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Market variance risk premiums in Japan for asset predictability

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  • Masato Ubukata
  • Toshiaki Watanabe

Abstract

This article evaluates the predictive performance of variance risk premiums (VRPs) in Japan on the Nikkei 225 returns, credit spreads, and the composite index of coincident indicators. Different monthly VRPs, such as expected and ex-post VRPs, are measured by using model-free implied and realized variances from option prices and high-frequency (HF) data, and their predictive ability is compared with that of VRPs using a realized measure based on coarser frequency return observations. The empirical results show that the VRPs in Japan with HF data are useful in predicting credit spreads and the composite index of coincident indicators, but lose their predictive ability for the Nikkei 225 returns. Such significant predictive power tends to be greater for the expected VRPs with HF data relative to the ex-post VRP with HF data and VRPs with daily data as well as for lower investment grade credit spreads. Copyright Springer-Verlag Berlin Heidelberg 2014

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  • Masato Ubukata & Toshiaki Watanabe, 2014. "Market variance risk premiums in Japan for asset predictability," Empirical Economics, Springer, vol. 47(1), pages 169-198, August.
    • Handle: RePEc:spr:empeco:v:47:y:2014:i:1:p:169-198
    DOI: 10.1007/s00181-013-0741-2
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    Cited by:

    1. Toshiaki Ogawa & Masato Ubukata & Toshiaki Watanabe, 2020. "Stock Return Predictability and Variance Risk Premia around the ZLB," IMES Discussion Paper Series 20-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    2. Maki, Daiki, 2024. "Forecasting downside and upside realized volatility: The role of asymmetric information," The Journal of Economic Asymmetries, Elsevier, vol. 29(C).
    3. Andersen, Torben G. & Todorov, Viktor & Ubukata, Masato, 2021. "Tail risk and return predictability for the Japanese equity market," Journal of Econometrics, Elsevier, vol. 222(1), pages 344-363.
    4. Ubukata, Masato, 2018. "Dynamic hedging performance and downside risk: Evidence from Nikkei index futures," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 270-281.
    5. Wei‐Shao Wu & Sandy Suardi, 2021. "Economic Uncertainty and Bank Lending," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(8), pages 2037-2069, December.
    6. Masato Ubukata, 2022. "A time-varying jump tail risk measure using high-frequency options data," Empirical Economics, Springer, vol. 63(5), pages 2633-2653, November.
    7. Masato Ubukata, 2019. "Jump tail risk premium and predicting US and Japanese credit spreads," Empirical Economics, Springer, vol. 57(1), pages 79-104, July.
    8. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.

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    More about this item

    Keywords

    Variance risk premium; Predictability; Realized variance; Implied variance; High-frequency data; C22; G17;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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