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    Predicting stock returns and volatility in brıcs countries during a pandemic: evidence from the novel wild bootstrap likelihood ratio approach
    (Faculty of Social Sciences, 2022) Özkan, Oktay; Olasehinde-Williams, Godwin; Olanipekun, Ifedola
    In this study, we examine how attention to different pandemics leads returns and volatility of BRICS stock markets, while controlling for economic policy uncertainty. The attention is measured via the newly developed daily infectious disease equity market volatility tracker (EMV-ID). To achieve the study objective, the wild bootstrap likelihood ratio test is employed in analysing time-series data covering the period November 1997 – May 2021. The estimations confirm a time-varying predictive performance of the EMV-ID on both stock returns and volatility series of BRICS, which increases significantly during the months marked by pandemics. The predictive power of the EMV-ID on stock market volatility is however relatively stronger than its predictive power on stock market returns. Our results are robust to alternative specification of volatility based on a Generalized Autoregressive Conditional Heteroskedasticity model.
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    Stock Market Response to Quantitative Easing: Evidence from the Novel Rolling Windows Nonparametric Causality-in-Quantiles Approach
    (Springer, 2023) Olasehinde-Williams, Godwin; Olanipekun, Ifedola; Özkan, Oktay
    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 signifcantly predicted by quantita tive 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 efect of quantitative easing on stock market volatility and returns becomes pronounced during periods of crisis. The reactions are most signifcant in periods corresponding to the Asian fnancial crisis, the global fnancial crisis and the COVID-19 pandemic outbreak. Overall, the causal efect of quantitative easing on both stock market returns and volatility changes through time; the efect on stock market returns is also greater than on stock market volatility.