Non-stationary non-parametric volatility model
Article first published online: 17 JUL 2012
© 2012 The Author(s). The Econometrics Journal © 2012 Royal Economic Society.
The Econometrics Journal
Volume 15, Issue 2, pages 204–225, June 2012
How to Cite
Han, H. and Zhang, S. (2012), Non-stationary non-parametric volatility model. The Econometrics Journal, 15: 204–225. doi: 10.1111/j.1368-423X.2011.00357.x
- Issue published online: 17 JUL 2012
- Article first published online: 17 JUL 2012
- First version received: May 2010; final version accepted: August 2011
- Kernel estimation;
- long memory property;
- non-parametric ARCH;
- non-parametric cointegrating regression;
- volatility persistence
Summary We investigate a new non-stationary non-parametric volatility model, in which the conditional variance of time series is modelled as a non-parametric function of an integrated or near-integrated covariate. Importantly, the model can generate the long memory property in volatility and allow the unconditional variance of time series to be time-varying. These properties cannot be derived from most existing non-parametric or semi-parametric volatility models. We show that the kernel estimate of the model is consistent and its asymptotic distribution is mixed normal. For an empirical application of the model, we study the daily S&P 500 index return volatility using the VIX index as the covariate. It is shown that our model performs reasonably well both in within-sample and out-of-sample forecasts.