to meet their financial obligations. It is based on classical financial-statement approach, a direct inclusion of risk …

stochastic volatility models. The empirical analysis on stock returns on the US market shows that 1% and 5 % Value-at-Risk …

An efficient and accurate approach is proposed for forecasting Value at Risk [VaR] and Expected Shortfall [ES] measures … in a Bayesian framework. This consists of a new adaptive importance sampling method for Quantile Estimation via Rapid …

the failure of financial modeling. More specifically, current risk models have failed to properly assess the risks … over prevailing models for evaluating stock market risk exposure during distressed market periods. …

Risk neutral densities (RND) can be used to forecast the price of the underlying basis for the option, or it may be … American puts and calls reported on the Chicago Mercentile Exchange for the end of the day. This process implies a risk neutral …

in modern financial risk management techniques like Value at Risk. This paper suggests a regression based density …

for improving our understanding of the risk of hedge funds. At the same time, research has sprung up that applies standard … performance and risk assessment. After constructing Bayesian estimators for alpha-stable distributions in the context of an ARMA …-GARCH time series model with stable innovations, we compare our risk evaluation and prediction results to the predictions of …

This paper discusses methods to quantify risk and uncertainty in macroeconomic forecasts. Both, parametric and non … macroeconometric model of the Bundesbank for Germany. Forecast intervals that integrate judgement on risk and uncertainty are obtained. …