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Academy of Economic Studies Doctoral School of Finance and Banking HEAT WAVES VERSUS METEOR SHOWERS An Analysis of Volatility Spillovers in the Romanian Exchange Market. Msc.Student James Omoto Anyanzwa
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Academy of Economic Studies Doctoral School of Finance and BankingHEAT WAVES VERSUS METEOR SHOWERS An Analysis of Volatility Spillovers in the Romanian Exchange Market
Msc.Student James Omoto Anyanzwa
Supervisor Professor Moisăr Altăr
Bucharest, July 2007
Why study Volatility Spillovers?
Edwards(1998) uses augmented GARCH model to analyze possibility of interest rate spillovers from Mexico to Argentina and Chile.The results indicate significant spillovers into Argentina and not Chile.
The following studies focus on volatility spillovers in exchange markets:
Recently there has been renewed interest to investigate volatility spillovers in the exchange markets of emerging and transition economies. Among these studies include;
In all these studies the authors find evidence of volatility spillovers across markets using GARCH model or its extensions.
--Initial data series: Nominal daily exchange rates for Romanian RON/USD,Eurozone EUR/USD, Bulgarian Lev/USD,Hungarian forint/USD and Croatian Kuna/USD.
- Time length: 1:06:2003-12:29:2006,
- A total of 1004 observations for each exchange rate series, after adjusting for missing values.
- 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.
- 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.
-ADF Test showed all the data series were non-stationary and integrated of order one,hence FIRST Differencing could make them stationary.
-Modified form: First Differences of Logarithms of RON/USD (dlogRON/USD),EUR/USD (dlogEUR/USD), Lev/USD(dlogBGN/USD), Forint/USD(dlogHUF/USD) and Kuna/USD (dlogHRK/USD) exchange rates.
Evolution of Exchange rates against the US Dollar:Nominal values Exists evidence of different degrees of volatility in the emerging Europe and the Eurozone.Countries with highly flexible exchange rate regime such as Eurozone are more volatile, compared to those with Comparatively rigid Regimes (Kocenda and Valachy 2005).
-Autoregressive Conditional Heteroskedasticity (ARCH) model, Engle (1982)
-Generalized ARCH (GARCH) model,Bollerslev (1986)
Why ARCH/GARCH Models?
These facts are only appealing in the context of high frequency data such as exchange rates.
is the exchange rate change over two consecutive trading days, is 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 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 terms).
- that the RON/USD volatility is time-varying ,being a function of its own past values (realizations), past endogenous squared residuals, and past cross-market squared volatility changes.
We denote the Exchange rate change (return) in market at time as and given as:
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 sampled at discrete times
Time lags is denoted by
Each exchange rate return ( ) is assumed to be normally distributed with mean zero ,but with a non-constant variance ( ).
The conditional volatility in market is thus described as :
Evidence of Heteroskedasticity (non-constant variance) in exchange rate returns. Large returns follow large returns (of either sign) and small returns follow small returns (of either sign) i.e. volatility clustering.
Hence the exchange rate return series show evidence of the “stylized facts “associated with all financial return series.
GARCH(1,1) Test,AR(1) Vs MA(1) Tests
An AR(p) process has a declining ACF and the PACF is zero for lags greater than p.
An MA(q) process has a ACF that is zero for lags greater than q and PACF that declines exponentially.
The mean equation for each exchange market is thus an MA(1) process.
it means presence of Meteor Showers i.e. conditional volatility in market
is also influenced by volatilities in foreign markets ( )
The significance of and implies presence of both Heat Waves and Meteor Showers effects.
The conditional volatility model for each exchange market is given as:
Estimated results are shown in Table 1.
Model was estimated by use of Bernd,Hall,Hall,and Hausman (1974) (BHHH) algorithms in E-Views 4.0 software.
Table 1 MA(1)-GARCH(1,1) Daily HEAT WAVE ESTIMATION RESULTSWhere implying Romania,Eurozone,Bulgaria,Hungary and Croatian markets respectively
All coefficients in the Romanian variance equation are significant i.e It means both Heat Waves and Meteor Showers are present. The RON/USD exchange rate volatility ( ) is determined by its own past volatility ( )
( ),Bulgaria ( ),Hungary ( ),and Croatia ( )
The Eurozone has the largest spillover volatility coefficient (0.040479).It means exchange rate changes in the Eurozone has greater impact on the RON/USD volatility, compared to Bulgaria, Hungary and Croatian volatilities which have negative spillover coefficients.
However the most powerful determinant of RON/USD volatility is its own previous volatility. The spillover coefficient for Romanian volatility is negative in Bulgarian and Croatian exchange markets and positive in Hungarian exchange markets. This shows insignificant spillovers from Romania into Bulgaria and Croatia and significant spillovers into Hungary.
so good and bad news have different effects.
The size and direction of the shock is determined by the coefficient
The coefficient is negative in the case of asymmetry ,implying bad news/innovation has greater impact on conditional volatility than good new/innovations.
Therefore a statistically positive , coupled with a negative (positive)
Implies that negative innovations in market have a greater impact on volatility of market than positive (negative) innovations. The positive value of coefficient indicates a decreased conditional variance i.e. no asymmetry and vice versa.
Table 3 Heat WavesMA(1)-TARCH(1,1) Conditional Variance Estimation Results.
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