We decompose financial return series via wavelets into different time scales to analyse their information content regarding the volatility of the returns. Moreover, we investigate the information of each scale and discuss the decomposition of daily Value-at-Risk (VaR) forecasts. By an extensive empirical analysis, we analyse financial assets in calm and turmoil market times and show that daily ...

This paper proposes and discusses the use of composite marginal likelihoods for Bayesian inference. This approach allows one to deal with complex statistical models in the Bayesian framework, when the full likelihood and thus the full posterior distribution is impractical to compute or even analytically unknown. The procedure is based on a suitable calibration of the composite likelihood that y...

Recently there has been an increasing interest in applying elliptical distributions to risk management. Under weak conditions, Hult and Lindskog (2002) showed that a random vector with an elliptical distribution is in the domain of attraction of a multivariate extreme value distribution. In this paper we study two estimators for the tail dependence function, which are based on extreme value the...

We handle two major issues in applying extreme value analysis to financial time series, bias and serial dependence, jointly. This is achieved by studying bias correction method when observations exhibit weakly serial dependence, namely the β−mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the β−mixin...

Recently there has been growing interest in applying elliptical distributions to risk management. Under certain conditions, Hult and Lindskog show that a random vector with an elliptical distribution is in the domain of attraction of a multivariate extreme value distribution. In this paper we study two estimators for the tail dependence function, which are based on extreme value theory and the ...

uncertainties in industrial environments can cause considerable complexities, during the implementation and management of projects. so, performing a risk management model in such environments has become inevitable. in such circumstances, many risk management models have been introduced in literature, which majorities of them are general, while some of them are developed to be applied to specifi...

The situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it, for example, difficult to find temporal structures with statistical significance in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e., a large number of assets in...