Forecasting volatility of SSEC in Chinese stock market using multifractal analysis

Yu Wei and Peng Wang

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 7, 1585-1592

Abstract: In this paper, taking about 7 years’ high-frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we propose a daily volatility measure based on the multifractal spectrum of the high-frequency price variability within a trading day. An ARFIMA model is used to depict the dynamics of this multifractal volatility (MFV) measures. The one-day ahead volatility forecasting performances of the MFV model and some other existing volatility models, such as the realized volatility model, stochastic volatility model and GARCH, are evaluated by the superior prediction ability (SPA) test. The empirical results show that under several loss functions, the MFV model obtains the best forecasting accuracy.

Keywords: Econophysics; Multifractal; Realized volatility; Stochastic volatility model; GARCH; Superior predictive ability (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (36)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:7:p:1585-1592

DOI: 10.1016/j.physa.2007.11.015

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