Day of the Week Effects : Recent Evidence from Nineteen Stock Markets

This paper provides international evidence for the presence of the day of the week effects in stock market returns denominated in both local currencies and the US dollars in most of the nineteen countries in the sample for the period July 1993 to July 1998. The observed daily patterns differ for local and dollar returns, the latter being exhibiting lower daily means and higher standard deviations. In local currency terms, a pattern of higher returns around the middle of the week, Tuesday and then Wednesday; and a lower pattern towards the end of the week, Thursday and then Friday, are observed. In dollar terms, a higher pattern occurs around the middle of the week, Wednesday and then Tuesday; and a lower one is observed towards the end of the week, Thursday and then Friday. The lower patterns are more apparent in both cases. Volatility is the highest on Mondays in both local and dollar returns. Local returns have the lowest volatility towards the end of the week, Thursday and Friday, whereas the lowest volatility of dollar returns are observed on Tuesdays. The results have useful implications for international portfolio diversification.Day of the Week Effects, Volatility, International Stock Markets

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 penalise under/over-prediction. When under-predictions are penalised more heavily ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalised more heavily the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters

Forecasting stock market volatility: Further international evidence

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 returns, seasonality and asymmetric conditional volatility in world equity markets

The paper tests four hypotheses at the same time using an autoregressive return-generating process and an asymmetric conditional variance specification, both also including deterministic day of the week dummies. The daily stock index returns from 19 countries are employed to test: (H1) predictable time variation in conditional volatility; (H2) asymmetry in volatility and leverage effect; (H3) effects of estimated volatility on returns; and (H4) day of the week effects on both returns and their volatility. Evidence is provided for predictable time varying daily volatility in all markets among which eight also exhibit a significant leverage effect. There is a significantly positive relationship between returns and their conditional volatility in only three countries. The nature of the day of the week effects on returns and their conditional volatility differs greatly among countries and across days. Thirteen countries exhibit seasonality in either mean returns (seven countries) or volatility (eight countries) or both (two countries). Each day is at least once reported to exhibit significant positive and negative effects in both mean and volatility with the exception that there is no negative effect on mean returns and no positive effect in volatility on Wednesdays.