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A new generalized volatility proxy via the stochastic volatility model

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  • Jong-Min Kim
  • Hojin Jung
  • Li Qin

Abstract

This article proposes power transformation of absolute returns as a new proxy of latent volatility in the stochastic model. We generalize absolute returns as a proxy for volatility in that we place no restriction on the power of absolute returns. An empirical investigation on the bias, mean square error and relative bias is carried out for the proposed proxy. Simulation results show that the new estimator exhibiting negligible bias appears to be more efficient than the unbiased estimator with high variance.

Suggested Citation

  • Jong-Min Kim & Hojin Jung & Li Qin, 2017. "A new generalized volatility proxy via the stochastic volatility model," Applied Economics, Taylor & Francis Journals, vol. 49(23), pages 2259-2268, May.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:23:p:2259-2268
    DOI: 10.1080/00036846.2016.1237751
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    References listed on IDEAS

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    1. Torben G. Andersen & Luca Benzoni & Jesper Lund, 2002. "An Empirical Investigation of Continuous‐Time Equity Return Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1239-1284, June.
    2. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    3. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, June.
    4. McKenzie, Michael D., 1999. "Power transformation and forecasting the magnitude of exchange rate changes," International Journal of Forecasting, Elsevier, vol. 15(1), pages 49-55, February.
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    Cited by:

    1. Jong-Min Kim & Chulhee Jun & Junyoup Lee, 2021. "Forecasting the Volatility of the Cryptocurrency Market by GARCH and Stochastic Volatility," Mathematics, MDPI, vol. 9(14), pages 1-16, July.

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