fluctuations not forecasted by Gaussian models. This paper applies a resampling method based on the bootstrap and a bias …-correction step to improve Value-at-Risk (VaR) forecasting ability of the n-EGARCH (normal EGARCH) model and correct the VaR for both … long and short positions. Our aim is to utilize the advantages of this model, but still use the bootstrap resampling method …

fifth risk factor based on realized volatility of index returns. Moreover, instead of using data for stocks of a particular … investable equity indices in the period of 2000-2015. Such an approach is proposed to estimate an equity risk premium for a … years we illustrate the importance of model risk and data overfitting effects when drawing conclusions upon results of …

-frequency intraday returns. It disentangles covariance estimation into variance and correlation components. This allows to estimate … covariance estimation and the jump robustness of the estimator are illustrated in a simulation study. In an application to the …

. These RV budgets provide insight into the risk concentration of a portfolio. Furthermore, the RV budgets can be directly … used in a portfolio strategy, called the equal-risk-contribution allocation strategy. This yields both a higher average …

We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non … immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains …. This method is especially relevant for risk management purposes such as, for example, the computation of portfolio Value at …

benchmarks use the lower partial moment as a risk measure. The lower partial moment, however, doesn’t entirely describe the panic …