Ercan Balaban

Day of the week effects: new evidence from an emerging stock market

The primary objective is to investigate day of the week effects in an emerging stock market of a developing country, namely Turkey. Empirical results verify that although day of the week effects are present in Istanbul Securities Exchange Composite Index (ISECI) return data for the period January 1988 to August 1994, these effects change in direction and magnitude through time.

Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of an Exchange Rate

The relative out-of-sample forecasting quality of symmetric and asymmetric conditional volatility models of an exchange rate differs according to the symmetric and asymmetric evaluation criteria as well as a regression-based test of efficiency. Both symmetric and asymmetric forecast competitors of currency volatility are biased and systematically overpredict volatility.

Stock returns and volatility: empirical evidence from fourteen countries

This is a pioneering effort to test in 14 countries the relationship between stock market returns and their forecast volatility derived from the symmetric and asymmetric conditional heteroscedasticity models. Both weekly and monthly returns and their volatility are investigated. An out-of-sample testing methodology is employed using volatility forecasts instead of investigating the relation between stock returns and their in-sample volatility estimates. Expected volatility is derived from the ARCH(p), GARCH(1,1), GJR-GARCH(1,1) and EGARCH(1,1) forecast models. Expected volatility is found to have a significant negative or positive effect on country returns in a few cases. Unexpected volatility has a negative effect on weekly stock returns in six to seven countries and on monthly returns in nine to eleven countries depending on the volatility forecasting model. However, it has a positive effect on weekly and monthly returns in none of the countries investigated. It is concluded that the return variance may not be an appropriate measure of risk.

Forecasting stock market volatility: Further international evidence

Abstract This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.

A note on the efficiency of financial markets in a developing country

Semi-strong efficiency in the stock market, the foreign exchange market and the interbank money market in Turkey is investigated by using the direct Granger causality tests. Significant deviations are reported from the efficient market hypothesis (EMH) with respect to changes in market liquidity in all these markets for the period of January 1989 to July 1995. It is also found that these markets are pairwise interdependent. However, market liquidity cannot be predicted by using developments in the financial market. Possible implications for domestic and foreign investors and for monetary policy makers are discussed.

Forecasting Stock Market Volatility: Evidence From Fourteen Countries

This paper evaluates the out-of-sample forecasting accuracy of eleven models for weekly and monthly volatility in fourteen stock markets. Volatility is defined as within-week (within-month) standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. We first use the standard (symmetric) loss functions to evaluate the performance of the competing models: the mean error, the mean absolute error, the root mean squared error, and the mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. We also employ the asymmetric loss functions to penalize under/over-prediction. When under-predictions are penalized more heavily ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.

Volatility clustering and event-induced volatility: Evidence from UK mergers and acquisitions

Abstract The paper describes simultaneous tests of the effects of announcements of UK mergers and acquisitions on both the mean and conditional volatility functions for UK bidder firms. Unlike previous research, the entire data set is utilized, thus avoiding researcher-chosen event periods. The cross-sectional test statistics for 745 firms show that the announcement day returns are significantly negative and the conditional volatility decreases. Results suggest that the event studies should incorporate firm-specific time-varying volatility into their abnormal return generating processes and into the tests calibrating the significance of both abnormal return and abnormal volatility around an event.

A note on return distribution of UK stock indices

The aim of this paper is to provide empirical evidence on the statistical distributions of returns on 32 UK sector indices as well as the FTSE-All and the FTSE-100 indices. These data are modelled for several holding periods, ranging from one day to one quarter, using symmetric stable Paretian distributions and their characteristic exponents are estimated. Numerical results suggest that both short and long horizon returns are non-normal and that deviation from normality is stronger for short horizon returns, with the exception of few sectors. In sum, these results suggest that asset pricing and risk management models, among others, should be modified to take into account departures form normality.

Forecasting Exchange Rate Volatility

The relative out-of-sample forecasting quality of symmetric and asymmetric conditional volatility models of an exchange rate differs according to the symmetric and asymmetric evaluation criteria. Both symmetric and asymmetric forecast competitors of currency volatility are biased and systematically overpredict volatility.