Copula modeling for Portfolio Return Analysis

Sammanfattning: In this thesis, we investigate the advantages of using high-dimensional copula modeling to understand the riskiness of portfolio investments and to more realistically estimate future portfolio values. Our approach involves benchmarking some pre-determined fitted copulas to the 0.05-quantile, the Tail Conditional Expectation, and the probability of negative returns for each portfolio. We find that the two R-Vine copula models used in this study provide good estimations of the distribution of portfolio values for the 1-month time frame, the shortest we consider in this thesis, most probably due to their flexibility and ability to represet a diverse array of dependence structures. However, for longer time frames (1 year or more), the Clayton copula appears to be a more suitable model. It aligns more closely with market behaviour due to its capacity of capturing lower tail dependence. In conclusion, we argue that by employing the right copula model, in our case the Clayton copula, we obtain a more realistic view on the distribution of the future portfolio values.