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Stock Market Response to Quantitative Easing: Evidence from the Novel Rolling Windows Nonparametric Causality-in-Quantiles Approach

Author

Listed:
  • Godwin Olasehinde-Williams

    (Istanbul Ticaret University)

  • Ifedola Olanipekun

    (Adeyemi College of Education
    Istanbul Gelisim University)

  • Oktay Özkan

    (Tokat Gaziosmanpasa University)

Abstract

The US Federal Reserve has been using quantitative easing as an unconventional monetary policy tool for providing liquidity and credit-market facilities to banks, and undertaking large-scale asset purchases in periods of crisis. This study carefully examines whether the US stock market has been responsive to the use of quantitative easing over time. A major contribution of this study to the extant literature is the introduction of the novel rolling windows nonparametric causality-in-quantiles approach to studying the reaction of the stock market to quantitative easing. This approach provides a means of investigating the time-varying causality between the variables across quantiles. The standard nonparametric causality-in-quantiles test results show that stock market performance is significantly predicted by quantitative easing, except at very low and very high levels of stock returns (volatility). The rolling windows nonparametric causality-in-quantiles test results indicate that the causal effect of quantitative easing on stock market volatility and returns becomes pronounced during periods of crisis. The reactions are most significant in periods corresponding to the Asian financial crisis, the global financial crisis and the COVID-19 pandemic outbreak. Overall, the causal effect of quantitative easing on both stock market returns and volatility changes through time; the effect on stock market returns is also greater than on stock market volatility.

Suggested Citation

  • Godwin Olasehinde-Williams & Ifedola Olanipekun & Oktay Özkan, 2024. "Stock Market Response to Quantitative Easing: Evidence from the Novel Rolling Windows Nonparametric Causality-in-Quantiles Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 947-977, August.
  • Handle: RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10450-y
    DOI: 10.1007/s10614-023-10450-y
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