The accuracy of parametric, non-parametric and semi-parametric methods in predicting the one-day-ahead Value-at-Risk (VaR) measure in three types of markets (stock exchanges, commodities and exchange rates) is investigated, both for long and short trading positions. The risk management...

The paper investigates the ability of oil price returns, oil price shocks and oil price volatility to provide predictive information on the state (high/low risk environment) of the US stock market returns and volatility. The disaggregation of oil price shocks according to their origin allows us...

This paper analyses several volatility models by examining their ability to forecast the Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx...

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...

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...

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized...

In order to provide reliable Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, this paper attempts to investigate whether an inter-day or an intra-day model provides accurate predictions. We investigate the performance of inter-day and intra-day volatility models by estimating the...

Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the...

This paper investigates the time-varying conditional correlation between oil price and stock market volatility for six major oil-importing and oil-exporting countries. The period of the study runs from January 2000 until December 2014 and a Diag-BEKK model is employed. Our findings report the...