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Exercise

Pick a winner based on log-likelihood

In this exercise, you will practice using log-likelihood to choose a model with the best fit.

GARCH models use the maximum likelihood method to estimate parameters. In general, the bigger the log-likelihood, the better the model since it implies a bigger probability of having observed the data you got.

Two GARCH models with different distribution assumptions have been defined and fitted with the S&P 500 return data. The normal distribution GARCH is saved in normal_result, and the skewed Student's t-distribution GARCH is saved in skewt_result.

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  • Print and review model fitting summaries in normal_result and skewt_result respectively.
  • Print the log-likelihood in normal_result and skewt_result respectively.
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