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YouTube T2-26: Maximum likelihood estimation of GARCH parameters

Nicole Seaman

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GARCH(1,1) is the popular approach to estimating volatility, but its disadvantage (compared to STDDEV or EWMA) is that you need to fit three parameters. Maximum likelihood estimation, MLE, is an immensely useful statistical approach that can be used to find "best fit" parameters. In this video, I replicate John Hull's example (the data is S&P 500 index values) to find the best fit alpha (α), beta (β), and omega (ω). Keep in mind that the long-run variance = ω/(1 - α - β), such that indirectly this is solving for a long-run (aka, unconditional) variance.

David's XLS is here: https://trtl.bz/2NlLn7d

 
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