Connectedness Between Currency Exchange Rates Volatility,Currency Risk Hedging,International Investments And Firms Value

Networks are usually exhibiting tools of representing people’s movements and financial activities.Most real-world systems can be mapped as networks,that is pair-wise relationships among substances from a certain collection.Naming the few networks are transport networks,social networks,financial networks,biological networks and political networks.The Forex,derivatives and stock markets are observed as a complex financial system,where the relationships between Multicurrency Exchange Rates(MCERs),trading of foreign currency derivatives contracts of both positions and various stocks represent the intricate system.However,in the complex systems,the system comprises non-linear interacting elements commonly found around us.The previous studies have been used most common Machine Learning Algorithms(MLAs)to estimate a complex financial system since several decades that cannot properly assess the complex network.Since last decade,several methodological approaches have been proposed to understand the complex system,one of the most interesting being the soft computing approach that includes Artificial Neural Networks(ANNs),Fuzzy Logic and Evolutionary Algorithms.The ANNs are basic building block of Artificial Intelligence(AI)that attempts to mimic the network of biological neurons that makes up a human brain so that computers can understand things and make decisions like a mammalian brain.So,the main objective of this empirical research is to extend financial network modelling and financial risk management literature and effectiveness.For this purpose,the present study uses Deep Neural Networks(DNNs),structural learning-based Bayesian neural networks and econometric models to extend financial network modelling and financial risk management literature and effectiveness.From DNNs,the present study uses Long Short-Term Memory Autoencoder(LSTM-AE)to assess foreign exchange derivatives market tendency over the period 2007-2021,and DNN-based univariate and multivariate regression with backpropagation algorithm and regression-based feedforward Multilayer Perceptron(MLP)to assess the interaction of variables of the model.From Bayesian network,the present study uses structural learning-based Bayesian neural networks with PC algorithm as a first robustness test to assess the probabilistic distribution and conditional effect of random variable of the model.Moreover,the present study also uses Generalized Least Square-Random Effect(GLS-RE)and Ordinary Least Square(OLS)models as a second robustness tests to examine the relationship of the variables.The significant findings of LSTM-AE intelligent neural network show that the Short-Position of Forwards,Futures and Swaps Contracts(SP-FFSC)of foreign currency derivatives are relatively higher volatile and extensive usage than Long-Position of Forwards,Futures and Swaps Contracts(LP-FFSC)of foreign currency derivatives that represent the hedgers hedged their currency risk by higher use of SP-FFSC of foreign currency derivatives throughout sample of the study.Furthermore,the output of feedforward DNN-based univariate regression shows that the intelligent network of Foreign Exchange(FX)reserves of Pakistan is positively and negatively affected the SP-FFSC and LP-FFSC of foreign exchange derivatives,respectively.In addition,the output of the proposed intelligent network with Rectified Linear Unit(Re LU)shows the LP-FFSC and SPFFSC are increased and decreased by 33.9% and 30.9%,respectively due to higher decumulation FX-reserves of the domestic country.On the other hand,the intelligent network demonstrates a negative and significant relationship between MCERs and SP-FFSC of foreign currency derivatives.Whereas,the intelligent network of MCERs is positively associated with LP-FFSC of foreign currency derivatives.Moreover,the probabilistic Bayes intelligent neural network of twoway Multicurrency Cross-Risk Hedging(MCCRH)conditionally depends on MCERs given the percentage of FX-reserves of an emerging country.The findings are concluded that the volatility of FX-reserves and forex exchange market are strong influenced on derivatives market of Pakistan to hedge currency risk.Furthermore,the output of regression-based MLP intelligent neural network also represents the substantial effects of Multicurrency Exchange Rate Returns(MCERRs)on two-way MCCRH and bilateral flows of international investment.Further,intelligent neural networks also assess the combined network effect of MCERRs and two-way MCCRH on bilateral flows of international investment.The findings of intelligent network of MCERRs show that the LP-FFSC and SP-FFSC are positively and negatively influenced by MCERRs,respectively.Both intelligent networks concluded that the MCERRs are a dominant impacted on two-way MCCRH.It also shows that the intelligent network of MCERRs is positively and negatively influenced the Foreign Direct Investment Inflow(FDII)and Foreign Direct Investment Outflow(FDIO)of Pakistan,respectively,that represent the network of MCERRs is dominant impacted on bilateral flows of international investment of Pakistan.Furthermore,a complex network of SP-FFSC positively-negatively influences the FDII and FDIO,respectively.Moreover,a complex network of LP-FFSC positively-negatively influence the FDIO and FDII,respectively.Both intelligent neural networks represent the association between two-way MCCRH and bilateral flows of international investments of Pakistan.Furthermore,the findings of intelligent neural networks showed the currency risk hedging and firms’ characteristics are dominant impact on firms’ value of Pakistan by an average of 30%.Moreover,the individual network effect of currency risk hedging is 9.27% and 13.60% on Tobin’s Q(TQ)and Adjusted Tobin’s Q(ATQ),respectively.The network findings concluded that currency risk hedging with foreign currency derivatives adds firms’ value premium.This research may help regulators and policymakers in decision-making to control MCERs volatility and maintain derivatives markets to promote emerging currency market stability which may assist international investors in forming effective investment strategies and balancing hedging activities of both positions.The findings of intelligent networks of two-way MCCRH may also support hedgers to manage financial risk by modifying their hedging strategies.The findings of this research may assist global risk managers in devising currency risk hedging strategies for expansion firms’ value of an emerging country.This is the first empirical study using regressionbased intelligent neural networks to assess the interaction of financial parameters by selfgenerating code of the networks that differentiate from previous studies and enhance the significance of the study.The present study has methodological contributions and innovation of the study.The intelligent neural networks may serve as a useful tool for regulators,policymakers and hedgers as they assess financial market tendencies and formulate optimal financial policies.