Econometric Modeling with Matlab. Conditional Variance Time Series Models

By: Noriega, B.

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. 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.The generalized autoregressive conditional heteroscedastic (GARCH) model is an extension of Engle's ARCH model for variance heteroscedasticity. If a series exhibits volatility clustering, this suggests that past variances might be predictive of the current variance. The GARCH(P, Q) model is an autoregressive moving average model for conditional variances, with P GARCH coefficients associated with lagged variances, and Q ARCH coefficients associated with lagged squared innovations.The exponential GARCH (EGARCH) model is a GARCH variant that models the logarithm of the conditional variance process. In addition to modeling the logarithm, the EGARCH model has additional leverage terms to capture asymmetry in volatility clustering. The EGARCH(P, Q) model has P GARCH coefficients associated with lagged log variance terms, Q ARCH coefficients associated with the magnitude of lagged standardized innovations, and Q leverage coefficients associated with signed, lagged standardized innovations.

• Format: Paperback
• Language: English
• ISBN: 1798663759
• ISBN13: 9781798663752
• Release Date: January 1
• Publisher: Independently Published
• Length: 150 Pages
• Weight: 0.50 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"