Modelling persistence in conditional volatility of asset returns

Rajan Pandey and Arya Kumar

Afro-Asian Journal of Finance and Accounting, 2017, vol. 7, issue 1, 16-34

Abstract: Studies on volatility forecasting models indicate superior performance of generalised autoregressive conditional heteroscedasticity (GARCH) type models in the modelling conditional variance of asset returns. The utility of GARCH parameters lies in their ability in explaining the persistence of the conditional variance. The estimate of persistence provides a quantitative measure of the impact of a sudden significant change in the asset return on its future volatility. This study attempts to analyse the magnitude and time-evolving pattern in the persistence of conditional volatility using data on S%P CNX NIFTY 50 (henceforth, Nifty) benchmark index. The GARCH (1, 1) model is fitted on daily returns and a simple iterative scheme is used to re-estimate GARCH parameters on samples of different sizes and different time periods. The GARCH estimates obtained through repeated estimations furnish empirical evidence on the nature and consistency of the persistence parameter. Findings of the study confirm high persistence in the volatility process and indicate a positive relationship between the conditional volatility and volatility persistence.

Keywords: volatility persistence; long memory; conditional volatility; volatility clustering; structural changes; volatility asymmetry; modelling; asset returns; volatility forecasting; GARCH. (search for similar items in EconPapers)
Date: 2017
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