Prediction of Financial Downside-Risk with Heavy-Tailed Conditional Distributions

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Value-at-Risk Prediction: A Comparison of Alternative Strategies (2005, background): “The stable Paretian distributional assumption, while better than both the normal and the t [agreeing with the findings of Mittnik and Paolella (2003)], did not outperform the use of the skewed t, the latter of which is more straightforward to program and much faster to estimate.”
Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions (2011, methodology): “…data sets has allowed the estimation and exploitation of these asymmetrical and heavy-tailed models, and empirical investigation has found that such extensions can be useful in characterizing the pattern of financial returns; see for example Mittnik and Paolella (2003) and Alberg et al. (2008).”
Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions (2009, methodology): “To compare unconditional and conditional in-sample fitted models, we employ three likelihood based goodness-of-fit criteria proposed by Mittnik and Paolella (2003).”