In this study we examine multiple models for the dependence structure (copulas) of economic variables based on a latent factor structure. We differentiate between three types of copulas, allowing for a degree of time-varying dependence in increasing order: static, block-dynamic and GAS-dynamic copulas. After an extensive simulation study, the models are applied to daily returns on 50 constituents of the S&P 100 index. We compare various static and dynamic dependence models by their ability to create risk forecasts for the Value at Risk (VaR) and Expected Shortfall (ES). We find significant evidence of asymmetric dependence and tail dependence, implying that dependence is stronger during crises than in booms. We also show that factor copula models provide superior estimates of some risk measures, with the Skew t −t factor copula being most consistent.

Additional Metadata
Keywords Keywords: Copulas, GAS dynamics, Value at Risk, Expected Shortfall, Correlation, (Time-varying) Dependence, Tail dependence, Risk.
Thesis Advisor Kiriliouk, A.A.
Persistent URL hdl.handle.net/2105/42981
Series Econometrie
Citation
Mhand Yamna, R. (2018, August 9). Modeling Dependence and Risk in High Dimensions with Static and Dynamic Factor Copulas. Econometrie. Retrieved from http://hdl.handle.net/2105/42981