Problem 3. (30p) Exercise 7.25 (Fitting copulas to equity return data) writing your own Python code...

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Problem 3. (30p) Exercise 7.25 (Fitting copulas to equity return data) writing your own Python code Take daily log-return data from 2015 to 2023 for the following 10 components of the Dow Jones 30 index: Apple, Cisco, Disney, IBM, Intel, McDonald's, Microsoft, Nike, Proctor & Gamble, and Walmart. - a) Fit both the Gauss copula and the t copula to the log-returns of these data us- ing the pseudo maximum-likelihood approach to take care of the unknown marginal distributions. What are the fitted copula parameters? - b) Compare the Akaike information criteria (AIC numbers) for the two models and confirm that the t copula is clearly the superior model for the dependence structure. - c) Repeat the analysis for monthly log-return data. Problem 3. (30p) Exercise 7.25 (Fitting copulas to equity return data) writing your own Python code Take daily log-return data from 2015 to 2023 for the following 10 components of the Dow Jones 30 index: Apple, Cisco, Disney, IBM, Intel, McDonald's, Microsoft, Nike, Proctor & Gamble, and Walmart. - a) Fit both the Gauss copula and the t copula to the log-returns of these data us- ing the pseudo maximum-likelihood approach to take care of the unknown marginal distributions. What are the fitted copula parameters? - b) Compare the Akaike information criteria (AIC numbers) for the two models and confirm that the t copula is clearly the superior model for the dependence structure. - c) Repeat the analysis for monthly log-return data.