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`

.

Instructions 1/2

**undefined XP**

- Print and review model fitting summaries in
`normal_result`

and`skewt_result`

respectively. - Print the log-likelihood in
`normal_result`

and`skewt_result`

respectively.