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Volatility forecasting using related markets’ information for the Tokyo stock exchange

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  • Jayawardena, Nirodha I.
  • Todorova, Neda
  • Li, Bin
  • Su, Jen-Je

Abstract

Due to lack of information, volatility cannot be estimated via a high-frequency approach when markets are non-trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high-frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE-listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in-depth empirical analysis. Our findings have important implications for investors and policy makers.

Suggested Citation

  • Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    • Handle: RePEc:eee:ecmode:v:90:y:2020:i:c:p:143-158
    DOI: 10.1016/j.econmod.2020.05.008
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    Cited by:

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    More about this item

    Keywords

    Tokyo stock exchange; Realised volatility; Overnight volatility; Lunch break; Forecasting; Economic significance;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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