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...

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...

The EC Directive on Financial Instruments Markets (MiFID) has introduced a number of order and trade publication obligations imposed on organized exchanges, Alternative Trading Systems (ATS), and the class of broker dealers that execute transactions in shares internally. This article...

The volatility prediction is the most important issue in finance, as it is the key ingredient variable in forecasting the prices of options, the VaR number and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of...

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

Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed 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...

We evaluate the performance of symmetric and asymmetric ARCH models in forecasting one-day-ahead Value-at-Risk (VaR) and realized intra day volatility of two equity indices in the Athens Stock Exchange (ASE). Under the framework of three distributional assumptions, we find out that the most...

Recently risk management has become a standard prerequisite for all financial institutions. Value-at-Risk is the main tool of reporting to the bank regulators the risk that the financial institutions face. As it is essential to estimate it accurately, numerous methods have been proposed in order...

We evaluate the performance of symmetric and asymmetric ARCH models in forecasting one-day-ahead Value-at-Risk (VaR) and realized intra-day volatility of two equity indices in the Athens Stock Exchange (ASE). Under the framework of three distributional assumptions, we find out that the most...

Most of the methods used in the ARCH literature for selecting the appropriate model are based on evaluating the ability of the models to describe the data. An alternative model selection approach is examined based on the evaluation of the predictability of the models in terms of standardized...