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Value At Risk Estimation Using Extreme Value Theory, Abhay K. Singh, David E. Allen, Robert J. Powell
Value At Risk Estimation Using Extreme Value Theory, Abhay K. Singh, David E. Allen, Robert J. Powell
Research outputs 2011
A common assumption in quantitative financial risk modelling is the distributional assumption of normality in the asset’s return series, which makes modelling easy but proves to be inefficient if the data exhibit extreme tails. When dealing with extreme financial events like the Global Financial Crisis of 2007-2008 while quantifying extreme market risk, Extreme Value Theory (EVT) proves to be a natural statistical modelling technique of interest. Extreme Value Theory provides well established statistical models for the computation of extreme risk measures like the Return Level, Value at Risk and Expected Shortfall. In this paper we apply Univariate Extreme Value Theory …
Evaluating Extremal Dependence In Stock Markets Using Extreme Value Theory, Abhay K. Singh, David E. Allen, Robert J. Powell
Evaluating Extremal Dependence In Stock Markets Using Extreme Value Theory, Abhay K. Singh, David E. Allen, Robert J. Powell
Research outputs 2011
Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst others. Extreme Value Theory (EVT) that provides well established methods for univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. This paper uses nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both right and left tails of …