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Correlations between stock returns and bond returns: income and substitution effects

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  • Gwangheon Hong
  • Youngsoo Kim
  • Bong-Soo Lee

Abstract

We attempt to better understand the varying correlations between stock and bond returns across countries and over sample periods using international data. The observation is that there are two forces that affect the correlation between stock and bond returns. The force that drives a positive correlation is identified as the income effect. The force that drives a negative correlation is identified as the substitution effect. In combination, the two effects help determine the actual correlation between stock and bond returns. We contribute to the literature by proposing an empirical method, the structural vector autoregression (VAR) identification method, to identify the two-income and substitution-effects and to measure the relative importance of the two effects that determine the actual net relation between the two asset returns. We further provide some evidence that the income and substitution effects are related to, among other things, the size of the financial market, the growth and volatility (risk) of the economy, and the business cycle over time. In addition, the framework of the income and substitution effects helps us better understand the automatic stabilizing effects of the dynamic optimal asset allocation during business cycles.

Suggested Citation

  • Gwangheon Hong & Youngsoo Kim & Bong-Soo Lee, 2014. "Correlations between stock returns and bond returns: income and substitution effects," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 1999-2018, November.
  • Handle: RePEc:taf:quantf:v:14:y:2014:i:11:p:1999-2018
    DOI: 10.1080/14697688.2011.631028
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    Cited by:

    1. Thomas C. Chiang, 2020. "Risk and Policy Uncertainty on Stock–Bond Return Correlations: Evidence from the US Markets," Risks, MDPI, vol. 8(2), pages 1-17, June.
    2. Morales-Zumaquero, Amalia & Sosvilla-Rivero, Simón, 2018. "Volatility spillovers between foreign exchange and stock markets in industrialized countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 121-136.
    3. Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark E. Wohar, 2017. "Do Bivariate Multifractal Models Improve Volatility Forecasting in Financial Time Series? An Application to Foreign Exchange and Stock Markets," Working Papers 201728, University of Pretoria, Department of Economics.
    4. Mostafa Ali & Gang Sun, 2017. "Dynamic Relations between Stock Price and Exchange Rate: Evidence from South Asia," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 331-341.
    5. Sui, Lu & Sun, Lijuan, 2016. "Spillover effects between exchange rates and stock prices: Evidence from BRICS around the recent global financial crisis," Research in International Business and Finance, Elsevier, vol. 36(C), pages 459-471.

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