
This paper develops a dynamic approximate factor model in which returns are time-series heteroskedastic. The heteroskedasticity has three components: a factor-related component, a common asset-specific component, and a purely asset-specific component. We develop a new multivariate GARCH model for the factor-related component. We develop a univariate stochastic volatility model linked to a cross-sectional series of individual GARCH models for the common asset-specific component and the purely asset-specific component. We apply the analysis to monthly US equity returns for the period January 1926 to December 2000. We find that all three components contribute to the heteroskedasticity of individual equity returns. Factor volatility and the common component in asset-specific volatility have long-term secular trends as well as short-term autocorrelation. Factor volatility has correlation with interest rates and the business cycle.
Applications of statistics to actuarial sciences and financial mathematics, Time series, auto-correlation, regression, etc. in statistics (GARCH), factor models, volatility, APT, Factor analysis and principal components; correspondence analysis, ARCH, principal components
Applications of statistics to actuarial sciences and financial mathematics, Time series, auto-correlation, regression, etc. in statistics (GARCH), factor models, volatility, APT, Factor analysis and principal components; correspondence analysis, ARCH, principal components
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