Options Trading: Forecasting Risk and Prices The sp500_options.CSV data file contains S\\&P 500 option price trades measured at 15 minute intervals. - Use Regression, ARIMA, or ARMA (pick 2) to build a model that can forecast these S\\&P 500 option prices (or returns) at the 15 minute frequency. - Do this by building a model on a training window and apply this model to new data on a tsting window, just as you would if you were algorithmically trading options in real life. Then, read in the underlying S\\&P 500 index data (this is an ETF linked to the S\\&P 500; the option above is written on this). - Pick a training window, and estimate a GARCH model to forecast volatility for this S\\&P 500 ETF. - Use this to develop a volatility forecast for 10 periods after the training window ends (i.e. 10 consecutive 15 minute intervals, or a 2.5 hour forecast). Is risk (volatility) expected to rise or to fall during this period? - Finally, compare this risk forecast to what happened to actual option prices during this tim