3/2/2024 0 Comments Aptech gauss 13 torrentThen we will take the natural log of these changes and multiply by 100 and plot the transformed data. We will transform our data by dividing y by y t-1, to get the percent change from one period to the next. The series seems to have a trend and the mean is clearly not zero-both are as we would expect for stock data. Extract all observations from the 4th column of 'y' For our example, we will use the fourth column of index.dat which contains data for the S&P 500. This file contains data for several stock indexes. + β p ε 2 t-p Load and plot time seriesįor this example, we will use a data set that comes with TSMT, named index.asc. Where, z t is a Gaussian white-noise process and σ t represents the time-varying standard deviation. The error term in a generalized autoregressive conditional heteroskedastic (GARCH) model is: This time-varying volatility cannot be modeled by an ARMA model, because its conditional variance given previous time periods is fixed. The volatility of financial time series data is often serially correlated, such that there are often periods of sustained high volatility and other periods of sustained low volatility.
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