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...
This study introduces GARCH models with cross-sectional market volatility, which we call GARCHX mode...
The persistent nature of equity volatility is investigated by means of a multi-factor stochastic vol...
The idea of component models for volatility is extended to dynamic correlations. We propose a model ...
This paper develops a dynamic approximate factor model in which returns are time-series heteroskeda...
Abstract: Volatility is a key parameter used inmany financial applications, from deriva-tives valuat...
Decomposing volatilities into a common market-driven component and an idiosyncratic item-specific co...
In large panels of financial time series with dynamic factor structure on the levels or returns, the...
Many economic and financial time series have been found to exhibit dynamics in variance; that is, th...
Abstract: We present a novel GARCH model that accounts for time varying, state dependent, persistenc...
This paper estimates a structural times series model of return volatility. We argue that the structu...
In large panels of financial time series with dynamic factor structure on the levels or returns, the...
This paper estimates a structural times series model of return volatility. We argue that the structu...
This thesis studies time series properties of the covariance structure of multivariate asset returns...
This paper offers a new estimator of volatility’s common component of a return. This estimator is o...
This study introduces GARCH models with cross-sectional market volatility, which we call GARCHX mode...
The persistent nature of equity volatility is investigated by means of a multi-factor stochastic vol...
The idea of component models for volatility is extended to dynamic correlations. We propose a model ...
This paper develops a dynamic approximate factor model in which returns are time-series heteroskeda...
Abstract: Volatility is a key parameter used inmany financial applications, from deriva-tives valuat...
Decomposing volatilities into a common market-driven component and an idiosyncratic item-specific co...
In large panels of financial time series with dynamic factor structure on the levels or returns, the...
Many economic and financial time series have been found to exhibit dynamics in variance; that is, th...
Abstract: We present a novel GARCH model that accounts for time varying, state dependent, persistenc...
This paper estimates a structural times series model of return volatility. We argue that the structu...
In large panels of financial time series with dynamic factor structure on the levels or returns, the...
This paper estimates a structural times series model of return volatility. We argue that the structu...
This thesis studies time series properties of the covariance structure of multivariate asset returns...
This paper offers a new estimator of volatility’s common component of a return. This estimator is o...
This study introduces GARCH models with cross-sectional market volatility, which we call GARCHX mode...
The persistent nature of equity volatility is investigated by means of a multi-factor stochastic vol...
The idea of component models for volatility is extended to dynamic correlations. We propose a model ...