Academy of Economic StudiesAcademy of Economic Studies

Doctoral School of Finance and Banking Doctoral School of Finance and Banking

HEAT WAVES VERSUS METEOR SHOWERSHEAT WAVES VERSUS METEOR SHOWERS

An Analysis of Volatility Spillovers in the Romanian An Analysis of Volatility Spillovers in the Romanian

Exchange Market Exchange Market

Msc.Student James Omoto AnyanzwaMsc.Student James Omoto Anyanzwa Supervisor Professor MoisSupervisor Professor Moisăr Altărăr Altăr

Bucharest, July 2007Bucharest, July 2007

CONTENTSCONTENTS

Importance of cross-market volatility spilloversImportance of cross-market volatility spillovers The objectives of the paperThe objectives of the paper Empirical StudiesEmpirical Studies DataData ModelModel ConclusionsConclusions ReferencesReferences

Importance of Cross-Market volatility SpilloversImportance of Cross-Market volatility Spillovers Volatility can be defined as the risk associated with the ownership Volatility can be defined as the risk associated with the ownership

of a financial asset over a given holding period. It refers to the rate of a financial asset over a given holding period. It refers to the rate at which the price of a financial asset change over time, and it is at which the price of a financial asset change over time, and it is measured either through the standard deviation or variance of the measured either through the standard deviation or variance of the asset return (Taylor 2005)asset return (Taylor 2005)

An important interpretation of spillovers is that of information An important interpretation of spillovers is that of information linkage across markets i.e. the reaction of a market to the arrival of linkage across markets i.e. the reaction of a market to the arrival of new information revealed in the other exchange markets.new information revealed in the other exchange markets.

The new information/news is reflected in price changes in a marketThe new information/news is reflected in price changes in a market Volatility spillovers occurs mostly when economies are related Volatility spillovers occurs mostly when economies are related

through trade and investment (Engle et al 1994,Cappiello,Gerard, through trade and investment (Engle et al 1994,Cappiello,Gerard, ,and Manganeli 2004) i.e. any news about economic fundamentals ,and Manganeli 2004) i.e. any news about economic fundamentals in one country will most likely have an implication for the other in one country will most likely have an implication for the other country.country.

Why study Volatility Spillovers?Why study Volatility Spillovers? Important for pricing of securities within and across markets, Important for pricing of securities within and across markets,

international diversification strategies, hedging strategies and for international diversification strategies, hedging strategies and for regulatory policy.regulatory policy.

Important in evaluating regulatory proposals in order to restrict Important in evaluating regulatory proposals in order to restrict international capital flows (Chan and Hooy 2003).international capital flows (Chan and Hooy 2003).

The Objectives of the PaperThe Objectives of the Paper

To investigate and test two volatility theories:To investigate and test two volatility theories:1.1. Whether exchange rate volatility in Romania is purely determined Whether exchange rate volatility in Romania is purely determined

by domestic factors, which is consistent with Heat Wave by domestic factors, which is consistent with Heat Wave HypothesisHypothesis

2.2. Whether exchange rate volatility in Romania is also influenced by Whether exchange rate volatility in Romania is also influenced by foreign factors such as exchange rate changes in the Eurozone, foreign factors such as exchange rate changes in the Eurozone, which is consistent with the Meteor Showers hypothesis.which is consistent with the Meteor Showers hypothesis.

3.3. Whether there exist any asymmetric response of the Romanian Whether there exist any asymmetric response of the Romanian exchange market to foreign shocks.exchange market to foreign shocks.

4.4. Whether there is any volatility spillovers out of Romania.Whether there is any volatility spillovers out of Romania.

Empirical Studies on Volatility SpilloversEmpirical Studies on Volatility Spillovers Previous studies concentrated mostly on volatility spillovers in Previous studies concentrated mostly on volatility spillovers in

international equity markets e.g.Mervyn and Wadhwani (1990), Hamao, international equity markets e.g.Mervyn and Wadhwani (1990), Hamao, Masulis, and Ng (1990),Lin,Engle,and Ito (1994),King,Sentana,and Masulis, and Ng (1990),Lin,Engle,and Ito (1994),King,Sentana,and Wadhwani(1994). All these studies find evidence of cross-border Wadhwani(1994). All these studies find evidence of cross-border correlation of stock markets and volatility spillovers. correlation of stock markets and volatility spillovers.

Edwards(1998) uses augmented GARCH model to analyze possibility of Edwards(1998) uses augmented GARCH model to analyze possibility of interest rate spillovers from Mexico to Argentina and Chile.The results interest rate spillovers from Mexico to Argentina and Chile.The results indicate significant spillovers into Argentina and not Chile.indicate significant spillovers into Argentina and not Chile.

Lau and Ivaschenko (2002) examine price and volatility spillovers in the Lau and Ivaschenko (2002) examine price and volatility spillovers in the Tech and non-Tech sectors in the US and the Asian countries.Tech and non-Tech sectors in the US and the Asian countries.

The following studies focus on volatility spillovers in exchange The following studies focus on volatility spillovers in exchange markets:markets:

Engle,Ito,and Lin (1990),Bailie and Bollerslev (1990),Engle,Ito,and Lin (1990),Bailie and Bollerslev (1990), Recently there has been renewed interest to investigate volatility Recently there has been renewed interest to investigate volatility

spillovers in the exchange markets of emerging and transition economies. spillovers in the exchange markets of emerging and transition economies. Among these studies include;Among these studies include;

Habib (2002)-uses GARCH model to investigate impact of external factors Habib (2002)-uses GARCH model to investigate impact of external factors on daily exchange rates and interest rates in Hungary, Czech Republic on daily exchange rates and interest rates in Hungary, Czech Republic and Poland.and Poland.

Kocenda (2005)-uses the GARCH model to study the exchange rate Kocenda (2005)-uses the GARCH model to study the exchange rate behavior of the Czech crown which is pegged to a currency basket behavior of the Czech crown which is pegged to a currency basket with an imposed narrow band.with an imposed narrow band.

Pramor and Tamirisa (2006)-studies convergence between the CEECs Pramor and Tamirisa (2006)-studies convergence between the CEECs and Eurozone by use of GARCH methodologyand Eurozone by use of GARCH methodology

Karacadag(2004)-analyses the effect on intervention on the level and Karacadag(2004)-analyses the effect on intervention on the level and volatility of the exchange rate in Mexico and Turkeyvolatility of the exchange rate in Mexico and Turkey

HorvHorvààth(2005)-uses a simple “out-of-sample” approach to predict th(2005)-uses a simple “out-of-sample” approach to predict exchange rate volatility for several CEECs based on Optimum exchange rate volatility for several CEECs based on Optimum Currency Area (OCA)criterion.Currency Area (OCA)criterion.

In all these studies the authors find evidence of volatility spillovers In all these studies the authors find evidence of volatility spillovers across markets using GARCH model or its extensions.across markets using GARCH model or its extensions.

DATADATA--

---- Initial data series: Nominal daily exchange rates for Romanian RON/USD,Eurozone Initial data series: Nominal daily exchange rates for Romanian RON/USD,Eurozone EUR/USD, Bulgarian Lev/USD,Hungarian forint/USD and Croatian Kuna/USD.EUR/USD, Bulgarian Lev/USD,Hungarian forint/USD and Croatian Kuna/USD.

- Time length: 1:06:2003-12:29:2006,- Time length: 1:06:2003-12:29:2006,- A total of 1004 observations for each exchange rate series, after adjusting for missing - A total of 1004 observations for each exchange rate series, after adjusting for missing

values.values. - All the data series are converted into the same currency, the US dollar- All the data series are converted into the same currency, the US dollar- The data series were collected from the websites of each country’s central bank.- The data series were collected from the websites of each country’s central bank.- On July 1,2005 Romania introduced new currency the Ron i.e. 1RON=10,000 - On July 1,2005 Romania introduced new currency the Ron i.e. 1RON=10,000

ROL,hence the ROL data series was converted to RON to ensure uniformity.ROL,hence the ROL data series was converted to RON to ensure uniformity.-ADF Test showed all the data series were non-stationary and integrated of order -ADF Test showed all the data series were non-stationary and integrated of order

one,hence FIRST Differencing could make them stationary.one,hence FIRST Differencing could make them stationary.-Modified form: First Differences of Logarithms of RON/USD -Modified form: First Differences of Logarithms of RON/USD

(dlogRON/USD(dlogRON/USD),EUR/USD (),EUR/USD (dlogEUR/USDdlogEUR/USD), Lev/USD(), Lev/USD(dlogBGN/USDdlogBGN/USD), ), Forint/USDForint/USD(dlogHUF/USD)(dlogHUF/USD) and Kuna/USD ( and Kuna/USD (dlogHRK/USDdlogHRK/USD) exchange rates.) exchange rates.

Evolution of Exchange rates against the US Dollar:Nominal valuesEvolution of Exchange rates against the US Dollar:Nominal values Exists evidence of different degrees of volatility in the emerging Europe Exists evidence of different degrees of volatility in the emerging Europe and the Eurozone.Countries with highly flexible exchange rate regime and the Eurozone.Countries with highly flexible exchange rate regime such as Eurozone are more volatile, compared to those with Comparatively such as Eurozone are more volatile, compared to those with Comparatively rigid Regimes (Kocenda and Valachy 2005). rigid Regimes (Kocenda and Valachy 2005).

.28

.30

.32

.34

.36

.38

.40

2003 2004 2005 2006

RON

1.00

1.05

1.10

1.15

1.20

1.25

1.30

1.35

1.40

2003 2004 2005 2006

EUR

.52

.56

.60

.64

.68

.72

2003 2004 2005 2006

BGN

.000040

.000042

.000044

.000046

.000048

.000050

.000052

.000054

.000056

2003 2004 2005 2006

HUF

.011

.012

.013

.014

.015

.016

2003 2004 2005 2006

HRK

The ModelThe Model

Most popularly used tools for modeling volatility in financial times Most popularly used tools for modeling volatility in financial times series are: series are:

-Autoregressive Conditional Heteroskedasticity (ARCH) model, Engle -Autoregressive Conditional Heteroskedasticity (ARCH) model, Engle (1982)(1982)

-Generalized ARCH (GARCH) model,Bollerslev (1986)-Generalized ARCH (GARCH) model,Bollerslev (1986)

Why ARCH/GARCH Models?Why ARCH/GARCH Models?

These models can capture the non-linear dependence These models can capture the non-linear dependence (heteroskedasticity) inherent in financial time series data i.e. the (heteroskedasticity) inherent in financial time series data i.e. the autocorrelation of absolute or squared returns.autocorrelation of absolute or squared returns.

Financial returns including exchange rates are known to exhibit the Financial returns including exchange rates are known to exhibit the stylized facts attributed to Mandelbrot (1963)i.e. (1) their distributions stylized facts attributed to Mandelbrot (1963)i.e. (1) their distributions are Leptokurtic (2)Non- constant variance (3) volatility clustering.are Leptokurtic (2)Non- constant variance (3) volatility clustering.

These facts are only appealing in the context of high frequency data These facts are only appealing in the context of high frequency data such as exchange rates.such as exchange rates.

GARCH (1,1) ModelGARCH (1,1) Model

TheThe simplest GARCH (1,1) model is given assimplest GARCH (1,1) model is given as : :

(i)(i)

(ii)(ii) The model consist of two components: Mean equation (i) and Variance The model consist of two components: Mean equation (i) and Variance

equation (ii) for the financial asset return. In our case,equation (ii) for the financial asset return. In our case,

is the exchange rate change over two consecutive trading days, is is the exchange rate change over two consecutive trading days, is the number of lags chosen by a certain lag selection criteria under the the number of lags chosen by a certain lag selection criteria under the condition of stationarity e.g. the absolute values of must be less condition of stationarity e.g. the absolute values of must be less than a unit ,the error term, is assumed to be a white noise i.e. mean than a unit ,the error term, is assumed to be a white noise i.e. mean zero, and independent over time (no serial correlation between the error zero, and independent over time (no serial correlation between the error terms).terms).

tt

k

iit rar

1

10

),0(~ 2tt N

212

2110

2 ttt DDD

tr k

""at

Conditions for StationarityConditions for Stationarity

However with financial asset returns, though disturbance terms are However with financial asset returns, though disturbance terms are serially uncorrelated, they are not independent i.e. they are serially uncorrelated, they are not independent i.e. they are heteroskedastic.heteroskedastic.

The constraints ensures that conditional variance The constraints ensures that conditional variance is always positive.is always positive.

While the condition 0 < <1 ensures a positive autocorrelation in the While the condition 0 < <1 ensures a positive autocorrelation in the volatility process, ,and measures volatility persistence with a rate of volatility process, ,and measures volatility persistence with a rate of decay governed by ( D1+D2).The closer the sum of DI and D2 is to one the decay governed by ( D1+D2).The closer the sum of DI and D2 is to one the slower the rate at which shocks on conditional variance in the next period slower the rate at which shocks on conditional variance in the next period dies out. This sum must also be less than one to ensure that the dies out. This sum must also be less than one to ensure that the GARCH(1,1) model is stationary.GARCH(1,1) model is stationary.

The upper limit of D1+D2=1,is the case of an integrated process.The upper limit of D1+D2=1,is the case of an integrated process. The GARCH(1,1) model implies that the current volatility is an The GARCH(1,1) model implies that the current volatility is an

exponentially weighted moving average of past squared returns.exponentially weighted moving average of past squared returns. The model is estimated by maximization of the likelihood functions The model is estimated by maximization of the likelihood functions

because of its non-linearity.i.e iteration procedures.because of its non-linearity.i.e iteration procedures. Stationary GARCH(1,1) model has dynamics that produces reversions of Stationary GARCH(1,1) model has dynamics that produces reversions of

the short run conditional volatility to a constant long run or unconditional the short run conditional volatility to a constant long run or unconditional variance given as:variance given as:

21 DD 2t

0,0,0 210 DDD

2102 1/ DDDt

Model Specification for the Romanian Case:Model Specification for the Romanian Case:

We base our analysis on the work done by Engle,Ito,and Lin (1990), We base our analysis on the work done by Engle,Ito,and Lin (1990), with slight modifications on the original model.with slight modifications on the original model.

Contrary to previous work by Engle’ et al , we use daily exchange rate Contrary to previous work by Engle’ et al , we use daily exchange rate data and eliminate the influence of current foreign volatility on data and eliminate the influence of current foreign volatility on Romanian conditional variance equation.Romanian conditional variance equation.

We use notations RON/USD,EUR/USD,BGN/USD,HUF/USD, and We use notations RON/USD,EUR/USD,BGN/USD,HUF/USD, and HRK/USD to denote Romanian,Eurozone,Bulgarian,Hungarian and HRK/USD to denote Romanian,Eurozone,Bulgarian,Hungarian and Croatian exchange rates against the US Dollar respectively.Croatian exchange rates against the US Dollar respectively.

All exchange rate series are expressed in the US Dollar in order to All exchange rate series are expressed in the US Dollar in order to allow correlations in the disturbance terms.allow correlations in the disturbance terms.

Exchange markets are labeled as Exchange markets are labeled as 11 for Romania, for Romania,2 2 (Eurozone), (Eurozone), 3 3 (bulgaria),(bulgaria),4 4 (Hungary) and (Hungary) and 5 5 (croatia)(croatia)

Our model is thus based on the following assumptions:Our model is thus based on the following assumptions: - that the RON/USD volatility is time-varying ,being a function of its - that the RON/USD volatility is time-varying ,being a function of its

own past values (realizations), past endogenous squared residuals, and own past values (realizations), past endogenous squared residuals, and past cross-market squared volatility changes.past cross-market squared volatility changes.

Continuation:Continuation:

Our sample size, consists of 5 exchange markets i.e. Romania Our sample size, consists of 5 exchange markets i.e. Romania RON/USD) ,Eurozone (EUR/USD), Bulgaria (BGN/USD),Hungary RON/USD) ,Eurozone (EUR/USD), Bulgaria (BGN/USD),Hungary (HUF/USD),and Croatia( HRK/USD)(HUF/USD),and Croatia( HRK/USD)

We denote the Exchange rate change (return) in market at time as We denote the Exchange rate change (return) in market at time as and given as: and given as:

forfor

The time-scale is one day ( in our case), though it may vary The time-scale is one day ( in our case), though it may vary between a minute or even seconds for tick data. Observations “z” are between a minute or even seconds for tick data. Observations “z” are sampled at discrete times sampled at discrete times

Time lags is denoted by Time lags is denoted by

Each exchange rate return ( ) is assumed to be normally distributed with Each exchange rate return ( ) is assumed to be normally distributed with mean zero ,but with a non-constant variance ( ).mean zero ,but with a non-constant variance ( ).

The conditional volatility in market is thus described as :The conditional volatility in market is thus described as :

n

i tti ,

)/log( ,,,,, tititititi

ztz

i

ti ,tih ,

ni ,....3,2,1

Descriptive Statistics for Exchange rate returnsDescriptive Statistics for Exchange rate returns Daily Exchange rate changes ( ) Daily Exchange rate changes ( )

Mean Mean 1.34E-051.34E-05 6.95E-066.95E-06 5.79E-06 5.79E-06 7.00E-067.00E-06 1.34E-061.34E-06

Median Median 0.0001030.000103 7.78E-057.78E-05 2.28E-052.28E-05 -.000339-.000339 0.000000 0.000000

Maximum Maximum 0.0513000.051300 0.0271880.027188 0.0282010.028201 0.0912880.091288 0.0294410.029441

MinimumMinimum -0.055396 -0.055396 -0.022516 -0.022516 -0.029324 -0.029324 -0.061153 -0.061153 -0.038907 -0.038907

Std. Dev. Std. Dev. 0.009285 0.009285 0.0082250.008225 0.0083670.008367 0.0120540.012054 0.0082220.008222

SkewnessSkewness -0.143651-0.143651 0.125610 0.125610 0.028357 0.028357 0.568961 0.568961 0.134730 0.134730

KurtosisKurtosis 5.4879415.487941 3.0114063.011406 3.3984773.398477 8.2637668.263766 4.2689054.268905

ObservationObservation 10021002 10021002 10021002 10021002 10021002

t,1 t,2t,3 t,4 t,5

ti ,

Evidence of Heteroskedasticity (non-constant variance) in exchange rate Evidence of Heteroskedasticity (non-constant variance) in exchange rate returns. Large returns follow large returns (of either sign) and small returns. Large returns follow large returns (of either sign) and small returns follow small returns (of either sign) i.e. volatility clustering.returns follow small returns (of either sign) i.e. volatility clustering.

-.06

-.04

-.02

.00

.02

.04

.06

2003 2004 2005 2006

RON_USD

-.03

-.02

-.01

.00

.01

.02

.03

2003 2004 2005 2006

EUR_USD

-.03

-.02

-.01

.00

.01

.02

.03

2003 2004 2005 2006

BGN_USD

-.04

-.03

-.02

-.01

.00

.01

.02

.03

2003 2004 2005 2006

HRK_USD

-.08

-.04

.00

.04

.08

.12

2003 2004 2005 2006

HUF_USD

interpretationinterpretation

The distribution of all the exchange rate returns are positively skewed The distribution of all the exchange rate returns are positively skewed (except for Romania), have average returns close to 0,and show (except for Romania), have average returns close to 0,and show evidence of fat tails i.e. they are leptokurtic (kurtosis value greater evidence of fat tails i.e. they are leptokurtic (kurtosis value greater than 3)than 3)

Hence the exchange rate return series show evidence of the “stylized Hence the exchange rate return series show evidence of the “stylized facts “associated with all financial return series.facts “associated with all financial return series.

GARCH(1,1) Test,AR(1) Vs MA(1) TestsGARCH(1,1) Test,AR(1) Vs MA(1) Tests

The return series were tested for GARCH effects by use of Engle (1982) The return series were tested for GARCH effects by use of Engle (1982) Lagrange Multiplier(LM) Test. The null hypothesis of homoskedasticty was Lagrange Multiplier(LM) Test. The null hypothesis of homoskedasticty was rejected, implying presence of heteroskedasticity. The GARCH(1,1) effects rejected, implying presence of heteroskedasticity. The GARCH(1,1) effects was tested based on ARCH(2) as proposed by Bollerslev (1986:318) i.e for was tested based on ARCH(2) as proposed by Bollerslev (1986:318) i.e for an ARCH(q) null,the LM test for GARCH(r,q) and ARCH(q+r) an ARCH(q) null,the LM test for GARCH(r,q) and ARCH(q+r) coincide,hence a positive test for ARCH(2) could be an indicative of coincide,hence a positive test for ARCH(2) could be an indicative of GARCH(1,1) process.GARCH(1,1) process.

Using the Autocorrelation and Partial Autocorrelation functions i.e. Using the Autocorrelation and Partial Autocorrelation functions i.e. ACF and PCF,it was discovered that all the exchange rate return series ACF and PCF,it was discovered that all the exchange rate return series could best be described by a Moving Average process of order one –could best be described by a Moving Average process of order one –MA(1).MA(1).

It means the current value of each return series depends on its past It means the current value of each return series depends on its past error terms but not on its previous values in which case it would be an error terms but not on its previous values in which case it would be an autoregressive process of order one-AR(1)autoregressive process of order one-AR(1)

Decision rule:Decision rule:

An AR(p) process has a declining ACF and the PACF is zero for lags An AR(p) process has a declining ACF and the PACF is zero for lags greater than p.greater than p.

An MA(q) process has a ACF that is zero for lags greater than q and PACF An MA(q) process has a ACF that is zero for lags greater than q and PACF that declines exponentially.that declines exponentially.

The mean equation for each exchange market is thus an MA(1) process.The mean equation for each exchange market is thus an MA(1) process.

model:model:

n

forfor = 1,2,3….. and = 1,2,3….. for = 1,2,3….. and = 1,2,3….. for Where and stands for domestic and foreign markets Where and stands for domestic and foreign markets respectively. is the information set available on market on date respectively. is the information set available on market on date The set contains last period’s information about changes in fundamentals from The set contains last period’s information about changes in fundamentals from

market and market market and market is the distribution of , while is the conditional variance for market is the distribution of , while is the conditional variance for market

on date on date is assumed to be normally distributed with mean 0 and non-constant is assumed to be normally distributed with mean 0 and non-constant

variance The conditional variance is viewed as being variance The conditional variance is viewed as being a function of its own past values ( ) , endogenous squared residuals( a function of its own past values ( ) , endogenous squared residuals( ) and squared residuals from foreign markets ( ) ) and squared residuals from foreign markets ( )

),0(~/ ,,, tititi hN

1

1

21,,

21,1,,

n

jtjjitiiitiiiti hh

i

j

n 1n ij i

j

ti , i t

ti ,i

j

tih ,

itti,

tih ,N

1, tih2

1, ti21, tj

Interpretation of model coefficientsInterpretation of model coefficients

The cross-market volatility spillover coefficient is given by The cross-market volatility spillover coefficient is given by The coefficient for is consistent with null hypothesis The coefficient for is consistent with null hypothesis

of Heat Waves effect . It means no volatility spillovers i.e. conditional of Heat Waves effect . It means no volatility spillovers i.e. conditional volatility( ) in market is only determined purely by its own volatility( ) in market is only determined purely by its own previous values and endogenous squared residuals ( )previous values and endogenous squared residuals ( )

When the coefficient is significantly different from zero for When the coefficient is significantly different from zero for it means presence of Meteor Showers i.e. conditional volatility in market it means presence of Meteor Showers i.e. conditional volatility in market is also influenced by volatilities in foreign markets ( )is also influenced by volatilities in foreign markets ( ) The significance of and implies presence of both Heat The significance of and implies presence of both Heat

Waves and Meteor Showers effects.Waves and Meteor Showers effects. The conditional volatility model for each exchange market is given as:The conditional volatility model for each exchange market is given as:

Estimated results are shown in Table 1.Estimated results are shown in Table 1.Model was estimated by use of Bernd,Hall,Hall,and Hausman (1974) (BHHH) Model was estimated by use of Bernd,Hall,Hall,and Hausman (1974) (BHHH)

algorithms in E-Views 4.0 software.algorithms in E-Views 4.0 software.

ji ,0, ji ij

i2

1, titih ,

1, tih

i

ji , ij i

21, tj

ji ,

21,1,, tiiitiiiti hh

Table 1Table 1 MA(1)-GARCH(1,1) Daily HEAT WAVE ESTIMATION RESULTS MA(1)-GARCH(1,1) Daily HEAT WAVE ESTIMATION RESULTS

Where implying Romania,Eurozone,Bulgaria,HungaryWhere implying Romania,Eurozone,Bulgaria,Hungary and Croatian markets respectively and Croatian markets respectively

MarketMarket

Romania Eurozone Bulgaria Hungary Croatia Romania Eurozone Bulgaria Hungary Croatia

LHS Variable (RONM) (EUM) ( BGNM) (HUFM) (HRKMLHS Variable (RONM) (EUM) ( BGNM) (HUFM) (HRKM))

2.38E-06 6.23E-07 4.41E-07 1.22E-05 1.87E-06 2.38E-06 6.23E-07 4.41E-07 1.22E-05 1.87E-06

(9.15E-07) (4.30E-07) ( 3.35E-07) (3.23E-06) (1.87E-06) (9.15E-07) (4.30E-07) ( 3.35E-07) (3.23E-06) (1.87E-06)

0.859904 0.951842 0.957275 0.697609 0.9091510.859904 0.951842 0.957275 0.697609 0.909151

(0.030817) (0.015900) (0.010841) (0.054142) (0.909151)(0.030817) (0.015900) (0.010841) (0.054142) (0.909151)

0.109699 0.035064 0.033378 0.189076 0.0502670.109699 0.035064 0.033378 0.189076 0.050267

(0.025120) (0.009937) (0.008561) (0.044313) (0.050267)(0.025120) (0.009937) (0.008561) (0.044313) (0.050267)

),0(~/ ,,, tititi hN2

1,1,, tiiitiiiti hh

5,4,3,2,1i

ti ,

i

1, tih

21, ti

Discussion of ResultsDiscussion of Results

TheThe results shows a strong GARCH Effect for all the market segments. results shows a strong GARCH Effect for all the market segments. Estimated coefficients in the GARCH term are positive and significant in Estimated coefficients in the GARCH term are positive and significant in all the markets indicating a powerful influence of own volatility on the all the markets indicating a powerful influence of own volatility on the current volatility.current volatility.

The coefficients indicate presence of Heat Wave effects in all the markets The coefficients indicate presence of Heat Wave effects in all the markets i.e. conditional volatility in each market is determined by internal factors.i.e. conditional volatility in each market is determined by internal factors.

The values in parentheses represents the standard errors for each The values in parentheses represents the standard errors for each estimated coefficient.estimated coefficient.

Furthermore ,in line with stationarity conditions we find thatFurthermore ,in line with stationarity conditions we find that

Hence GARCH(1,1) stability conditions are satisfied.Hence GARCH(1,1) stability conditions are satisfied.

1)(,0,0,0 iiiii and

Cross-Market Volatility SpilloversCross-Market Volatility Spillovers

After inclusion of squared residuals/innovations from foreign markets as After inclusion of squared residuals/innovations from foreign markets as additional regressors in the RON/USD BGN/USD,HUF/USD and additional regressors in the RON/USD BGN/USD,HUF/USD and HRK/USD conditional variance equations. the following equations were HRK/USD conditional variance equations. the following equations were estimated:estimated:

The results are presented in Table 2The results are presented in Table 2

21,515

21,414

21,313

21,212

21,1111,11,1 ttttttit hh

21,525

21,424

21,323

21,121

21.2221,222,2 ttttttt hh

21,535

21,434

21,332

21,131

21,3331,333,3 ttttttt hh

21,545

21,343

21,242

21,141

21,4441,444,4 ttttttt hh

21,454

21,353

21,252

21,551

21,5551,555,5 ttttttt hh

Table 2Table 2 MA(1)-GARCH(1,1) Daily Meteor Showers Estimation Results MA(1)-GARCH(1,1) Daily Meteor Showers Estimation Results

MarketMarket

Romania Bulgaria Hungary CroatiaRomania Bulgaria Hungary Croatia

LHS (RONM) ( BGNM ) (HUFM) (HRKM)LHS (RONM) ( BGNM ) (HUFM) (HRKM)

6.70E-06 2.78E-06 5.13E-06 1.14E-06 6.70E-06 2.78E-06 5.13E-06 1.14E-06

(2.05E-06) (4.23E-07) (2.54E-06) (4.20E-07)(2.05E-06) (4.23E-07) (2.54E-06) (4.20E-07)

0.721532 0.894953 0.751864 0.9575870.721532 0.894953 0.751864 0.957587

(0.042273) (0.021409) (0.050665) (0.019708)(0.042273) (0.021409) (0.050665) (0.019708)

RONMVt-1 0.177511 RONMVt-1 0.177511 -0.006957 0.009500 -0.002660-0.006957 0.009500 -0.002660 (0.043760) (0.002390) (0.010146) (0.001322) (0.043760) (0.002390) (0.010146) (0.001322)

EUMV t-1EUMV t-1 0.0404790.040479 0.011512 -0.010089 -0.006486 0.011512 -0.010089 -0.006486 (0.020454) (0.010387) (0.021690) (0.006199)(0.020454) (0.010387) (0.021690) (0.006199)

ti ,

i

),0(~/ ,,, tititi hN2

1

1

1,

21,1,,

t

n

jjitiiitiiiti hh

1, tih

Table 2 ContinuationTable 2 Continuation

Market Romania Bulgaria Hungary CroatiaMarket Romania Bulgaria Hungary Croatia

(RONM) (BGNM) (HUFM) (HRKM)(RONM) (BGNM) (HUFM) (HRKM)

LHSLHS

6.70E-06 2.78E-06 5.13E-06 1.14E-06 6.70E-06 2.78E-06 5.13E-06 1.14E-06

(2.05E-06) (4.23E-07) (2.54E-06) (4.20E-07)(2.05E-06) (4.23E-07) (2.54E-06) (4.20E-07)

0.721532 0.721532 0.894953 0.751864 0.957587 0.894953 0.751864 0.957587

(0.042273) (0.021409) (0.050665) (0.019708)(0.042273) (0.021409) (0.050665) (0.019708)

BGNMVt-1 BGNMVt-1 -0.01495-0.01495 0.042796 0.091602 0.000928 0.042796 0.091602 0.000928

( 0.008078) ( 0.013238) (0.024801) ( 0.004540)( 0.008078) ( 0.013238) (0.024801) ( 0.004540)

HUFMVt-1 HUFMVt-1 --0.0002220.000222 0.001906 0.144792 0.002703 0.001906 0.144792 0.002703

(0.002177) (0.001584) (0.041604) (0.001421) (0.002177) (0.001584) (0.041604) (0.001421)

HRKMVt-1 HRKMVt-1 --0.0146820.014682 0.006662 -0.010958 0.021863 0.006662 -0.010958 0.021863

(0.007964) (0.008747) (0.014092) (0.011740) (0.007964) (0.008747) (0.014092) (0.011740)

ti ,

i

1, tih

NOTES: RONMVt-1, EUMVt-1, BGNMVt-1, HRKMVt-1 stands for one lagged NOTES: RONMVt-1, EUMVt-1, BGNMVt-1, HRKMVt-1 stands for one lagged period volatilities in the Romanian, European Union, Bulgarian ,Hungarian and period volatilities in the Romanian, European Union, Bulgarian ,Hungarian and Croatian markets respectivelyCroatian markets respectively

Discussion of ResultsDiscussion of Results The results indicates that in addition to own volatility, the conditional volatility The results indicates that in addition to own volatility, the conditional volatility

in each of the CEEC markets is influenced by news/innovations coming from at in each of the CEEC markets is influenced by news/innovations coming from at least one of the four other markets.least one of the four other markets.

The model coefficients satisfy the GARCH requirement that The model coefficients satisfy the GARCH requirement that

and and

All coefficients in the Romanian variance equation are significant i.e It means both All coefficients in the Romanian variance equation are significant i.e It means both Heat Waves and Meteor Showers are present. Heat Waves and Meteor Showers are present. The RON/USD exchange rate The RON/USD exchange rate volatility ( ) is determined by its own past volatility ( )volatility ( ) is determined by its own past volatility ( )

0,0,0 iii 1 ii

th ,1 1,1 th

And also by exchange rate changes in the foreign markets i.e. Eurozone And also by exchange rate changes in the foreign markets i.e. Eurozone ( ),Bulgaria ( ),Hungary ( ),and Croatia ( )( ),Bulgaria ( ),Hungary ( ),and Croatia ( )

The Eurozone has the largest spillover volatility coefficient (The Eurozone has the largest spillover volatility coefficient (0.0404790.040479).It ).It means exchange rate changes in the Eurozone has greater impact on means exchange rate changes in the Eurozone has greater impact on the RON/USD volatility, compared to Bulgaria, Hungary and Croatian the RON/USD volatility, compared to Bulgaria, Hungary and Croatian volatilities which have negative spillover coefficients.volatilities which have negative spillover coefficients.

However the most powerful determinant of RON/USD volatility is its However the most powerful determinant of RON/USD volatility is its own previous volatility. The spillover coefficient for Romanian own previous volatility. The spillover coefficient for Romanian volatility is negative in Bulgarian and Croatian exchange markets and volatility is negative in Bulgarian and Croatian exchange markets and positive in Hungarian exchange markets. This shows insignificant positive in Hungarian exchange markets. This shows insignificant spillovers from Romania into Bulgaria and Croatia and significant spillovers from Romania into Bulgaria and Croatia and significant spillovers into Hungary.spillovers into Hungary.

21,2 t 2

1,3 t 21,4 t 2

1,5 t

MA(1)-TARCH(1,1) ModelMA(1)-TARCH(1,1) Model

We employ the Threshold GARCH (1,1) model of We employ the Threshold GARCH (1,1) model of Glosten,Jagannathan,and Runkle (1993) in order to capture any Glosten,Jagannathan,and Runkle (1993) in order to capture any asymmetry in the foreign shocks impacting on Romanian RON/USD asymmetry in the foreign shocks impacting on Romanian RON/USD volatility. The model incorporates a leverage term that allows for the volatility. The model incorporates a leverage term that allows for the asymmetric effects of good and bad news. A similar approach was used asymmetric effects of good and bad news. A similar approach was used by Savva,Osborn and Gill(2005) to examine asymmeric volatility by Savva,Osborn and Gill(2005) to examine asymmeric volatility spillovers and correlations between the US and European Stock spillovers and correlations between the US and European Stock markets(New York,London,Franfurt and Paris Stock markets).markets(New York,London,Franfurt and Paris Stock markets).

Hence drawing on the work by Savva et al (2005) we introduce a Hence drawing on the work by Savva et al (2005) we introduce a multiplicative dummy as an indicator function to check for any multiplicative dummy as an indicator function to check for any statistical difference in the event of negative shocks.statistical difference in the event of negative shocks.

Daily Heat Waves MA(1)- TARCH(1,1) model SpecificationDaily Heat Waves MA(1)- TARCH(1,1) model Specification

Cross-market TARCH(1,1) model Cross-market TARCH(1,1) model

Where and are domestic and foreign markets respectively.Where and are domestic and foreign markets respectively.

is the multiplicative dummy variable. It assumes the value of one in the is the multiplicative dummy variable. It assumes the value of one in the case of negative shocks (i.e ) and zero in the case of a positive case of negative shocks (i.e ) and zero in the case of a positive shock (i.e ) shock (i.e )

so good and bad news have different effects.so good and bad news have different effects.

Spillovers are captured by the coefficient for Spillovers are captured by the coefficient for

][ 12

1,,2

1,,2

1,1,, ttjjitjjitiiitiiiti hh

][ 12

1.2

1,1,, ttiitiitiiiti hh

i j

1t02

1 t021 t

ji, ij

Interpretation of coefficientsInterpretation of coefficients

The size and direction of the shock is determined by the coefficientThe size and direction of the shock is determined by the coefficient

The coefficient is negative in the case of asymmetry ,implying bad The coefficient is negative in the case of asymmetry ,implying bad news/innovation has greater impact on conditional volatility than good news/innovation has greater impact on conditional volatility than good new/innovations.new/innovations.

Therefore a statistically positive , coupled with a negative (positive)Therefore a statistically positive , coupled with a negative (positive)

Implies that negative innovations in market have a greater impact on Implies that negative innovations in market have a greater impact on volatility of market than positive (negative) innovations. The volatility of market than positive (negative) innovations. The positive value of coefficient indicates a decreased conditional positive value of coefficient indicates a decreased conditional variance i.e. no asymmetry and vice versa.variance i.e. no asymmetry and vice versa.

ji , jj

i

Table 3Table 3 Heat Waves Heat WavesMA(1)-TARCH(1,1) Conditional Variance Estimation Results.MA(1)-TARCH(1,1) Conditional Variance Estimation Results.

MarketMarket

Romania Eurozone Bulgaria Hungary CroatiaRomania Eurozone Bulgaria Hungary Croatia

LHS Variable (RONM) (EUM) (BGNM) (HUFM) (HRKM)LHS Variable (RONM) (EUM) (BGNM) (HUFM) (HRKM)

2.04E-06 7.82E-07 4.03E-07 1.35E-05 2.23E-06 2.04E-06 7.82E-07 4.03E-07 1.35E-05 2.23E-06

(8.56E-07) (4.76E-07) (3.29E-07) (3.43E-06) (1.28E-06)(8.56E-07) (4.76E-07) (3.29E-07) (3.43E-06) (1.28E-06)

0.873640 0.947370 0.959368 0.678284 0.894383 0.873640 0.947370 0.959368 0.678284 0.894383

(0.02848) (0.019215) (0.011492) (0.057261) (0.045726) (0.02848) (0.019215) (0.011492) (0.057261) (0.045726)

0.089144 0.020558 0.038922 0.094310 0.021706 0.089144 0.020558 0.038922 0.094310 0.021706

(0.053441) ( 0.016782) (0.015929) (0.059422) (0.048313)(0.053441) ( 0.016782) (0.015929) (0.059422) (0.048313)

0.022579 0.032514 -0.013890 0.223765 0.0768570.022579 0.032514 -0.013890 0.223765 0.076857

(0.095092(0.095092) ) (0.029151) (0.026030) (0.106047) (0.074028) (0.029151) (0.026030) (0.106047) (0.074028)

ti ,

i

),0(~/ ,,, tititi hN)( 1

21,

21,1,, ttiiitiiitiiiti hh

1, tih

][ 12

1, tti

21, ti

For implying For implying Romanian,Eurozone, ,Bulgarian,Hungarian and Croatian markets Romanian,Eurozone, ,Bulgarian,Hungarian and Croatian markets respectively.respectively.

is the Leverage term(indicator function) in the is the Leverage term(indicator function) in the TARCH(1,1) model.TARCH(1,1) model.

The coefficient is positive for all the markets except The coefficient is positive for all the markets except Bulgaria,meaning no asymmetry in domestic shocks affecting Bulgaria,meaning no asymmetry in domestic shocks affecting Romanian,Eurozone,Hungarian and Croatian market volatilities.it Romanian,Eurozone,Hungarian and Croatian market volatilities.it means both good and bad news/innovations have the same effect on means both good and bad news/innovations have the same effect on conditional volatilities. The coefficient in the case of Bulgarian conditional volatilities. The coefficient in the case of Bulgarian market indicting presence of asymmetry i.e bad news/innovations has market indicting presence of asymmetry i.e bad news/innovations has greater impact on Bulgarian conditional volatility than good news.greater impact on Bulgarian conditional volatility than good news.

,5,4,3,2,1i

][ 12

1, tti

0

Cross-market MA(1)-TGARCH(1,1) for Romania Exchange MarketCross-market MA(1)-TGARCH(1,1) for Romania Exchange Market

A cross-market TGARCH(1,1) was estimated for the Romanian exchange A cross-market TGARCH(1,1) was estimated for the Romanian exchange market. To determine the nature of each foreign shock, different shocks were market. To determine the nature of each foreign shock, different shocks were incorporated into the Romanian conditional variance equation one at a time incorporated into the Romanian conditional variance equation one at a time because introducing all the shocks into the equation makes it impossible to because introducing all the shocks into the equation makes it impossible to determine the asymmetry of each shock.determine the asymmetry of each shock.

For 1,2,3,4,5 imply Romanian,Eurozone,Bulgarian,Hungarian and For 1,2,3,4,5 imply Romanian,Eurozone,Bulgarian,Hungarian and Croatian markets respectively.Croatian markets respectively.

21,2121

21,212

21,1111,111,1 ][ tttttt hh

21,3131

21,313

21,1111,111,1 ][ tttttt hh

21,4141

21,414

21,1111,111,1 ][ tttttt hh

21,5151

21,515

21,1111,111,1 ][ tttttt hh

Romanian Cross-market MA(1)-TARCH(1,1) Estimation ResultsRomanian Cross-market MA(1)-TARCH(1,1) Estimation Results

Where Eurozone,Bulgaria,Hungary and Croatian marketsWhere Eurozone,Bulgaria,Hungary and Croatian markets

MarketMarket

LHSLHS

RomaniaRomania

(RONM)(RONM)

RomaniaRomania

(RONM)(RONM)

RomaniaRomania

(RONM)(RONM)

RomaniaRomania

(RONM)(RONM)

6.69E-06 6.69E-06 0.673744 0.673744

6.56E-06 6.56E-06 0.752382 0.752382

1.93E-06 1.93E-06 0.884923 0.884923

3.03E-06 3.03E-06 0.851457 0.851457

RONMVt-1RONMVt-1

EUMVt-1EUMVt-1

BGNMVt-1BGNMVt-1

HUFMVt-1HUFMVt-1

HRKMVt-1HRKMVt-1

0.240701 0.240701

0.0411790.041179

-0.093502-0.093502

0.1871740.187174

-0.009453-0.009453

-0.038191-0.038191

0.078582 0.078582

-0.001314-0.001314

0.029870.02987

0.106635 0.106635

-0.004932-0.004932

0.0137490.013749

),0(~ ,, titi hN2

1,112

1,,2

1.1111,111,1 ][ tjjttjjittt hh

5,4,3,2j

t,1

11,1 th

][ 12

1, ttj

Discussion of Estimated ResultsDiscussion of Estimated Results

The results shows that volatility transmission mechanism for the The results shows that volatility transmission mechanism for the Eurozone and Bulgarian exchange markets is asymmetric i.e. the Eurozone and Bulgarian exchange markets is asymmetric i.e. the coefficients measuring asymmetry is signficant for the two coefficients measuring asymmetry is signficant for the two markets,reinforcing the assertion that bad news increases volatility more markets,reinforcing the assertion that bad news increases volatility more than the good news.than the good news.

Thus a negative innovation in the Eurozone and Bulgarian markets is Thus a negative innovation in the Eurozone and Bulgarian markets is estimated to increase volatility in the Romanian market by 0.093502 and estimated to increase volatility in the Romanian market by 0.093502 and 0.0381910.038191 times respectively than that of a positive innovation of the same times respectively than that of a positive innovation of the same magnitude.magnitude.

The coefficient is positive in the case of Hungarian and Croatian The coefficient is positive in the case of Hungarian and Croatian shocks i.e both good and bad news/innovations from these markets have shocks i.e both good and bad news/innovations from these markets have the same impact on the Romanian RON/USD volatility.the same impact on the Romanian RON/USD volatility.

ConclusionConclusion

In this paper we used interdaily nominal exchange rate data to analyze In this paper we used interdaily nominal exchange rate data to analyze volatility spillovers into the Romanian exchange market from the Eurozone and volatility spillovers into the Romanian exchange market from the Eurozone and a group of selected CEECs exchange markets. a group of selected CEECs exchange markets. The analysis was based on two The analysis was based on two alternative GARCH (1, 1) and TARCH (1, 1) specifications. alternative GARCH (1, 1) and TARCH (1, 1) specifications. The symmetric The symmetric GARCH approach assumes that the impact of a shock on conditional volatility is GARCH approach assumes that the impact of a shock on conditional volatility is the same, regardless of whether the shock is positive or negative. Alternatively, the same, regardless of whether the shock is positive or negative. Alternatively, the TARCH model takes into account the asymmetric effects, and assumes that the TARCH model takes into account the asymmetric effects, and assumes that the impact of a shock on conditional volatility is not the same but it is determined the impact of a shock on conditional volatility is not the same but it is determined by the sign of the shock. by the sign of the shock.

Using the two approaches, we estimated the conditional variance for each Using the two approaches, we estimated the conditional variance for each exchange rate return series and tested for cross-market volatility spillovers. Our exchange rate return series and tested for cross-market volatility spillovers. Our main interest was to find whether exchange rate changes in foreign markets have main interest was to find whether exchange rate changes in foreign markets have any significant influence on the Romanian RON/USD volatility and whether any significant influence on the Romanian RON/USD volatility and whether leverage effects are present.leverage effects are present.

First we tested each exchange rate return series for ARCH effects using First we tested each exchange rate return series for ARCH effects using Engle’s Lagrange Multiplier (LM) Test, and then implemented an identification Engle’s Lagrange Multiplier (LM) Test, and then implemented an identification test using Autocorrelation and Partial autocorrelation functions to determine the test using Autocorrelation and Partial autocorrelation functions to determine the structure for each return series. All the return series structure for each return series. All the return series (RON/USD,EURO/USD,BGN/USD,HUF/USD and HRK/USD) were found to (RON/USD,EURO/USD,BGN/USD,HUF/USD and HRK/USD) were found to contain GARCH(1,1) effects and were best described by the moving average contain GARCH(1,1) effects and were best described by the moving average process of order one i.e MA(1).process of order one i.e MA(1).

Our findings provide support for the presence of both Heat Waves and Our findings provide support for the presence of both Heat Waves and Meteor Showers on the Romanian Exchange market. It means Romanian Meteor Showers on the Romanian Exchange market. It means Romanian RON/USD volatility is determined both by its own previous volatility (i.e. RON/USD volatility is determined both by its own previous volatility (i.e. Heat Wave hypothesis) and by exchange rate changes in foreign markets Heat Wave hypothesis) and by exchange rate changes in foreign markets (i.e. Meteor Showers hypothesis).These findings also indicate that the (i.e. Meteor Showers hypothesis).These findings also indicate that the RON/USD volatility responds asymmetrically to news from some selected RON/USD volatility responds asymmetrically to news from some selected markets. In particular, we find that news/innovations from the Eurozone markets. In particular, we find that news/innovations from the Eurozone market has a significant influence on the RON/USD volatility, and they are market has a significant influence on the RON/USD volatility, and they are also highly asymmetric. This means bad/negative news from the Eurozone also highly asymmetric. This means bad/negative news from the Eurozone exchange market has a larger impact on Romanian RON/USD volatility exchange market has a larger impact on Romanian RON/USD volatility than the good/positive news. than the good/positive news.

Our findings also indicate that the impact of exchange rate changes in Our findings also indicate that the impact of exchange rate changes in Bulgaria, Hungary and Croatia on the Romanian RON/USD volatility is Bulgaria, Hungary and Croatia on the Romanian RON/USD volatility is insignificant. With the exception of Bulgaria, we find that news/innovations insignificant. With the exception of Bulgaria, we find that news/innovations from these CEECs markets, though insignificant, are highly symmetric, from these CEECs markets, though insignificant, are highly symmetric, implying both bad and good news will have the same impact on the implying both bad and good news will have the same impact on the RON/USD volatility. However our results indicate that the Heat Wave RON/USD volatility. However our results indicate that the Heat Wave hypothesis is very powerful in determing the RON/USD volatility than the hypothesis is very powerful in determing the RON/USD volatility than the Meteor Showers. It means Romanian RON/USD volatility is determined Meteor Showers. It means Romanian RON/USD volatility is determined mostly by its own previous values than external factors.mostly by its own previous values than external factors.

Our results conforms to Pramor and Tamirisa (2006) findings that Our results conforms to Pramor and Tamirisa (2006) findings that volatility transmits from developed to emerging markets, and that smaller, volatility transmits from developed to emerging markets, and that smaller, less developed markets are likely to be more sensitive to transmitted shocks. less developed markets are likely to be more sensitive to transmitted shocks. These findings are important to investors in formulation of trading These findings are important to investors in formulation of trading strategies, risk management and to monetary authorities especially in the strategies, risk management and to monetary authorities especially in the formulation of monetary policies. It is also important for Romanian policy formulation of monetary policies. It is also important for Romanian policy makers to understand the sources of volatility in the exchange market makers to understand the sources of volatility in the exchange market particularly as the country hopes to join the European Exchange rate particularly as the country hopes to join the European Exchange rate management mechanism 11 (ERM11) in a bid to adopt the euro as its management mechanism 11 (ERM11) in a bid to adopt the euro as its official currency sometimes in the future. Our study was restricted to official currency sometimes in the future. Our study was restricted to determining the sources of volatility shocks; however questions related to the determining the sources of volatility shocks; however questions related to the main causes of volatility shocks remain an area of further research.main causes of volatility shocks remain an area of further research.

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