CS
C. Song
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This thesis studies the time-varying dependence between financial markets using a Gaussian Copula model. The study focuses on the dynamic Gaussian Copula dependence parameter 𝜌𝑖𝑗,𝑡, which characterizes the dependence relationship between two financial markets after filtering out the dynamic features of their marginal distributions. The thesis combines GARCH-type marginal modelling, probability integral transforms, and likelihood-based estimation of several static and dynamic Gaussian copula specifications.
The first part introduces the theoretical background on time series, copulas, maximum likelihood estimation, Generalized Autoregressive Score (GAS) updating, and GARCH marginal models. The second part uses simulation studies to explain why the observed behaviour of a time series can be affected by marginal mean and volatility dynamics and why copula parameters should be estimated using filtered pseudo-observations rather than the raw return series. The third part performs a Monte Carlo study to assess the finite sample properties of selected specifications of time-varying dependence. It concludes that estimation precision increases with sample size, while the complexity of recursive and score-driven models makes them computationally more demanding.
The empirical analysis uses data from five financial markets: stocks, silver, gold, Bitcoin, and an energy market. The results show that cross-market dependence is not constant over time. Different market pairs are characterized by different dependence specifications. This thesis uses Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to select the dependence specifications. Using AIC, GAS-type models are frequently selected, while BIC more often favours simpler constant or recursive specifications.
Finally, this thesis studies the minimum-variance portfolio allocation and CoVaR-based downside risk analysis by using the estimated dependence paths. The dynamic minimum-variance portfolio results show that time-varying dependence estimates can reduce portfolio volatility compared with an equal-weight portfolio. The ΔCoVaR analysis shows that downside risk transmission changes over time, differs by direction, and varies substantially across market pairs.
Overall, the thesis provides a framework for time-varying Gaussian copula models to analyse dynamic financial market dependence and its implications for portfolio and risk management.
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The first part introduces the theoretical background on time series, copulas, maximum likelihood estimation, Generalized Autoregressive Score (GAS) updating, and GARCH marginal models. The second part uses simulation studies to explain why the observed behaviour of a time series can be affected by marginal mean and volatility dynamics and why copula parameters should be estimated using filtered pseudo-observations rather than the raw return series. The third part performs a Monte Carlo study to assess the finite sample properties of selected specifications of time-varying dependence. It concludes that estimation precision increases with sample size, while the complexity of recursive and score-driven models makes them computationally more demanding.
The empirical analysis uses data from five financial markets: stocks, silver, gold, Bitcoin, and an energy market. The results show that cross-market dependence is not constant over time. Different market pairs are characterized by different dependence specifications. This thesis uses Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to select the dependence specifications. Using AIC, GAS-type models are frequently selected, while BIC more often favours simpler constant or recursive specifications.
Finally, this thesis studies the minimum-variance portfolio allocation and CoVaR-based downside risk analysis by using the estimated dependence paths. The dynamic minimum-variance portfolio results show that time-varying dependence estimates can reduce portfolio volatility compared with an equal-weight portfolio. The ΔCoVaR analysis shows that downside risk transmission changes over time, differs by direction, and varies substantially across market pairs.
Overall, the thesis provides a framework for time-varying Gaussian copula models to analyse dynamic financial market dependence and its implications for portfolio and risk management.
...
This thesis studies the time-varying dependence between financial markets using a Gaussian Copula model. The study focuses on the dynamic Gaussian Copula dependence parameter 𝜌𝑖𝑗,𝑡, which characterizes the dependence relationship between two financial markets after filtering out the dynamic features of their marginal distributions. The thesis combines GARCH-type marginal modelling, probability integral transforms, and likelihood-based estimation of several static and dynamic Gaussian copula specifications.
The first part introduces the theoretical background on time series, copulas, maximum likelihood estimation, Generalized Autoregressive Score (GAS) updating, and GARCH marginal models. The second part uses simulation studies to explain why the observed behaviour of a time series can be affected by marginal mean and volatility dynamics and why copula parameters should be estimated using filtered pseudo-observations rather than the raw return series. The third part performs a Monte Carlo study to assess the finite sample properties of selected specifications of time-varying dependence. It concludes that estimation precision increases with sample size, while the complexity of recursive and score-driven models makes them computationally more demanding.
The empirical analysis uses data from five financial markets: stocks, silver, gold, Bitcoin, and an energy market. The results show that cross-market dependence is not constant over time. Different market pairs are characterized by different dependence specifications. This thesis uses Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to select the dependence specifications. Using AIC, GAS-type models are frequently selected, while BIC more often favours simpler constant or recursive specifications.
Finally, this thesis studies the minimum-variance portfolio allocation and CoVaR-based downside risk analysis by using the estimated dependence paths. The dynamic minimum-variance portfolio results show that time-varying dependence estimates can reduce portfolio volatility compared with an equal-weight portfolio. The ΔCoVaR analysis shows that downside risk transmission changes over time, differs by direction, and varies substantially across market pairs.
Overall, the thesis provides a framework for time-varying Gaussian copula models to analyse dynamic financial market dependence and its implications for portfolio and risk management.
The first part introduces the theoretical background on time series, copulas, maximum likelihood estimation, Generalized Autoregressive Score (GAS) updating, and GARCH marginal models. The second part uses simulation studies to explain why the observed behaviour of a time series can be affected by marginal mean and volatility dynamics and why copula parameters should be estimated using filtered pseudo-observations rather than the raw return series. The third part performs a Monte Carlo study to assess the finite sample properties of selected specifications of time-varying dependence. It concludes that estimation precision increases with sample size, while the complexity of recursive and score-driven models makes them computationally more demanding.
The empirical analysis uses data from five financial markets: stocks, silver, gold, Bitcoin, and an energy market. The results show that cross-market dependence is not constant over time. Different market pairs are characterized by different dependence specifications. This thesis uses Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to select the dependence specifications. Using AIC, GAS-type models are frequently selected, while BIC more often favours simpler constant or recursive specifications.
Finally, this thesis studies the minimum-variance portfolio allocation and CoVaR-based downside risk analysis by using the estimated dependence paths. The dynamic minimum-variance portfolio results show that time-varying dependence estimates can reduce portfolio volatility compared with an equal-weight portfolio. The ΔCoVaR analysis shows that downside risk transmission changes over time, differs by direction, and varies substantially across market pairs.
Overall, the thesis provides a framework for time-varying Gaussian copula models to analyse dynamic financial market dependence and its implications for portfolio and risk management.