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Tail Dependence Considerations for Cross-Asset Portfolios
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Analys av samband mellan extremutfall för bivariata investeringsportföljer (Swedish)
Abstract [en]

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.

Abstract [sv]

Extrema riskhändelser, avkastningsfördelningar med hög kurtosis och bivariat asymptotiskt beroende är viktiga aspekter vid mitigering och diversifiering av extremutfallsrisk inom finans. Dessa områden är i denna masteruppsats undersökta med hjälp av gränsvärdescopulas och beroende- samt riskmått för extremutfall. Teorin är applicerad på en global aktieportfölj till vilken karaktäristiska marknadsindex för regionala aktiemarknader och andra tillgångsslag lagts till. Det visas att dessa index innehar så kallade stiliserade finansiella egenskaper såsom feta svansar, högre volatilitet förekommande i kluster och en mer normalfördelad avkastningsprofil vid lägre frekventa datapunkter. Resultaten om beroendet för bivariata data vid extremutfall visar på en avsaknad av stark gemensam korrelation, men lämpliga sambandsstrukturer lyckas ändå anpassas till datan. Dessa sambandsstrukturer enligt teorin om extremvärdescopulas används för att dra slutsatser om möjligheter att mitigera och diversifiera extremutfallsrisk för bivariata investeringsportföljer genom att antingen gå lång respektive kort positivt svanskorrelerade instrument, eller genom kvantifiering av diversifieringsnytta vid extremutfall.  

Place, publisher, year, edition, pages
2016.
Series
TRITA-MAT-E, 2016:13
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-185201OAI: oai:DiVA.org:kth-185201DiVA: diva2:925973
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2016-05-03 Created: 2016-04-13 Last updated: 2016-05-03Bibliographically approved

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CiteExportLink to record
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