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• W1590326324 abstract "This chapter reviews modeling time-varying volatility using generalized autoregressive conditional heteroskedastic (GARCH) processes. The presence of excess kurtosis in GARCH models with conditional normally distributed innovations has led to the use of student-GARCH models and GARCH-jump models. Persistence in conditional variances was modeled using variance component models with a stochastic trend component. The finding of time-variation in conditional covariances and correlations resulted in the development of multivariate GARCH and factor-GARCH models. Factor-GARCH models have several attractive features. First, they can be easily interpreted in terms of economic theory (factor models like the arbitrage pricing theory have been used extensively in finance). Second, they allow for a parsimonious representation of time-varying variances and covariances for a high dimensional vector of variables. Third, they can account for both observed and unobserved factors. Fourth, they have interesting implications for common features of the variables. These common features can be tested in a straightforward way. Fifth, they have appeared to fit well in several instances." @default.
• W1590326324 created "2016-06-24" @default.
• W1590326324 creator A5009560383 @default.
• W1590326324 date "1996-01-01" @default.
• W1590326324 modified "2023-10-02" @default.
• W1590326324 title "7 GARCH models of volatility" @default.
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• W1590326324 doi "https://doi.org/10.1016/s0169-7161(96)14009-8" @default.
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