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Cross-asset relations, correlations and economic implications

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  • McMillan, David G.

Abstract

This paper examines the interrelations and time-varying correlations for eight assets. One-year rolling correlations reveal that each of the 28 correlations exhibit both positive and negative values. Linear regressions reveal that given macroeconomic and financial variables contain predictive power for different asset return correlations. The term structure of interest rates and consumer sentiment feature as prominent predictor variables. Structural break tests and non-linear regressions indicate a cycling of correlations between high and low risk periods. In seeking to consider the economic content of the interrelations, we construct a safe and risky portfolio and show that the correlation between these portfolios can allow for improved market timing. Further, the safe and risky portfolio returns and correlation exhibit predictive power for macroeconomic conditions and may be used in a leading indicator role. The results presented here should be of interest to investors and policy-makers as well as academics wishing to examine the relations between both asset returns and financial and real markets.

Suggested Citation

  • McMillan, David G., 2019. "Cross-asset relations, correlations and economic implications," Global Finance Journal, Elsevier, vol. 41(C), pages 60-78.
  • Handle: RePEc:eee:glofin:v:41:y:2019:i:c:p:60-78
    DOI: 10.1016/j.gfj.2019.02.003
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    3. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Jaipong, Peemmawat & Yamaka, Woraphon, 2022. "The transition of the global financial markets' connectedness during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
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    5. Merediz-Solà, Ignasi & Bariviera, Aurelio F., 2019. "A bibliometric analysis of bitcoin scientific production," Research in International Business and Finance, Elsevier, vol. 50(C), pages 294-305.

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    More about this item

    Keywords

    Time-varying correlations; Cross-asset; Rolling windows; Markov switching; Macroeconomic; Prediction;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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