Discrete stochastic autoregressive volatility

Adriana S. Cordis and Chris Kirby

Journal of Banking & Finance, 2014, vol. 43, issue C, 160-178

Abstract: We use Markov chain methods to develop a flexible class of discrete stochastic autoregressive volatility (DSARV) models. Our approach to formulating the models is straightforward, and readily accommodates features such as volatility asymmetry and time-varying volatility persistence. Moreover, it produces models with a low-dimensional state space, which greatly enhances computational tractability. We illustrate the proposed methodology for both individual stock and stock index returns, and show that simple first- and second-order DSARV models outperform generalized autoregressive conditional heteroscedasticity and Markov-switching multifractal models in forecasting volatility.

Keywords: Markov chain; Time-varying transition probabilities; Discrete autoregressive model; Stochastic volatility; Realized volatility (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:43:y:2014:i:c:p:160-178

DOI: 10.1016/j.jbankfin.2014.03.020

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