backtest at the 1% risk level for single models and for linear and log pools. We also find that the robust VaR backtest … exhibited by linear and log pools is better than the one of single models at the 5% risk level. Finally, the equally …

loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach …) on the daily closing price of JKII from 1 August 2020-13 August 2021 to predict the price and loss risk of JKII at 16 … 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one …

risk management, stress testing, business planning and so on …

-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student …

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six … distributions and also with the Filtered Historical Simulation (FHS), or the Extreme Value Theory (EVT) methods. Our analysis is …-2009 financial crisis. Using an extensive array of statistical and regulatory risk management loss functions, we find that the …

risk-adjusted portfolio returns after accounting for estimation error and model uncertainty, as evidenced by the …

In 2008, the S&P500 aggregated a loss of 30.16% during three selected days. Unfortunately, benchmark risk measures didn …'t forecast these hazards. Consequently, we witness a growing interest in coherent risk measures, sensitive to high moments and … heavy tail risk. Such measures were proposed by Aumann-Serrano (2007) and Foster-Hart (2008). As a generalization of these …