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...

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period Value-at-Risk (VaR) and Expected Shortfall (ES) across 20 stock indices worldwide. The dataset is comprised of daily data covering...

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time-varying conditional heteroscedasticity is the best performing...

Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an asymmetric Autoregressive Conditional Heteroscedasticity...

The time-varying correlation between oil prices returns and European industrial sector indices returns, considering the origin of the oil price shock, is investigated. A time-varying multivariate heteroskedastic framework is employed to test the above hypothesis based on data from 10 European...

The paper constructs measures of intra-day realized volatility for 17 European and USA stock indices. We utilize a model-free de-noising method by assembling the realized volatility in sampling frequency selected according to the volatility signature plot which minimizes the micro-structure...

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...