TITLE:
An Econometric Approach to Incorporating Non-Normality in VaR Measurement
AUTHORS:
Victor Gumbo, Simiso Siziba
KEYWORDS:
VaR, Stylised Facts, Volatility Clustering, Leptokurtic Returns, Converging Correlation
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.6 No.1,
February
25,
2016
ABSTRACT: Following the recent financial crises, there has been a proliferation of new risk management and portfolio construction approaches. These approaches all endeavour to better quantify and manage risk by accounting for the stylised facts of financial time series mainly heavy and skewed tails, volatility clustering and converging correlations. Capturing all these stylised facts in a coherent framework has proved to be an elusive and knotty task. We here propose a pure econometric framework that captures all the stylised facts satisfactorily. We use three data sets to show how the approach is implemented in VaR forecasting and correlation analysis. We show how an investment portfolio can be constructed in order to optimise reserve capital holding. The approach employed is linear programming (LP) computable, satisfies second degree stochastic dominance and outperforms the general mean/VaR quadratic optimisation to arrive at efficient asset allocation.