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Through the exogenous growth model, the inflow of international investments promotes economic growth by the inclusion of foreign technologies in production functions and capital accumulation. But the proportional increasing outflow of international investments from emerging countries rapidly squeezes their economies. The main reasons of the massive gap between the inflow and outflow of international investments are devaluation of their home currency and inaccessibility of the foreign exchange derivatives instruments for potential currency risk hedging. The previous studies have strongly ignored the role of two-way multicurrency cross-risk hedging (MCCRH) in bilateral flows of international investments by conditioning effect of the currency returns. By considering the significance of the subject matter, it is needed to assess the interaction between two-way MCCRH, currency returns of multicurrency exchange rate returns (MCERRs) and bilateral flows of international investments, and conditional dependency of random variables of the intelligent network structure. The regression-based MLP and probabilistic Bayes intelligent neural networks represented that the two-way MCCRH and currency returns of MCERRs have a strong impact on bilateral flows of international investments in an emerging country. The constraint-based approach of the probabilistic Bayes intelligent neural network showed that the two-way MCCRH conditionally affects the bilateral flows of international investments given the percentage of currency returns of MCERRs. This study may assist policymakers and regulators to enhance currency returns and availability of foreign exchange derivatives instruments at the least cost to grab more intention of international investors for investments that will expand the economic growth of Pakistan. • A strong nexus between forex and foreign exchange derivatives markets of Pakistan • Currency risk hedging affected bilateral flows of international investments. • Currency returns of MCERRs are influencing factor of international investments. • The Bayes intelligent network structure has joint distribution of random variables. • The Bayes intelligent network structure has conditional dependency of random variables. [ABSTRACT FROM AUTHOR]

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. • The Indian Currency market is affected around 10 % to 12 % by COVID-19 pandemic. • The Indian derivatives market is affected around 3 % to 5 % by COVID-19 pandemic. • The Indian Currency market effect spillovers on significant derivatives market. • A Deep neural network (DNN) gives best prediction than a sallow neural network (SNN). • The Bayesian network is an appropriate approach to estimate the conditional effect. [ABSTRACT FROM AUTHOR]

Considering electricity utilization as the primary source for the firms to sustain and grow, this study examines the impact of an electricity shortfall on firms' access to financial leverage of 432 listed non-financial Pakistani firms from 2001 to 2020. The analysis uses pooled ordinary least squares (OLS), fixed effects (FE), and the generalized method of moments (GMM) and finds that an electricity shortfall adversely impacts access to financial leverage. This adverse effect increases with an increase in the intensity of the electricity shortfall and vice versa. The results suggest that capital structure is associated with the electricity supply to the listed firms. • The utilization of electricity is the primary source of energy for firms to operate and sustain themselves. • Inefficient electricity supply significantly affects the access of financial leverage for firms. • Electricity reliability enhances firms' performance and broadens the market for financial leverage. [ABSTRACT FROM AUTHOR]