returns. For multivariate density and portfolio risk forecasting, a drawback of these models is the underlying assumption of … generalized hyperbolic innovations. The novelty of the model is that parameter estimation is conducted by joint maximum likelihood …. An extensive empirical study confirms the COMFORT model’s superiority in terms of multivariate density and Value-at-Risk …

We comprehensively investigate the usefulness of tail risk measures proposed in the literature. We evaluate both the …) performs the best overall. While some other tail risk measures excel at specialized tasks, BT11Q performs well in all tests …

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based …

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise … estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part … techniques in applications of Value-at-Risk prediction in GARCH models …

We analyze the impact of the estimation frequency - updating parameter estimates on a daily, weekly, monthly or … returns for constituents of the S&P 500 index. We assess the implication for one-day ahead 95% and 99% Value-at-Risk (VaR …

Value at risk (VaR) and expected shortfall (ES) are two of the most widely used risk measures in economics and finance … models for the two risk measures. The semiparametric estimations rely on using a class of consistent loss functions recently …