Tid: 16 februari 2012 kl 15.15-16.00. (Observera dagen och tiden)

Föredragshållare: Jan Engshagen

Titel: Nothing is normal in finance! (Examensarbete - Master thesis)

Abstract This thesis project is divided in two parts. The first part examines the possibility that correlation matrix estimates based on an outlier sample would contain information about extreme events. The findings are that such methods do not perform better than simple shrinkage methods where robust shrinkage targets are used. They are especially outperformed when it comes to the extreme events, where a shrinkage of the correlation matrix towards the identity matrix seems to give the best results. The second part is about valuation of skewness in marginal distributions and the penalizing of heavy tails. It is shown that it is reasonable to use a degrees of freedom parameter instead of kurtosis and a certain regression parameter instead of skewness due to robustness issues. When minimizing the one period draw-down is our target, the "value" of skewness seems to have a linear relationship with expected returns. Re-valuing of expected returns, in terms of skewness, in the standard Markowitz framework will tend to lower expected shortfall (ES), increase skewness and lower the realized portfolio variance. Penalizing of heavy tails will most times in the same way lower ES, lower kurtosis and realized portfolio variance. The results indicate that the parameters representing higher order moments in some way characterize the assets and also reflect their future behaviour. These properties can be used in a simple optimization framework and give positive effects even on portfolio level.

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