Tail Dependence Considerations for Cross-Asset Portfolios
Extreme events, heaviness of log return distribution tails and bivariate asymptotic dependence are important aspects of cross-asset tail risk hedging and diversification. These are in this thesis investigated with the help of threshold copulas, scalar tail dependence measures and bivariate Value-at-Risk. The theory is applied to a global equity portfolio extended with various other asset classes as proxied by different market indices. The asset class indices are shown to possess so-called stylised facts of financial asset returns such as heavy-tailedness, clustered volatility and aggregational Gaussianity. The results on tail dependence structure show on lack of strong joint tail dependence, but suitable bivariate dependence models can nonetheless be found and fitted to the data. These dependence structures are then used when concluding about tail hedging opportunities as defined by highly tail correlated long vs short positions as well as diversification benefits of lower estimated Value-at-Risk for cross-asset portfolios than univariate portfolios.
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