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4

If you estimate your model via Maximum Likelihood method, you are forced to re-estimate the full model. This is due to the fact that estimates are values which maximize the full likelihood, the latter being based on a recursive algorithm which use all observations (including the new one) and implies that a new observation may also impact likelihood values of ...


2

You're right. Hansen and Lunde ran 330 specifications, and found GARCH (1,1) the best fitting volatility model. However, in some cases other specifications can beat the results of GARCH (1,1). Checking the ACF/PACF of the squared error term is necessary, although, not sufficient condition. Let's assume the following GARCH (m,s) model $$y_t=a_0+a(L)\...


1

If you have options data with long enough history you could always construct a comparable index by computing the implied volatilities and using a similar weighting methodology to VIX or looking at the implied volatility of the 1 month call/put with strike closest to the price at the observation date (i.e. one closest to 100% moneyness). If you want an ...


5

These are 2 completely different ways of estimating volatility. GARCH models are calibrated on historical time series i.e. information provided under the real-world measure $\mathbb{P}$. Although you can obviously use them for forecasting, the core information which is used to build the model is backward-looking in nature (historical behaviour of the stock)....



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