deviation. *stp, *mtp, *ltp denote adjusted correlation coefficients for the short, m edium and long term periods respectively. : correlation coefficient adjuster.


11/67


138

Looking at the Middle East, with the exceptions of Israel, Oman and Saudi Arabia, oil exporting countries(Bahrain and Qatar) were contaminated while but for Abu Dhabi non producing states (Dubai, Jordan, Lebanon)remained unaffected. Borrowing from Anoruo, and Mustafa (2007) on the relation between oil and stock prices,where causality runs from the Dow Jones Industrial Average (DJIA) to oil prices and not vice versa; the DJIAwhich is our base criterion in this study negatively affected oil prices which in - turn had a toll on stock markets ofoil exporting countries.

While Dhaka and India in Asia remained uncontaminated, China and Mongolia were affected only in theshort and long horizons respectively. Other emerging markets were contaminated at least in two time-horizonseach. The unexpected speed and force with which the global financial crisis affected Asian economies could beexplained from trade channels. The region has deep economic integration with the rest of the world, especiallydevelopments in the United States. A case in point is the loss in export volume growth in Western Asia from 6.4%in 2006 to -0.6 in 2007. Conversely, the fact that India was unaffected is not unexpected. This is because, Indiahas a completely different approach to financial globalization. Whereas, the Indian current account was fullyopened on a gradual basis in the 90s, a more calibrated approach has been followed to the opening of the capitalaccount and subsequently the financial sector. This approach is consistent with the weight of available empiricalevidence on the benefits of capital account liberalization for acceleration of economic growth, particularly inemerging economies. Evidence suggests that the greatest gains are obtained from openness to foreign direct

investment followed by portfolio investment. Benefits resulting from external debt flows are questionable untilgreater domestic financial market development has taken place (Henry 2007).

Conclusion

Financial integration among economies has the benefit of improving allocation efficiency and diversifyingrisk. However the recent global financial crisis, considered as the worst since the Great Depression has re -ignited the fierce debate about the merits of financial globalization and its implications for growth especially indeveloping countries. This paper has examined whether equity markets in emerging countries were vulnerable tocontagion during the recent global financial meltdown.

Findings show: (1) with the exceptions of India and Dhaka, Asian markets were worst hit; (2) but for Peru,Venezuela and Columbia, Latin American countries were least affected; (3) Africa and Middle East emergingmarkets were averagely contaminated with the exceptions of Kenya, Namibia, Nigeria, Morocco, Dubai, Jordan,Israel, Oman, Saudi Arabia and Lebanon.

Results have two important policy implications. Firstly, we confirm that Latin America was most preparedto brace the financial crisis, implying their fiscal and monetary policies are desirous of examination and imitation.Secondly, we have confirmed that strategic opening of the current and capital accounts based on empiricalevidence for a given region/country as practiced by India is a caution against global economic and financialshocks.

References

[1] Anoruo, E., and Mustafa, M. 2007. An empirical investigation into the relation of oil to stock market prices.North American Journal of Finance and Banking Research, 1(1):1-15.

[2] Asongu, S.A. 2011a. Political Crises and Risk of Financial Contagion in Developing Countries: Evidence fromAfrica. Journal of Economics and International Finance, 3(7):462-467.

[3] Asongu, S.A. 2011b. The 2011 Japanese earthquake, tsunami and nuclear crisis: evidence of contagion frominternational financial markets. MPRA PaperNo. 31174

[4] Calvo, G.A., Leiderman, L., and Reinhart, C.A. 1996. Inflows of capital to developing countries in the 1990s.Journal of Economic Perspectives, 10:123-139.

[5] Collins. D., Biekpe, N. 2003. Contagion: a fear for African equity markets? Journal of Economics andBusiness, 55:285-297.

[6] Coulibaly, B. 2009. Effects of financial autarky and integration: The case of South Africa embargo. Journal ofInternational Money and Finance, 28:454-478.

[7] Demyanyk, Y., Volosovych, V. 2008. Gains from financial integration in the European Union: Evidence for newand old members. Journal of International Money and Finance, 27:277-294.


12/67

Volume III Issue 2 (6) Winter 2012

139

[8] Forbes, K.J. 2000. The Asian flu and Russian virus: firm-level evidence on how crises are transmittedinternationally. NBER Working Paper7807.

[9] Forbes, K., and Rigobon, R. 2002. No contagion, only interdependence: measuring stock market co movements. Journal of Finance, 57(5):22232261.

[10] Frankel, J. and Rose, A.K. (1996). Currency crashes in emerging markets: An empirical treatment. Journal of

International Economics, 41:351-366.

[11] Henry, P.B. 2007. Capital Account Liberalization: Theory, Evidence and Speculation. Journal of EconomicLiterature, XLV: 887-935.

[12] In, F., Cui, J., and Mahraj, A. 2008. The Impact of a new term auction facility on LIBOR-OIS spreads andvolatility transmission between money and mortgage market. http://papers.ssrn.com/sol3/papers.cfm?abstract id=1272806(unpublished manuscript).

[13] Ji, P.I., and In, F. 2010. The Impact of the global financial crisis on the cross-currency linkage of LIBOR-OISspreads. Journal of International Financial Markets, Institutions and Money, 20:575-589.

[14] Kanas, A. 1998. Linkages between the US and European equity markets: further evidence from cointegrationtest.Applied Financial Economics, 8 (6):607614.

[15] King, M., Sentana, E., and Wadhwani, S. 1994. Volatility and links between national stock markets.Econometrica, 62 (4):901933.

[16] King, M., and Wadhwani, S. 1990. Transmission of volatility between stock markets. Review of FinancialStudies, 3(1):533.

[17] Kose, M.A., Prasad., E.S., and Taylor, A.D. 2011. Thresholds in the process of international financialintegration. Journal of International Money and Finance, 30:147-179.

[18] Lee., H., Wu, H., and Wang., Y. 2007. Contagion effect in financial markets after the South-East AsiaTsunami. Research in International Business and Finance, 21:281-296.

[19] McAndrews, J., Sarkar, A., and Wang, Z. 2008. The Effects of the term auction facility on the London inter-

bank offered rate. Federal Reserve Bank of New York Staff Reports No. 335.

[20] Obstfeld, M. 1986. Rational and self-fulfilling balance of payments crises. American Economic Review,76:7281.

[21] Schmukler, S.L. 2004. Financial Globalization: gain and pain for developing countries. Federal Reserve Bankof Atlanta Economic Review: 39-66.

[22] Taylor, J.B., and Williams, J.C. 2008. Further results on a black swan in the money market. Stanford Institutefor Economic Policy, Research Discussion Paper07-046.
http://papers.ssrn.com/sol3/papers.cfmhttp://papers.ssrn.com/sol3/papers.cfm


13/67


140

MACROECONOMIC IMPACTS OF THE INTEREST RATE SHOCKS IN

THE SELECTED EURO AREA COUNTRIES

Jlia UROVFaculty of Economics

Technical University of Koice, [email protected]

Abstract:The following paper aims at analysing the impact of a shock in interest rate on selected economic variables in the

five European Union member countries (Germany, France, Netherlands, Belgium and Luxembourg). The analysis shall bedone through the VAR method where we will use the approach of the recursive Cholesky decomposition of the variance-covariance matrix. We expect that the results of the analysis will enable us to determine the shock impact on thedevelopment of the selected variables. The data necessary for the analysis will be taken from the IMF, BIS, Eurostat andECB statistics collected in the time period 2002 - 2012.

Keywords: transmission mechanism; interest rate; nominal effective exchange rate; inflation; VAR; Choleskydecomposition; impulse-response function.

JEL Classification: F 36, F41, E52

1. Introduction

The objective of the European monetary policy is to maintain price stability in all member states of theeuro area through the single monetary policy. The last four years reviewed whether the single Europeanmonetary policy is able to achieve its main objective and thus create a stable economic environment for overallmacroeconomic developments in the euro area and for its individual members. To analyze the ability of commonmonetary policy to influence the development of macroeconomic indicators, it is nec essary to study ECBs

monetary policy transmission mechanism. This should allow influencing the development of other economicvariables and ultimately, the development of the price level by regulating the official interest rates. The figurebelow (Figure 1) provides illustration of the main transmission channels of ECB monetary policy decisions.

Figure 1. Transmission mechanism of monetary policySource: ECBIn our analysis, we will be interested in interest rates transmission channel and its impact on inflation,

nominal effective exchange rate - NEER and GDP development. In this paper we will analyse, if shock in interest

DOI: 10.2478/v10259-012-0009-8


14/67


141

rates (monetary shock) will have a desirable impact on selected macroeconomic indicators (inflation, NEER,GDP). We will estimate models based on the fundamental assumptions of the transmission mechanism ofmonetary policy. The recent crisis significantly affected the evolution of the euro area, so we divided our analysisinto two periods The first one covers the period from the first quarter of 2002 to the fourth quarter 2007. Thesecond period covers also the years of the economic crisis (2002Q1-2012Q2). Selection of the countries -Germany, France, Netherlands, Belgium and Luxembourg, reflects the size and influence of these economies inthe euro area. The actual problems that affected economies such as Greece, Spain, and Ireland, have not yetmanifested in selected countries. We will also be able to follow the shock impact on the observed indicators forthe whole euro area and compare it with the results for the reporting countries. Finally, we could also conclude ifthe model is able to explain global performance across the euro area and the individual results of euro areacountries.

2. Overview of the literature

Many recent studies focused on the European monetary policy and monetary transmission mechanism.Crespo-Cuaresma, Reininger (Crespo-Cuaresma, Reininger 2007) studied the interest rate pass-through in fiveCentral and Eastern European countries - the Czech Republic, Hungary, Poland, Slovakia and Slovenia. Thepass - through appears similar in these countries and is higher than in core euroarea countries. Karagiannis,

Panagopoulos, Vlamis (Karagiannis, Panagopoulos, Vlamis 2010) examined the interest rate transmissionmechanism for the Eurozone and the USA. For an efficient monetary policy, any change in the central bankpolicy rate is meant to be transmitted to retail interest rates, ultimately influencing consumer and business lendingrates and therefore aggregate domestic demand and output. They reveal the relative importance of the centralbank and Money market rates as policy vehicle variables in the two banking systems. Chionis, Leon (Chionis,Leon 2006) examined the transmission process of the policy rate to the lending and deposit rates in Greece forthe period 19962004 within bivariate cointegration and error correction framework.

As a consequence of the common monetary policy the bank rates become much more responsive to thepolicy rate in terms of impact multipliers and speed of convergence to the equilibrium rates. They stated thatpositive effects of the monetary policy have not fully arrived at the debtors and investors yet. Badarau, Levieuge(Badarau, Levieuge 2011) analyze how financial heterogeneity can accentuate the cyclical divergences inside amonetary union that faces technological, monetary, budgetary and financial shocks. They show that a common

monetary policy contributes to worsening cyclical divergences, in comparison with monetary policies that wouldbe nationally conducted. Gntner (Gntner 2011) stated that the degree of monopolistic competition in thebanking sector has a sizeable impact on the pass-through of changes in the policy rate. In particular, a morecompetitive market for bank credit amplifies the efficiency of monetary policy. gert, Moons, Garretsen, Aarle,Fornero (gert, Moons, Garretsen, Aarle, Fornero 2007) analyzes monetary policy in a stylized New - Keynesianmodel. Using simulations of the estimated model, it is analyzed how these aspects might affect monetary policyof the ECB and macroeconomic fluctuations in the Euro Area.

Macroeconomic adjustments and monetary policy were shown to depend crucially upon the monetarypolicy regime: whether monetary policy was implemented under commitment, discretion or a rule-basedframework was seen to have important consequences. Their analysis highlighted the role of external factors andfiscal policy for monetary policy in the Euro Area. Not only will the interest rate channel of monetary policydetermine outcomes, but also the exchange rate channel, via pass-through and competitiveness effects.

Designing monetary and fiscal regimes in the Euro Area is very much interdependent and conditionalupon the economic structure and presence of different types of shocks. Brissimis, Skotida (Brissimis, Skotida2008) examined the optimal design of monetary policy in the European monetary union in the presence ofstructural asymmetries across union member countries. Based on a two-country, forward-looking, generalequilibrium model, which is estimated for two euro area countries (Germany and France), they showed that thereare gains to be achieved by the ECB taking into account the heterogeneity of economic structures. They statedthat it is important that the ECB takes into consideration national characteristics in formulating its monetarypolicy, especially in view of more countries joining the European monetary union in the future. Vlaar (Vlaar 2004)investigated the monetary transmission mechanism within the European Monetary Union. He concluded thatpermanently reducing the inflation objective depresses output in the first year, but has no real effects in the longrun. His results indicated that aggregate demand shocks are most important during the first year, after which

aggregate supply shocks dominate. Fourcans, Vranceanu (Fourcans, Vranceanu 2007) analysed the Europeancentral bank monetary policy over the period 1999-2006. They inferred some policy recommendations and


15/67


142

pointed out that the bank appears to react significantly to future inflation deviations from the objective, but alsodirectly to changes in real activity. For a bank like the ECB, the main concern should be the building of credibility.

The focus on real activity may be premature, and may put at risk the euro zone performance in the long-run. Sondermann, Bohl, Siklos (Sondermann, Bohl, Siklos 2009) analyzes the first part of the stock marketchannel of monetary policy in the euro area. They find heterogeneous reactions of euro area stock markets tounexpected ECBs interest rate decisions. In general, they find ECBs decisions to be well anticipated by stockmarkets. Mirdala (Mirdala 2009) analyze the ability of the exchange rate to weaken or eventually to strengthenthe transmission of the external inflation pressures to the national economy in the Czech Republic, Hungary,Poland and the Slovak republic. Sinicakova, Pavlickova (Sinicakova, Pavlickova 2011) stated that the period ofcrisis threatened the position of the Taylor-type rules and similar monetary rules in the application of monetarypolicy.

It seemed that the Taylor rules were not valid any more. They have compared the formulations ofmonetary rules in several countries and they have calculated a monetary rule for Slovakia. Bartokova (Bartokova2010) explains the functioning of a monetary transmission in general, and then focuses on the particular types oftransmission channels used by each of the central banks in V4 countries. These countries were mainly focusingon the maintaining of price and exchange rate stability at the beginning of the transformation process.

3. Data and econometric modelFor the purpose of estimating the effect of the interest rate exogenous shocks on economy of the country

we have used the quarterly data from 2002Q1 to 2012Q2 (42 observations) for three macroeconomic indicators -gross domestic product, inflation (domestic consumer price index), NEER for each country from the group beinganalysed (Germany, France, Belgium, Netherlands and Luxembourg). Time series for the gross domestic productare seasonally adjusted. The data were taken from the IMF, Bank for international settlements, Eurostat, ECBand statistics.

In order to analyze the transmission of the interest rate shocks, we shall use the VAR method vectorautoregressive methodology. This method belongs to the most successful, flexible and easily usable methods toanalyse time series of more variables. The final causal impacts of unexpected shocks on the variables beingexamined are summarized in the impulse response functions. For our purposes we shall use the approach of therecursive Cholesky decomposition of the variance-covariance matrix.

Before using the results of econometric analysis it is necessary to test the time series for stationarity andcointegration. Stationarity of time series is an important precondition of an econometric analysis quality. We shalldetermine stationarity through the unit root test using the ADF Augmented Dickey - Fuller Test and the PP Phillips Perron Test. Both of the tests verify the zero hypotheses that the time series are non-stationary. Theunit root test performed on the values and particularly on the first differentials of the time series has rejected thezero hypotheses, thus it has proven the existence of stationarity in the time series being monitored.

After verification of stationarity it is necessary to carry out the Johansens cointegration test in order toverify existence of a long - term balance relationship among the variables. Cointegration testing is also importantfor distinguishing between real and false regression. The results of the Johansens cointegration test haveproven that there are no stable relations among the variables, i.e. the variables are not cointegrated. The resultsof the unit root and cointegration tests are not reported here. They are available upon request from the author.

4. Results and discussion

We create two models: The first model will analyze the impact of monetary policy shock - shock in the official interest rate on

the development of 3-month Money market interest rates in the euro area and the development of 3-month Euribor -Yt = [it, iet] (it- 3 -month Money market interest rates, iet- 3- month Euribor).

The second model is based on the assumption that the first model is working and that the shock in theofficial interest rate is transferred to Euribor. In this second model, we will analyze the impact ofshocks in 3-month Euribor on the development of GDP, inflation rate and NEER in the euro area andin selected individual countries - Yt = [yt, e t pt] (yt- gross domestic product, et- NEER, pt consumerprice index).


16/67


143

4.1 Effects of Interest rate shock on mmir and euribor

Firstly we will analyze the impact of the shock in the official interest rate on the development of Euriborand Money market interest rate. Based on assumptions about the functioning of the interest rate transmissionmechanism, change in official interest rates should be passed on money market interest rates and then on bankrates. If we follow the evolution of interest rates (Figure2) in the period 2002-2012Q2 we can conclude that they

have very similar development. We also noted the upward trend of monitored interest rates in the period 2005-2008 and the sharp decline in 2009 and 2010.

Figure 2. The evolution of interest ratesSource: Eurostat, ECB

We studied the impact of shock in the official interest rate on the development of 3-month money marketinterest rate (MMIR) and the 3- month Euribor, based on the quarterly data for the period 2002-2007. On thebasis of the analysis performed through the VAR method we may form the course of impulse-response functionsin the following charts that show responses of MMIR in eurozone and Euribor to the Cholesky one standarddeviation shocks. We would expect that a positive shock in official interest rates cause the same reaction inMMIR and Euribor. This reaction is relatively weak and can be observed in the following figure (Figure 3).

Figure 3. Response of Euribor and MMIRSource:Authors calculations.

Moving the period to second quarter 2012 (period 2002-2012Q2) reaction is relatively stronger and thehighest response comes with lag of three quarters. At the end of the period the reaction disappears (see Figure4). On this basis, we might conclude that the transmission of the official interest rate shock on Euribor and MMIRwas of the same nature.

Figure 4. Response of Euribor and MMIR

Source: Authors calculations.

0

1

2

3

4

5

6

02 03 04 05 06 07 08 09 10 11 12

CBIR_EU

0

1

2

3

4

5

6

02 03 04 05 06 07 08 09 10 11 12

EURIB3M

0

1

2

3

4

5

6

02 03 04 05 06 07 08 09 10 11 12

MMIR_3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of EURIB3M to CBIR_EU

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of MMIR_3M to CBIR_EU

Response to Cholesky One S.D. Innovations

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of MMIR_3M to CBIR_EU

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of EURIB3M to CBIR_EU

Response to Cholesky One S.D. Innovations


17/67


144

4.2. Effects of interest rate shock on GDPDevelopment of GDP in the reporting countries during the period 2002 -2012Q2 was very similar to that of

the entire euro zone (see figure 5, where 2005=100%). By 2007, GDP globally grew. In certain countries such asLuxembourg we can follow a stronger growth, in others such as France, the growth was slower. In 2008 all thecountries were influenced by crisis and compared to 2007, GDP even declined. After this year, the GDP growth

trend re-establishes.

*EU=eurozone, DE=Germany, FR= France, BE = Belgium, NL= Netherlands, LU= Luxembourg

Figure 5. Development of GDPSource: IMF

The basic hypothesis for the reaction of GDP to interest rate shock is that a positive shock in interest rate

(3 - month Euribor) may have a negative impact on further development of the GDP. We can expect that a rise ofinterest rates causes a decline of the major components of GDP - demand, investment, and thus with a certaintime lag will have a negative impact on the development of the total GDP. The reaction of the shock can beobserved in figure 6, which shows the response for the countries based on data up to 2007. We can state that inthe case of euro area, we verified the basic hypothesis. Response of GDP to a shock in interest rate is negativeand increases with the lag of five quarters. A very similar reaction has GDP development in Germany, Franceand Belgium. Weak response can be observed in the Netherlands and the basic hypothesis is not verified in thecase of Luxembourg, where a positive shock in interest rates has a positive reaction of GDP.


Figure 6. Response of GDP (by 2007)Source: Authors calculations.

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_BE_SA

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_DE_SA

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_EU_SA

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_FR_SA

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_LU_SA

88

92

96

100

104

108

112

116

02 03 04 05 06 07 08 09 10 11 12

GDP_NL_SA

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP _EU_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_DE_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_FR to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_BE_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GD P_NL_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GD P_LU_SA to EURIB3M


18/67


145

With extending the monitored period up to 2012Q2, model verified the basic hypothesis with a lag of sixquarters in all reported countries except Luxembourg (see Figure 7). In this case, the reaction is positive, andagain at the end of the reporting period disappears.


Figure 7. Response of GDP (by 2Q2012)Source:Authors calculations.

4.3 Effects of interest rate shock on NEER

Another observed reaction on the interest rates shock is the reaction of the nominal effective exchangerates (NEER). NEER is a weighted average value of a country's bilateral exchange rate to all the currencies ofthe relevant trade partners of a country. The weights are determined by the importance a home country places onall other currencies traded within the pool, as measured by the balance of trade. (NBS, Investopedia). Based onthe figure 8, we can follow the evolution of NEER in the individuals monitored countries and for the whole euroarea.

It can be concluded again, that the development of individual countries is very similar to that for the wholeeuro area.The development of NEER for euro zone is the most unstable while relatively stable development ofNEER shows the case of Luxembourg. During the reporting period, NEER gradually appreciated in selectedcounties over 2002-2009 (except 2005) and also depreciated over 2010-2012Q2.

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_DE_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_EU_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_FR to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_BE_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_NL_SA to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of GDP_LU_SA to EURIB3M


19/67


146


Figure 8. Development of NEER

Source: Bank for international settlements

The basic hypothesis in this case expects that a positive shock in interest rates will cause NEERappreciation and thus a positive response of IRF function. The basic hypothesis was confirmed for the results inthe case of euro area. The initial reaction is only slightly positive, but its intensity increases with the distance ofthe two quarters. Impulse is lost after nine quarters.Similar, but not as strong reaction can be observed in thecase of France and Luxembourg. The basic hypothesis was not verified in the case of Germany, Belgium and theNetherlands (see Figure 9).

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_BE

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_DE

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_EU

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_FR

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_LU

80

85

90

95

100

105

110

02 03 04 05 06 07 08 09 10 11 12

NEER_NL

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_EU to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_DE to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_FR to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_BE to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_LU to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_NL to EURIB3M


Figure 9. Response of NEER (by 2007)

Source: Authors calculations.


20/67


147

By including the crisis in the reporting period and moving the period to second quarter 2012, the NEERreaction in some cases changed. We can state that in all reported countries, as well as for the euro area, thebasic hypothesis of NEER positive response to the positive shock in interest rates is verified. However, a positivereaction comes up with a lag of four quarters with the highest intensity after seven quarters (see Figure 10).


Figure 10. Response of NEER (by 2Q 2012)Source:Authors calculations.

4.4. Effects of interest rate shock on inflationThe verification of the price level reaction to the interest rates shock is one of the most important results of

the model. We assume that a positive shock in interest rate will have a negative reaction in the evolution of theprice level in euro area, as well as in the monitored countries. Behaviour of inflation in the euro area over the

period 2002 - 2007 is relatively stable. Since the end of 2007 to the end of the reporting period the developmentof inflation has been relatively volatile. A similar trend can be observed in all surveyed countries with the mostvolatile inflation behaviour in the case of Belgium (see Figure 11).

*EU=eurozone, DE=Germany, FR= France, BE = Belgium, NL= Netherlands, LU= Luxembourg Figure 11. Development of inflation rate

Source: IMF

As already mentioned, the basic hypothesis expects that positive shock in interest rate will cause anegative reaction in the evolution of the price level and the negative course of IRF functions performed through

the VAR method. Based on the analysis of data till 2007, we can monitor the progress of IRF functions (seeFigure 12). The basic hypothesis was confirmed in the euro zone with the lag of one quarter, but in the mid-term

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_BE

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_DE

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_EU

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_FR

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_LU

-2

0

2

4

6

02 03 04 05 06 07 08 09 10 11 12

P_NL

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_EU to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_DE to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_FR to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_BE to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_NL to EURIB3M

-2

-1

0

1

2

1 2 3 4 5 6 7 8 9 10

Response of NEER_LU to EURIB3M


21/67


148

the reaction disappears. A similar reaction can also be observed in the case of France and Luxembourg, forBelgium, the reaction is lagged by five quarters. In the case of Germany and Netherlands the reaction iscontroversial. It should be noted that in all cases the reaction is relatively weak.


Figure 12. Response of inflation (by 2007)Source: Authors calculations.

Including the years 2008 to 2012 (the second quarter) in the analyzed period, the reaction of IRF functionsis more significant. The lag of reaction however has shifted to six quarters. In the case of Netherlands, the basichypothesis is not verified (see Figure 13). We expected a stronger and intensive response of price level to ashock in interest rate.


Figure 13. Response of inflation (by 2Q 2012)Source:Authors calculations

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_EU to EURIB3M

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_DE to EURIB3M

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_FR to EURIB3M

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_BE to EURIB3M

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_NL to EURIB3M

-.4

-.2

.0

.2

.4

1 2 3 4 5 6 7 8 9 10

Response of P_LU to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_BE to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_NL to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_LU to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_EU to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_D E to EURIB3M

-1.0

-0.5

0.0

0.5

1.0

1 2 3 4 5 6 7 8 9 10

Response of P_FR to EURIB3M


22/67


149

Conclusion

The aim of the analysis was to investigate the response of macroeconomic variables to a shock in interestrate. In this way, we wanted to analyze the ability of the monetary policy and transmission mechanism toinfluence selected indicators for the whole euro area, as well as of the selected countries. We examined twomodels. In the first model, we studied the reaction of the money market interest rates and Euribor to shock in

official interest rate, where we verified the basic hypothesis. The second model analyzed the impact of interestrate shocks on GDP, NEER and inflation in the two studied periods. First period ends in 2007 before the crisis,the second period ends in second quarter of 2012 which already includes crisis period.

The first examined indicator was GDP. Here, the basic hypothesis indicates that the positive shock ininterest rate should cause the negative reaction of GDP. During the first observed period basic hypothesis wasverified for the cases of euro zone and other countries except Netherlands and Luxembourg. The reaction ofNetherlands was weak and the basic hypothesis for the case of Luxembourg must be rejected. In the secondperiod, the model confirms the basic hypothesis but with the lag of six quarters in the case of all monitoredcountries except Luxembourg, where this hypothesis must be rejected again.

The basic hypothesis for reaction of second monitored indicator NEER expect that positive interest rateshock causes the positive reaction of NEER behaviour. This hypothesis was verified for euro zone, France andLuxembourg in the first observed period. Hypothesis was not verified for Germany, Belgium and Netherlands.

Moving the period to second quarter 2012, the basic hypothesis of NEER positive response, in all reportedcountries, as well as for the euro area, was verified but with the lag of four quarters.

For the last analysed indicator inflation, the basic hypothesis of negative reaction to interest rate shockwas verified during the first monitored period for the case of euro zone, France, Luxembourg and Belgium. Wedid not verify basic hypothesis in the case of Germany and Netherlands. For the second analysed period, thebasic hypothesis is verified only with the lag of six quarters and the hypothesis is rejected in the case ofNetherlands.

The results of our analysis are not clear in all cases and weak especially for the case of inflation, so wecannot clearly confirms the high efficiency of the transmission mechanism. Therefore, it is certainly necessary tocontinue the analysis and explored model.

Acknowledgement

This paper was written in connection with scientific project VEGA no. 1/0973/11.

References

[1] Badarau, C., Levieuge, G. 2011. Assessing the effects of financial heterogeneity in a monetary union a DSGEapproach. Economic Modelling28(2011): 24512461.

[2] Bartkov, L. 2010. The evolution of the monetary policy transmission mechanism of V4 countries. E+MEkonomie a Management. 13 (2): 6-18.

[3] Brissimis, N.S., Skotida, I. 2008. Optimal monetary policy in the euro area in the presence of heterogeneity.Journal of International Money and Finance 27(2008): 209-226.

[4] Chionis, P.D., Leon, A.C. 2006. Interest rate transmission in Greece: Did EMU cause a structural break?Journal of Policy Modeling28 (2006) 453466. ISSN: 0161-8938

[5] gert, B., Crespo-Cuaresma, J., Reininger, T. 2007. Interest rate pass-through in central and Eastern Europe:Reborn from ashes merely to pass away?, Journal of Policy Modeling29 (2007) 209225. ISSN: 0161-8938

[6] Fourcans, A., Vranceanu, R. 2007. The ECB monetary policy: Choices and challenges. Journal of PolicyModeling29(2007):181194.

[7] Jochen, H.F.G. 2011. Competition among banks and the pass-through of monetary policy. EconomicModelling28(2011):18911901.

[8] Karagiannis, S., Panagopoulos, Y., Vlamis, P. 2010. Interest rate pass-through in Europe and the US:Monetary policy after the financial crisis. Journal of Policy Modeling32(2010):323338.

[9] Mirdala, R. 2009. Exchange rate pass-through to domestic prices in the Central European countries. Journalof Applied Economic Sciences. 4(3): 408-424.


23/67


150

[10] Moons, C., Garretsen, H., Van Aarle, B., Fornero, J. 2007. Monetary policy in the New-Keynesian model: Anapplication to the Euro Area. Journal of Policy Modeling29(2007): 879902.

[11] Sinikov, M., Pavlickov, V. 2011. Monetary Rules and Their Application in Times of Economic Crisis and inthe Euro Area. E&M Economics and Management3(2011):16-29.

[12] Sondermann, D.T. Bohl, M.L., Siklos, P. 2009. The euro area stock market channel: Does one size fit all?

Finance Research Letters 6 (2009): 230235.

[13] Vlaar, P.J.G. 2004. Shocking the eurozone. European Economic Review48(2004):109 131.

*** ECB. 2012.http://www.ecb.europa.eu/mopo/intro/transmission/html/index.en.html.

*** Investopedia:http://www.investopedia.com/terms/n/neer.asp#ixzz29eZnIU00.

*** NBS. Metodika vpotu efektvneho vmennho kurzu v NBS.

***http://www.nbs.sk/_img/Documents/_Statistika/VybrMakroUkaz/EER/NEER_REER_Metodika.pdf.
http://www.ecb.europa.eu/mopo/intro/transmission/html/index.en.htmlhttp://www.ecb.europa.eu/mopo/intro/transmission/html/index.en.htmlhttp://www.investopedia.com/terms/n/neer.asp#ixzz29eZnIU00http://www.investopedia.com/terms/n/neer.asp#ixzz29eZnIU00http://www.nbs.sk/_img/Documents/_Statistika/VybrMakroUkaz/EER/NEER_REER_Metodika.pdfhttp://www.nbs.sk/_img/Documents/_Statistika/VybrMakroUkaz/EER/NEER_REER_Metodika.pdfhttp://www.nbs.sk/_img/Documents/_Statistika/VybrMakroUkaz/EER/NEER_REER_Metodika.pdfhttp://www.investopedia.com/terms/n/neer.asp#ixzz29eZnIU00http://www.ecb.europa.eu/mopo/intro/transmission/html/index.en.html


24/67


151

ANALYSIS ON RUNS OF DAILY RETURNS IN

ISTANBUL STOCK EXCHANGE

Ahmet ENSOY8Istanbul Stock Exchange, Turkey

[email protected]

Abstract:The aim of this paper is to obtain some statistical properties about runs of daily returns of ISE30, ISE50 and ISE100

indices and compare these results with the empirical stylized facts of developed stock markets. In this manner, all timehistorical daily closing values of these indices are studied and the following observations are obtained: Exponential law fitspretty well for the distribution of both run length and magnitude of run returns. Market is equally likely to go up or go downevery day.

Market depth has improved over recent years. Large magnitudes of run returns are more likely to be seen in positiveruns. As in the developed stock markets, daily returns in Istanbul Stock Exchange dont have significant autocorrelations butabsolute values (i.e. magnitudes) of daily returns exhibit strong and slowly decaying autocorrelations up to several weeks

suggesting volatility clustering. Similar to the absolute daily returns, absolute value of run returns display strong and slowlydecaying autocorrelations which again supporting the existence of volatility clustering. Unlike magnitudes of run returns,lengths of runs dont have significant autocorrelations.

Keywords: stylized facts; return runs; autocorrelation; volatility clustering; stock market efficiency

JEL Classification:C10, C50, G14

1. Introduction

In recent decades, empirical studies on financial time series indicate that if we examine these series froma statistical point of view, the seemingly random variations of asset prices do share some quite nontrivialstatistical properties. Such properties, common across a wide range of instruments, markets and time periods are

called stylized empirical facts (Cont 2001). Researchers have now come to agree on several stylized facts aboutfinancial markets: heavy tails in asset return distributions, absence of autocorrelations in asset returns, volatilityclustering and asymmetry between rises and falls... (Cont (2001); Engle, and Patton (2001); Abergel et al. (2009);Mantegna, and Stanley (2000); Bouchaud, and Potters (2003))

Most of the time, these studies mainly focus on analyzing daily or weekly individual returns of the assets,but sometimes just the sign of these returns can be a useful tool for understanding the market structure (Marumoet al. (2002)). Moreover, instead of individual returns, considering the cumulative returns of specific sequencesmay give us nontrivial information about the market or even help us to reveal some stylized facts about stockprice movements. In this paper, we will conduct a detailed runs analysis similar to work of Gao, and Li (2006) onDow Jones Industrial; first we will analyze the distributions of run lengths and run returns of ISE30, ISE50 andISE100 indices then we will talk about some of the stylized facts observed in Istanbul Stock Exchange, and finallywe will investigate the time correlation of the run lengths and magnitudes of run returns.

2. Analysis

In financial markets, a run is a consecutive series of price movements without a sign reversal, hence apositive (negative) run is an uninterrupted sequence of positive (negative) returns and this run continues until anegative (positive) return comes out.

For example, consider daily closing values of ISE30 index for 12 days from 27.01.1997 to 13.02.1997.

These values are: , 8

The views and opinions in the studies belong to the author and do not necessarily reflect those of the Istanbul StockExchange management and/or its departments

DOI: 10.2478/v10259-012-0010-2


25/67


152

which give us the following daily returns:, , , , , , .Signs of these returns generate the sequence

which contains

three positive and three negative runs. The lengths of the three negative runs are and similarly lengths ofthe three positive runs are: .The cumulative returns (which we will call run returns) obtained in the positive runs are and the cumulative returns obtained in the negative runs are: . 9Runs are simple constructs, but little research has been done on them in finance. Most of these

researches aim to examine the informal efficiency of stocks (however this paper does not have such a purpose)because of the distinctive10 run length of a random walk. Fama (1965) investigated the runs of several stocks,and found little evidence for violations of efficiency based on serial dependence in returns. Similar researcheshave been done by Moore (1978), and Grafton (1981) to test the efficient market hypothesis. Easley, and others(1997) used runs to examine dependence in intra-day data.

We consider daily closing values of ISE30, ISE50 and ISE100 indices from the day they have beenintroduced to the date 24.04.2012. The daily return of an index is found by

where is the index closing value of day .Distribution of the Run Length. Using and the definition of a run, we obtain several information from

the empirical data. Tables 1.a and Tables 1.b show us the longest positive and negative runs, their correspondingdate periods and their returns and Table 2 shows the frequencies of all runs with different lengths;

Table 1.a.All time longest positive runs of ISE30, ISE50 and ISE100

Longest Positive Runs ReturnsISE30 02.09.1997 17.09.1997

12 days0,22338

ISE50

14.01.1997 27.01.199710 days

0,601792

02.11.1999 15.11.199910 days

0.292414

18.08.2005 01.09.200510 days

0,158245

ISE100

13.02.1989 02.03.198912 days

0,62534

15.09.1989 04.10.198912 days 0,29406

13.08.1993 31.08.199312 days

0,39589

Table 1.b.All time longest negative runs of ISE30, ISE50 and ISE100

Longest Negative Runs Returns

9 The possibility is very small but if there happens to be a day with zero return, it is omitted10 For pure random walks, average run length is two


26/67


153

ISE 3009.01.2008 23.01.2008 - 11 days -0,1979914.11.2011 24.11.2011 - 9 days -0,12934

ISE 50 09.01.2008 23.01.2008 - 11 days -0,19919

ISE 100

16.08.1988 01.09.1988 - 12 days -0,0851523.06.1988 07.07.1988 - 11 days -0,1749518.04.1994 02.05.1994 - 11 days -0,37174

09.01.2008 23.01.2008 - 11 days -0,20093

Table 2. Frequencies of runs with different lengths

LENGTH1 2 3 4 5 6 7 8 9 10 11 12 TOTAL

ISE30Positive Run 465 223 136 65 33 19 5 - 4 3 - 1 954Negative Run 483 233 135 59 25 8 7 2 1 - 1 - 954

ISE50Positive Run 375 188 102 55 26 18 3 2 2 2 - - 773Negative Run 390 194 111 45 17 8 6 2 - - 1 - 774

ISE100

Positive Run 633 354 202 108 56 35 12 4 6 9 - 3 1422Negative Run 697 344 210 97 34 18 14 4 1 - 3 1 1423

Considering run length distributions (obtained from Table 2) in Figure 1.a, 1.b and 1.c; we suggest that thenumber of observations of a run with length can be expressed as the following exponential form;

Figure. 1.a: Run length distribution of ISE 30

Figure. 1.b: Run length distribution of ISE 50


27/67


154

Figure. 1.c: Run length distribution of ISE 100

For positive and negative runs of each index, fitting an exponential form of to the data in Table 2gives us the following results;

Ta