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Mutual volatility transmission between assets and trading places

Author

Listed:
  • Masuhr Andreas

    (Department of Economics, Institute for Econometrics, University of Münster, Münster, Germany)

  • Trede Mark

    (Department of Economics, Institute for Econometrics, University of Münster, Münster, Germany)

Abstract

This article proposes a framework to model the mutual volatility transmission between multiple assets and multiple trading places in different time zones. The model is estimated using a dataset containing daily returns of three stock indices – the MSCI Japan, the EuroStoxx 50, and the S&P 500 – each traded at three major trading places: the stock exchanges in Tokyo, London, and New York. Strong volatility transmission effects can be observed between New York and Tokyo, whereas current volatility in New York mostly depends on past volatility in New York. For the assets in consideration, spillovers are strong across trading zones, but weak across assets, suggesting a close connection between market places but only a loose volatility link between international stock indices. Volatility impulse response functions indicate a long-lasting and comparably large response of volatility in Tokyo, whereas they suggest a quicker volatility decay in London and New York.

Suggested Citation

  • Masuhr Andreas & Trede Mark, 2023. "Mutual volatility transmission between assets and trading places," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-15.
    • Handle: RePEc:vrs:demode:v:11:y:2023:i:1:p:15:n:1005
    DOI: 10.1515/demo-2022-0155
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    References listed on IDEAS

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    More about this item

    Keywords

    international volatility spillovers; copula-GARCH models; intra-day; volatility impulse responses;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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