Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5088851 | Journal of Banking & Finance | 2014 | 49 Pages |
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
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Economics, Econometrics and Finance
Economics and Econometrics
Authors
Adriana S. Cordis, Chris Kirby,