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.
This paper evaluates the out-of-sample forecasting accuracy of seven models for weekly volatility in fourteen stock markets. Volatility is defined as within-week standard deviation of continuously compounded daily returns on the stock market index of each country for the period December 1987 to December 1997. Total volatility series include 522 weeks. The first half of the sample (261 weeks) 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, and a regression model. We first use the standard 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. We also employ the asymmetric loss functions to penalise under/over-prediction.
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.Stock market volatility, forecasting, forecast evaluation,
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.Conditional heteroscedasticity, volatility clustering, event studies, mergers and acquisitions, announcement effects, abnormal performance,
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