type="main" xml:id="sjpe12049-abs-0001" <title type="main">Abstract</title> <p>This article investigates the time-varying correlation between the EU12-wide business cycle and the initial EU12 member-countries based on Scalar-BEKK and multivariate Riskmetrics model frameworks for the period 1980–2012. The paper provides...</p>

Autoregressive fractionally integrated moving average (ARFIMA) and heterogeneous autoregressive (HAR) models are estimated and their ability to predict the one-trading-day-ahead CAC40 realized volatility is investigated. In particular, this paper follows three steps: (i) The optimal sampling...

Purpose – The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically...

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

Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions and for all types of financial assets. However, they have not succeeded yet as the testing...

Purpose – Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one-day-ahead value-at-risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions....

Purpose – The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically...

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 the indices. The ARFIMAX model with time-varying conditional heteroskedasticity is the best...

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, an asymmetric Autoregressive Conditional Heteroscedasticity (ARCH) model...