| Time Series Econometrics. Conditional Variance Models Contributor(s): Prost, R. (Author) |
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ISBN: 1716568218 ISBN-13: 9781716568213 Publisher: Lulu.com OUR PRICE: $24.69 Product Type: Paperback Published: September 2020 * Not available - Not in print at this time * |
| Additional Information |
| BISAC Categories: - Computers | Mathematical & Statistical Software |
| Physical Information: 0.32" H x 8.27" W x 11.69" (0.83 lbs) 150 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Conditional variance models are appropriate for time series that do not exhibit significant autocorrelation, but are serially dependent. For modeling time series that are both autocorrelated and serially dependent, you can consider using a composite conditional mean and variance model. Two characteristics of financial time series that conditional variance models address are: Volatility clustering and Leverage effects. Volatility is the conditional standard deviation of a time series. Autocorrelation in the conditional variance process results in volatility clustering. The GARCH model and its variants model autoregression in the variance series. Leverage effects. The volatility of some time series responds more to large decreases than to large increases. This asymmetric clustering behavior is known as the leverage effect. The EGARCH and GJR models have leverage terms to model this asymmetry. In this book a variety of examples are presented, all of them treated with MATLAB. |