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Volatility modelling in R exercises (Part-4)

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This is the fourth part of the series on volatility modelling. For other parts of the series follow the tag volatility. In this exercise set we will explore GARCH-M and E-GARCH models. We will also use these models to generate rolling window forecasts, bootstrap forecasts and perform simulations. Answers to the exercises are available here. Exercise 1Load the rugarch and the FinTS packages. Next, load the m.ibmspln dataset from the FinTS package. This dataset contains monthly excess returns of the S&P500 index and IBM stock from Jan-1926 to Dec-1999 (Ruey Tsay (2005) Analysis of Financial Time Series, 2nd ed. ,Wiley, chapter 3).Also, load the forecast package which we will use for autocorrelation graphs. Exercise 2Estimate a GARCH(1,1)-M model for the S&P500 excess returns series. Determine if the effect of volatility on asset returns is significant. Exercise 3Excess IBM stock…
Original Post: Volatility modelling in R exercises (Part-4)