Highlights The COVID-19 pandemic has jolted foreign exchange markets within a short time. We measure forex efficiency with multifractal detrended fluctuation analysis. We find varying degrees of forex market efficiency before and during COVID-19. Investors can structure their investment strategies to exploit market inefficiency. Our findings may help policymakers find responses to such forex market upheavals.

We employ multifractal detrended fluctuation analysis (MF-DFA) to provide the first look at the efficiency of forex markets during the initial period of ongoing COVID-19 pandemic, which has disrupted the financial markets globally. We use high frequency (5-min interval) data of six major currencies traded in the forex market for the period from 01 October 2019 to 31 March 2020. Prior to the application of MF-DFA, we examine the inner dynamics of multifractality using seasonaltrend decompositions using loess (STL) method. Overall, the results confirm the presence of multifractality in forex markets, which demonstrates, in particular: (i) a decline in the efficiency of forex markets during the period of COVID-19 outbreak, and (ii) the heterogeneity in the effects on the strength of multifractality of exchange rate returns under investigation. The largest effect is observed in the case of AUD as it shows the highest (lowest) efficiency before (during) COVID-19 assessed in terms of low (high) multifractality. During COVID-19 period, CAD and CHF exhibit the highest efficiency. Our findings may help policymakers in shaping a comprehensive response to improve the forex market efficiency during such a black swan event.

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Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future.

PurposeThe purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).Design/methodology/approachThe data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.FindingsThe results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.Practical implicationsThe findings of this study may have important implications for investment and risk management by using index-based products.Originality/valueNumerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.