Persistence and Kurtosis in GARCH and Stochastic Volatility Models

M. Angeles Carnero

208 citations0/0 files storedJournal of Financial Econometrics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

Abstract

This article shows that the relationship between kurtosis, persistence of shocks to volatility, and first-order autocorrelation of squares is different in GARCH and ARSV models. This difference can explain why, when these models are fitted to the same series, the persistence estimated is usually higher in GARCH than in ARSV models, and, why gaussian ARSV models seem to be adequate, whereas GARCH models often require leptokurtic conditional distributions. We also show that introducing the asymmetric response of volatility to positive and negative returns does not change the conclusions. These results are illustrated with the analysis of daily financial returns.

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References

1986Generalized autoregressive conditional heteroskedasticity 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 2003Modeling and Forecasting Realized Volatility 1998Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models 1995Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1992No news is good news 2007Modelling Financial Time Series 19965 Stochastic volatility

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