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S&P500 volatility analysis using high-frequency multipower variation volatility proxies

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Abstract

The availability of ultra-high-frequency data has sparked enormous parametric and nonparametric volatility estimators in financial time series analysis. However, some high-frequency volatility estimators are suffering from biasness issues due to the abrupt jumps and microstructure effect that often observed in nowadays global financial markets. Hence, we motivate our studies with two long-memory time series models using various high-frequency multipower variation volatility proxies. The forecast evaluations are illustrated using the S&P500 data over the period from year 2008 to 2013. Our empirical studies found that higher-power variation volatility proxies provide better in-sample and out-of-sample performances as compared to the widely used realized volatility and fractionally integrated ARCH models. Finally, these empirical findings are used to estimate the one-day-ahead value-at-risk of S&P500.

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Notes

  1. Seasonality often exists in high-frequency intraday return, however, not for the intraday quadratic variation. To the authors’ best knowledge, there are two important literatures Taylor and Xu (1997) and Martens et al. (2002) dealing with seasonality in high-frequency intraday return. For better forecasts, Taylor and Xu (1997) suggested to use variance multiplier to deseasonalize the intraday return (one-minute data) for FOREX in a GARCH-based model. Similar approached had been implemented by Martens et al. (2002) in spot FOREX (hourly data) of the Deutsche mark and the Japanese yen against the US dollar. From the authors point of views, since the GARCH model specifications consist of conditional mean (return) and conditional volatility equations, the intraday returns have direct impact to the volatility estimation accuracy. However, for high-frequency volatility models such as HAR and ARFIMA, these models directly using the realized volatility (or other power variation volatility) which have no direct influences from the intraday return.

    Other than GARCH-based models, Deo et al. (2006) proposed a slow time-varying seasonal intraday return filtration (hourly data) for the US stock market using stochastic volatility model. Ghysels et al. (2006) suggested mix data sampling (MIDAS) regression approach with the consideration of seasonal adjustment in S&P500 index empirical analysis. As a summary, the intraday return seasonality filtration can be considered as a special topic which needs a careful study in terms of their theoretical properties. Thus, the authors only have intension to include this feature in our future study.

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Acknowledgements

The authors would like to thank the financial support from the Malaysia Ministry of Higher Education (MOHE) under the Fundamental Research Grant Scheme (FRGS) EP160022.

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  1. Wen Cheong Chin

    Present address: Department of Mathematics, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor Darul Ehsan, Malaysia

Authors and Affiliations

  1. Faculty of Management, SIG Quantitative of Economics and Finance, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Wen Cheong Chin

  2. School of Information Technology, Monash University, 47500, Subang Jaya, Selangor, Malaysia

    Min Cherng Lee

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  1. Wen Cheong Chin
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Correspondence to Min Cherng Lee.

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Chin, W.C., Lee, M.C. S&P500 volatility analysis using high-frequency multipower variation volatility proxies. Empir Econ 54, 1297–1318 (2018). https://doi.org/10.1007/s00181-017-1345-z

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