GARCH(1,1): Parameters from Maximum Likelihood Estimation
1. Context
In this short video from FRM Part 1 curriculum, we look at estimating parameters of the GARCH(1,1) model using Maximum Likelihood Estimation (MLE). We first understand the MLE technique using three case studies – estimating average default time (that follows Exponential Distribution), estimating average arrival rate of events (that follow Poisson Distribution) and estimating unconditional variance from observed daily returns. Based on the intuition gathered from these, we finally apply MLE to estimating GARCH(1,1) parameters. The details of the reading in which this topic appears are given below:
| Area | Quantitative Analysis |
| Reading | Volatility |
| Reference | John C. Hull, Chapter 10. Volatility In Risk Management and Financial Institutions, 4th Edition, (Hoboken, NJ: John Wiley & Sons, 2015). |