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Volatility connectedness in global foreign exchange markets

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  • Wen, Tiange
  • Wang, Gang-Jin

Abstract

We statically and dynamically measure total and directional volatility connectedness in global foreign exchange (forex) markets. We use the volatility spillover index and LASSO-VAR approaches in the variance decomposition framework to construct high-dimensional volatility connectedness network linking 65 major currencies. Empirical results indicate that the US dollar (USD) and Euro are major volatility transmitters while other currencies including Japanese yen and British pound are basically net volatility receivers. In volatility connectedness network, currencies tend to be clustered according to geographical distributions. Dynamically, total volatility connectedness reacts sensitively to changes in international economic fundamentals and increases during crisis periods. Directional volatility connectedness of Renminbi has decreased significantly since the reforms of the Chinese exchange rate regime (which shifts from a USD-pegged exchange rate regime to a regulated, managed floating exchange rate regime). Generally, oil exports, forex regimes and monetary policies are major factors driving volatility transmission across global forex markets.

Suggested Citation

  • Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
  • Handle: RePEc:eee:mulfin:v:54:y:2020:i:c:s1042444x20300062
    DOI: 10.1016/j.mulfin.2020.100617
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    Keywords

    Forex markets; Volatility connectedness; Volatility spillover; Financial network; Currency;
    All these keywords.

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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

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