Is stochastic volatility more flexible than garch?
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical properties usually observed in high frequency financial time series: high kurtosis, small first order autocorrelation of squared observations and slow decay towards zero of the autocorrelation coefficients of squared observations. We show that the ARSV(1) model is more flexible than the GARCH(1,1) model in the sense that it is able to generate series with higher kurtosis and smaller first order autocorrelation of squares for a wider variety of parameter specifications. Our results may help to clarify some puzzles raised in the empirical analysis of real financial time series.
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References listed on IDEAS
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- Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002.
"Bayesian Analysis of Stochastic Volatility Models,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 69-87, January.
- Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-389, October.
- repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
- Geoffrey F. Loudon & Wing H. Watt & Pradeep K. Yadav, 2000. "An empirical analysis of alternative parametric ARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(2), pages 117-136.
- Philippe Jorion, 1988. "On Jump Processes in the Foreign Exchange and Stock Markets," Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 427-445. Full references (including those not matched with items on IDEAS)