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Stock-bond return correlations: Moving away from "one-frequency-fits-all" by extending the DCC-MIDAS approach

Author

Listed:
  • Allard, Anne-Florence
  • Iania, Leonardo
  • Smedts, Kristien

Abstract

This paper explores the determinants of U.S. stock-bond correlations estimated at various frequencies. For this purpose, the two-component DCC-MIDAS model of correlation (Colacito et al., 2011) is used and extended to incorporate a third correlation frequency component. Subsequently, macroeconomic and financial variables are studied as determinants of each component. We show that the daily correlation component is driven by financial market factors, while the monthly component is more influenced by macroeconomic factors. Finally, the yearly component is determined by funding opportunities in the economy. These results are important as they show that different correlation components and determinants should be considered for different investment horizons.
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Suggested Citation

  • Allard, Anne-Florence & Iania, Leonardo & Smedts, Kristien, 2020. "Stock-bond return correlations: Moving away from "one-frequency-fits-all" by extending the DCC-MIDAS approach," LIDAM Reprints LFIN 2020005, Université catholique de Louvain, Louvain Finance (LFIN).
    • Handle: RePEc:ajf:louvlr:2020005
    Note: In : International Review of Financial Analysis, Vol. 71 (2020)
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    Cited by:

    1. Wang, Xiong & Li, Jingyao & Ren, Xiaohang, 2022. "Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond," International Review of Financial Analysis, Elsevier, vol. 83(C).
    2. Guo, Yaoqi & Deng, Yiwen & Zhang, Hongwei, 2023. "How do composite and categorical economic policy uncertainties affect the long-term correlation between China's stock and conventional/green bond markets?," Finance Research Letters, Elsevier, vol. 57(C).
    3. Hu, Zinan & Borjigin, Sumuya, 2024. "The amplifying role of geopolitical Risks, economic policy Uncertainty, and climate risks on Energy-Stock market volatility spillover across economic cycles," The North American Journal of Economics and Finance, Elsevier, vol. 71(C).
    4. Wang, Xiong & Li, Jingyao & Ren, Xiaohang & Bu, Ruijun & Jawadi, Fredj, 2023. "Economic policy uncertainty and dynamic correlations in energy markets: Assessment and solutions," Energy Economics, Elsevier, vol. 117(C).
    5. Pan, Zhijie & Zheng, Yanting & Xu, Dandan & Wang, Ting, 2024. "How green screening influences risk transmission among stock-bond indices: Insight into the dependence structure," Finance Research Letters, Elsevier, vol. 69(PA).
    6. Dai, Zheyu & Liu, Jian, 2024. "Exploring the mechanism of regional ecological legal governance's impact on corporate bond credit spreads," Finance Research Letters, Elsevier, vol. 70(C).
    7. Su, Xianfang & Guo, Dawei & Dai, Liang, 2023. "Do green bond and green stock markets boom and bust together? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Pineda, Julián & Cortés, Lina M. & Perote, Javier, 2022. "Financial contagion drivers during recent global crises," Economic Modelling, Elsevier, vol. 117(C).
    9. Yao, Yinhong & Chen, Xiuwen & Chen, Zhensong, 2025. "Portfolio tail risk forecasting for international financial assets: A GARCH-MIDAS-R-Vine copula model," The North American Journal of Economics and Finance, Elsevier, vol. 77(C).
    10. Zhang, Hongwei & Wei, Shiyao & Guo, Yaoqi & Gao, Wang, 2024. "Analyzing the interconnection between rare earth market and green economy: Time-varying effects of trade policy uncertainty," Resources Policy, Elsevier, vol. 97(C).
    11. Pan, Beier, 2023. "The asymmetric dynamics of stock–bond liquidity correlation in China: The role of macro-financial determinants," Economic Modelling, Elsevier, vol. 124(C).
    12. Jian Ni & Yue Xu, 2023. "Forecasting the Dynamic Correlation of Stock Indices Based on Deep Learning Method," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 35-55, January.

    More about this item

    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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