Artificial neural network (ANN)-based estimation of the influence of COVID-19 pandemic on dynamic and emerging financial markets.

The COVID-19 pandemic is a serious global issue destroying financial markets awfully. The proper estimation effect of COVID-19 pandemic on dynamic emerging financial markets is a big challenge due to a complex multidimensional data. However, the present study proposes a Deep Neural Network (DNN)-based multivariate regression approach with backpropagation algorithm and structural learning-based Bayesian network with constraint-based algorithm to investigate the influence of COVID-19 pandemic on the currency and derivatives markets of an emerging economy. The output shows that the COVID-19 pandemic has negatively influenced the financial markets as indicated by sharply depreciating currency value around 10 % to 12 % and reducing short-position of futures derivatives around 3 % to 5 % for currency risk hedging. The robustness estimation shows that there have probabilistic distributed between Traded Futures Derivatives Contracts (TFDC), Currency Exchange Rate (CER), and Daily Covid Cases (DCC) and Daily Covid Deaths (DCD). Moreover, the output represents that the futures derivatives market conditionally depends on the currency market volatility given percentage of COVID-19 pandemic. This study may help to policymakers of financial markets in decision-making to control CER volatility that may promote currency market stability to enhance currency market activities and boost confidence of foreign investors in extreme financial crisis circumstances.

Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic.

The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.

Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective.

We studied the cross-correlations in the daily closing prices of 181 stocks listed on the Pakistan stock exchange (PSX) covering a time period of 2007-2017 to compute the threshold networks and minimum spanning trees. In addition to the full sample analysis, our study uses three subsamples to examine the structural change and topological evolution before, during, and after the global financial crisis of 2008. We also apply Shannon entropy on the overall sample to measure the volatility of individual stocks. Our results find substantial clustering and a crisis-like less stable overall market structure, given the external and internal events of terrorism, political, financial, and economic crisis for Pakistan. The subsample results further reveal hierarchal scale-free structures and a reconfigured metastable market structure during a postcrisis period. In addition, time varying topological measures confirm the evidence of the presence of several star-like structures, the shrinkage of tree length due to crisis-related shocks, and an expansion in the recovery phase. Finally, changes of the central node of minimum spanning trees (MSTs), the volatile stock recognition using Shannon entropy, and the topology of threshold networks will help local and international investors of Pakistan Stock Exchange limited (PSX) to manage their portfolios or regulators to monitor the important nodes to achieve stability and to predict an upcoming crisis.