Spyros Xanthopoulos-Sisinis

TL;DR: In this paper, the authors assess the value-at-risk (VaR) forecasting performance of recently proposed realized volatility models combined with alternative parametric and semi-parametric quantile estimation methods, and find that statistical accuracy and regulatory compliance is essentially improved when they use quantile methods which account for the fat tails and the asymmetry of the innovations distribution.

TL;DR: In this paper, the authors used the Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi-period value-at-risk (VaR) estimates.

TL;DR: In this article, the authors used the Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi-period value-at-risk (VaR) estimates.

TL;DR: In this paper, the Heterogeneous Autoregressive (HAR) Realized Volatility (RV) model is extended in order to account for asymmetric responses to negative and positive shocks occurring at distinct frequencies, as well as, for the long range dependence in the heteroscedastic variance of the residuals.

TL;DR: In this paper, traditional and modern correlation forecasting techniques are compared using standard statistical and risk management loss functions in three portfolios consisting of stocks, bonds and currencies, and they find that GARCH models can better account for the correlation's dynamic structure in the stock and bond portfolios.