We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...

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 Mixture of t approximations [QERMit]. As a first step the...

We study whether prices of traded options contain information about future extreme market events. Our option-implied conditional expectation of market loss due to tail events, or tail loss measure, predicts future market returns, magnitude, and probability of the market crashes, beyond and above...

Reliable early warning signals are essential for timely implementation of macroeconomic and macro-prudential policies. This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial (systemic) risks. Forecasts are obtained from: (a)...

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

In this paper the out-of-sample prediction of Value-at-Risk by means of models accounting for higher moment dynamics is studied. We consider models differing in terms of skewness and urtosis and, in particular, the GARCHDSK model, which allows for dynamic skewness and kurtosis. The issue of VaR...