Please use this identifier to cite or link to this item: https://doi.org/10.1198/jasa.2010.ap09039
Title: Localized realized volatility modeling
Authors: Chen, Y. 
Härdle, W.K.
Pigorsch, U.
Keywords: Adaptive procedure
Localized autoregressive modeling
Issue Date: Dec-2010
Citation: Chen, Y., Härdle, W.K., Pigorsch, U. (2010-12). Localized realized volatility modeling. Journal of the American Statistical Association 105 (492) : 1376-1393. ScholarBank@NUS Repository. https://doi.org/10.1198/jasa.2010.ap09039
Abstract: With the recent availability of high-frequency financial data the long-range dependence of volatility regained researchers' interest and has led to the consideration of long-memory models for volatility. The long-range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short-memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern. In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. A simulation study shows that long memory processes as well as short memory processes with structural breaks can be well approximated by this local approach. Furthermore, using S&P500 data we find that our local modeling approach outperforms long-memory type models and models with structural breaks in terms of predictability. © 2010.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105208
ISSN: 01621459
DOI: 10.1198/jasa.2010.ap09039
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