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Time-Varying Spillovers between Currency and Stock Markets in the USA: Historical Evidence From More than Two Centuries

Author

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  • Semei Coronado

    (Independent Consultant)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, South Africa,)

  • Besma Hkiri

    (Department of Finance and Economics, University of Jeddah, Saudi Arabia)

  • Omar Rojas

    (Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, México)

Abstract

In this paper, we analyze time-varying causality between the dollar-pound exchange rate and S&P 500 returns over the monthly period of September, 1791 to September, 2019. Based on a Dynamic Conditional Correlation-Multivariate Generalised Autoregressive Conditional Heterosckedasticity (DCC-MGARCH) framework, we find that evidence of unidirectional causality between the two returns is in general weak, and primarily restricted to the period following the breakdown of the Bretton Woods agreement. However, instantaneous spillovers across the returns of these two markets is quite strong, which in turn tends to suggest the existence of nonsynchronous trading and also high-frequency causal dependency, with the latter confirmed based on daily data covering 3 January 1900 – 4 October 2019. Moreover, the underlying DCC reveals that there is actually portfolio diversification opportunities for investors. Finally, an analysis of the second moments reveal much stronger evidence of volatility spillovers between these two assets, when compared to the return linkages. This result has important implications from the perspective of policy making aiming to reduce the impact of uncertainty on the real economy.

Suggested Citation

  • Semei Coronado & Rangan Gupta & Besma Hkiri & Omar Rojas, 2020. "Time-Varying Spillovers between Currency and Stock Markets in the USA: Historical Evidence From More than Two Centuries," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(4), pages 44-76, December.
    • Handle: RePEc:aag:wpaper:v:24:y:2020:i:4:p:44-76
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Teona Shugliashvili, 2023. "The words have power: the impact of news on exchange rates," FFA Working Papers 5.006, Prague University of Economics and Business, revised 31 Jul 2023.
    2. Caporina, Massimiliano & Costola, Michele, 2021. "Time-varying granger causality tests for applications in global crude oil markets: A study on the DCC-MGARCH Hong test," SAFE Working Paper Series 324, Leibniz Institute for Financial Research SAFE.
    3. Celso-Arellano, Pedro & Gualajara, Victor & Coronado, Semei & Martinez, Jose N. & Venegas-Martínez, Francisco, 2023. "Impact of the global fear index (covid-19 panic) on the S&P global indices associated with natural resources, agribusiness, energy, metals and mining: Granger Causality and Shannon and Rényi Transfer ," MPRA Paper 117138, University Library of Munich, Germany, revised 06 Feb 2023.
    4. Caporin, Massimiliano & Costola, Michele, 2022. "Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test," Energy Economics, Elsevier, vol. 111(C).
    5. Semei Coronado & Jose N. Martinez & Victor Gualajara & Rafael Romero-Meza & Omar Rojas, 2023. "Time-Varying Granger Causality of COVID-19 News on Emerging Financial Markets: The Latin American Case," Mathematics, MDPI, vol. 11(2), pages 1-18, January.

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    More about this item

    Keywords

    Time-varying Granger causality; currency and equity markets; returns and volatilities.;
    All these keywords.

    JEL classification:

    • 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
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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