Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jeconom.2008.08.016
Title: Time series properties of ARCH processes with persistent covariates
Authors: Han, H. 
Park, J.Y.
Keywords: ARCH
Leptokurtosis
Nonlinearity
Nonstationarity
Persistent covariate
Volatility persistence
Issue Date: 2008
Source: Han, H., Park, J.Y. (2008). Time series properties of ARCH processes with persistent covariates. Journal of Econometrics 146 (2) : 275-292. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jeconom.2008.08.016
Abstract: We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.
Source Title: Journal of Econometrics
URI: http://scholarbank.nus.edu.sg/handle/10635/22408
ISSN: 03044076
DOI: 10.1016/j.jeconom.2008.08.016
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