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....

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....

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...

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...

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...

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...

We evaluate the performance of an extensive family of ARCH models in modeling daily Value-at-Risk (VaR) of perfectly diversified portfolios in five stock indices, using a number of distributional assumptions and sample sizes. We find, first, that leptokurtic distributions are able to produce...

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...