credit risk. The specification effect can lead to Value-at-Risk (VaR) reductions in the range of 3 percent to 47 percent …

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing … fat-tails) and leverage effects translates in more accurate VaR measure. …

these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing … fat-tails) and leverage effects translates in more accurate VaR measure. …

forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping … prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S … accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR …

evaluate the distributed volatility is offered. Distributed volatility, however, as VaR, helps to evaluate the positive and … negative part of volatility, but unlike VaR, describes volatility dynamics. So it allows forecast calculation of the financial …

performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test … that VaR forecasts at 90 % and 95% have desirable properties. Regarding 99% VaR forecasts, We find significant evidence …

several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and …