Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1368-423X.2011.00357.x
Title: Non-stationary non-parametric volatility model
Authors: Han, H. 
Zhang, S. 
Keywords: Kernel estimation
long memory property
non-parametric ARCH
non-parametric cointegrating regression
non-stationarity
volatility persistence
Issue Date: Jun-2012
Source: Han, H., Zhang, S. (2012-06). Non-stationary non-parametric volatility model. Econometrics Journal 15 (2) : 204-225. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1368-423X.2011.00357.x
Abstract: 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. © 2012 The Author(s). The Econometrics Journal © 2012 Royal Economic Society.
Source Title: Econometrics Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/52115
ISSN: 13684221
DOI: 10.1111/j.1368-423X.2011.00357.x
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