Localized Realized Volatility Modelling

SFB 649 Discussion Paper 2009-003

36 Pages Posted: 9 Jan 2017

See all articles by Ying Chen

Ying Chen

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Uta Pigorsch

University of Mannheim

Date Written: January 22, 2009

Abstract

With the recent availability of high-frequency financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. 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 (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). 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. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability.

Keywords: Localized Autoregressive Modeling, Realized Volatility, Adaptive Procedure

JEL Classification: G17, C14, C51

Suggested Citation

Chen, Ying and Härdle, Wolfgang K. and Pigorsch, Uta, Localized Realized Volatility Modelling (January 22, 2009). SFB 649 Discussion Paper 2009-003, Available at SSRN: https://ssrn.com/abstract=2894277 or http://dx.doi.org/10.2139/ssrn.2894277

Ying Chen

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Spandauer Strasse 1
Berlin, D-10178
Germany

Wolfgang K. Härdle (Contact Author)

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Uta Pigorsch

University of Mannheim ( email )

Universitaetsbibliothek Mannheim
Zeitschriftenabteilung
Mannheim, 68131
Germany

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