The paper investigates the time-varying correlation between stock market prices and oil prices for oil-importing and oil-exporting countries. A DCC-GARCH approach is employed to test the above hypothesis based on data from six countries; Oil-exporting: Canada, Mexico, Brazil and Oil-importing:...

This paper examines hedging in South African stock index futures market. The hedge ratios are estimated by six econometric techniques: the standard OLS regression, simple and vector error correction models, the ECM with generalised autoregressive heteroskedasticity (GARCH) as well as...

ARCH models for the daily S&P500 log-returns are estimated, whereas the intraday prices comprise the dataset for an ARFIMAX model. Model’s forecasting performance is statistically superior when the CBOE’s VIX index is incorporated as an explanatory variable.

The main objective of the paper is to test whether post-earnings announcement drift (PEAD) is a consequence of the presence of self-attribution bias in investors’ expectations, regarding permanent earnings. This is the first study to examine empirically this issue, in the sample of Athens...

The EC Directive on financial instruments markets 2004 (MiFID) has introduced a number of order and trade publication obligations imposed on organised exchanges, alternative trading systems (ATS), and the class of broker dealers that execute transactions in shares internally. This article...

Implied volatility index of the S&P500 is considered as a dependent variable in a fractionally integrated ARMA model, whereas volatility measures based on interday and intraday datasets are considered as explanatory variables. The next trading day’s implied volatility forecasts provide...

A number of single ARCH model-based methods of predicting volatility are compared to Degiannakis and Xekalaki’s (2005) poly-model SPEC algorithm method in terms of profits from trading actual options of the S&P500 index returns. The results show that traders using the standardized prediction...

Volatility prediction is the key variable in forecasting the prices of options, value-at-risk and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of asset returns, but also intra-day models, we were able to...

Autoregressive conditional heteroscedasticity (ARCH) models have successfully been applied in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In...

In statistical modeling contexts, the use of one-step-ahead prediction errors for testing hypotheses on the forecasting ability of an assumed model has been widely considered. Quite often, the testing procedure requires independence in a sequence of recursive standardized prediction errors,...