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Corporate treasurers manage the currency risk of their organization by hedging through futures contracts. The purpose of this paper is to evaluate the effectiveness of hedging by US currency futures contracts by taking into account the efficiency of the currency market.

The static models for calculating hedge ratio are as popular as dynamic models. But the main disadvantage with the static models is that they do not consider important properties of time series like autocorrelation and heteroskedasticity of the residuals and also ignore the cointegration of the market variables which indicate short-run market disequilibrium. The present study, therefore, measures the hedging effectiveness in the US currency futures market using two dynamic models – constant conditional correlation multivariate generalized ARCH (CCC-MGARCH) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH).

The study finds that both the dynamic models used in the study provide similar results. The relative comparison of CCC-MGARCH and DCC-MGARCH models shows that CCC-MGARCH provides better hedging effectiveness result, and thus, should be preferred over the other model.

The findings of the study are important for the company treasurers since the new updated Indian accounting standards (Ind-AS), applicable from the financial year 2016–2017, make it mandatory for the companies to evaluate the effectiveness of hedges. These standards do not specify a quantitative method of evaluation but provide the flexibility to the companies in choosing an appropriate method which justifies their risk management objective. These results are also useful for the policy makers as they can specify and list the appropriate methods for evaluating the hedge effectiveness in the currency market.

Majorly, the studies on Indian financial market limit themselves to either examining the efficiency of that market or to evaluate the effectiveness of the hedges undertaken. Moreover, most of such works focus on the stock market or the commodity market in India. This is one of the first studies which bring together the concepts of efficiency of the market and effectiveness of the hedges in the Indian currency futures market.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process.

A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR‐MGARCH) model is used to construct a time‐varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two‐step approach, which allows for a large system of related prices to be estimated.

In‐sample and out‐of‐sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers' and commercial consumers' risks in futures market participation.

The application is limited to an examination of Montana wheat markets.

Agricultural producers who use futures markets to reduce market risk will have a better method for determining hedging positions, because MGARCH estimated hedge ratios incorporate more information than hedge ratios estimated using existing practices.

Portfolio variance reduction is analogous to utility improvement for agricultural producers. More efficient hedging strategies can lead to better implementation of futures markets and increased social welfare.

This research substantially extends current literature on agricultural hedge strategies by illustrating the advantages of using an hedge ratio estimation approach that incorporates important information about prices at linked markets and prices of other commodities. Providing evidence that market portfolio variance can be lowered using the multivariate estimation approach, the research offers commercial agricultural producers and consumers a practical tool for improving futures market strategies.

The purpose of this study is to investigate the correlation between the price return of leading cryptocurrencies, including Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Peercoin and Dash, and stock return of technology companies' indices that mainly operate on the blockchain platform and provide financial services, including alternative finance, democratized banking, future payments and digital communities.

This study employs a Bayesian asymmetric dynamic conditional correlation multivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (BADCC-MGARCH) model with skewness and heavy tails on daily sample ranging from August 11, 2015, to February 10, 2020, to investigate the dynamic correlation between price return of several cryptocurrencies and stock return of the technology companies' indices that mainly operate on the blockchain platform. Data are collected from multiple sources. For parameter estimation and model comparison, the Markov chain Monte Carlo (MCMC) algorithm is employed. Besides, based on the expected Akaike information criterion (EAIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and weighted Deviance Information Criterion (wDIC), the skewed-multivariate Generalized Error Distribution (mvGED) is selected as an optimal distribution for errors. Finally, some other tests are carried out to check the robustness of the results.

The study results indicate that blockchain-based technology companies' indices' return and price return of cryptocurrencies are positively correlated for most of the sampling period. Besides, the return price of newly invented and more advanced cryptocurrencies with unique characteristics, including Monero, Ripple, Dash, Stellar and Peercoin, positively correlates with the return of stock indices of blockchain-based technology companies for more than 93% of sampling days. The results are also robust to various sensitivity analyses.

The positive correlation between the price return of cryptocurrencies and the return of stock indices of blockchain-based technology companies can be due to the investors' sentiments toward blockchain technology as both cryptocurrencies and these companies are based on blockchain technology. It could also be due to the applicability of cryptocurrencies for these companies, as the price return of more advanced and capable cryptocurrencies with unique features has a positive correlation with the return of stock indices of blockchain-based technology companies for more days compared to the other cryptocurrencies, like Bitcoin, Litecoin and Ethereum, that may be regarded more as speculative assets.

The study results may show the positive role of cryptocurrencies in improving and developing technology companies that mainly operate on the blockchain platform and provide financial services and vice versa, suggesting that managers and regulators should pay more attention to the usefulness of cryptocurrencies and blockchains. This study also has important risk management and diversification implications for investors and companies investing in cryptocurrencies and these companies' stock. Besides, blockchain-based technology companies can add cryptocurrencies to their portfolio as hedgers or diversifiers based on their strategy.

This is the first study analyzing the connection between leading cryptocurrencies and technology companies that mainly operate on the blockchain platform and provide financial services by employing the Bayesian ssymmetric DCC-MGARCH model. The results also have important implications for investors, companies, regulators and researchers for future studies.

Whether stock returns are linked to exchange rate changes and whether foreign exchange risk is priced in a domestic context are less conclusive and thus still subject to a great debate. The purpose of this paper is to provide new empirical evidence on these two inter‐related issues, which are critical to investors and corporate risk management.

This paper applies two different econometric approaches: Nonlinear Seemingly Unrelated Regression (NLSUR) via Hansen's Generalized Method of Moment (GMM) and multivariate GARCH in mean (MGARCH‐M) to examine the exchange rate exposure and its pricing.

Using industry data for Japan, similar to previous studies, foreign exchange risk is not priced based on the test of an unconditional two‐factor asset pricing model. However, strong evidence of time‐varying foreign exchange risk premium and significant exchange rate betas are obtained based on the tests of conditional asset pricing models using MGARCH‐M approach where both conditional first and second moments of industry returns and risk factors are estimated simultaneously.

The strong empirical evidence found in this study implies that corporate currency hedging not only results in more stable cash flows for a firm, but also reduces its cost of capital, and hence is justifiable.

This paper conducts an in‐depth investigation regarding the exchange rate exposure and its pricing by utilizing two different econometric approaches: NLSUR via Hansen's GMM and MGARCH‐M. In doing so, a more reliable conclusion about the exchange rate exposure and its pricing can be drawn.

The paper tries to analyse empirically the impact of India's economic policy uncertainty (EPU) index on different macro-economic variables of India, like import, export, interest rate, exchange rate, inflation rate and stock market during pre-COVID-19 and COVID-19 era.

Although there exist several works where relationship and volatility among the stock markets and macro-economic indicators during the COVID-19 pandemic have been estimated, but till now none of the studies examined the effect of EPU index on different macro-economic variables in the Indian context along with the stock market due to the outbreak of COVID-19 pandemic. This is considered a noteworthy gap and hence opens up a new dimension for examination. To get a clear picture, monthly data from January, 2012 to September, 2021 have been considered where January, 2012–February, 2020 is taken as the pre-COVID-19 period and March, 2020–September, 2021 as COVID-19 period. All the data are converted into log natural. The authors applied DCC-GARCH model to investigate the impact of EPU index on volatility of selected variables over the study period across a multivariate framework and Markov regime-switching model to examine the switching over of the variables.

The results of dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model indicates the presence of volatility in the dependent variables arising out of economic policy uncertainty considering the segmentation of the study period into pre-COVID-19 and COVID-19. The results of Markov regime-switching model show the variables make a significant move from low-volatility regime to high-volatility regime due to the presence of COVID-19.

It can be implied that impact of EPU in terms of volatility on the Indian Stock Market will lead to unfavourable investment conditions for the prospective investors. Even, the different macro-economic variables are to suffer from the volatility arising out of EPU across a long time horizon as confirmed from the DCC-MGARCH model.

The study is original in nature. It adds superior values from the new and significant findings from the study empirically. Application of DCC-MGARCH model and Markov regime switching model makes the study an innovative one in terms of methodology and findings.

This paper aims to study the co-movement dynamics of Islamic equity returns to explain international portfolio diversification opportunities for investors having a heterogeneous stock holding period in light of Brexit.

The authors use the following three recent methodologies: the multivariate generalised autoregressive conditional heteroskedastic-dynamic conditional correlations, continuous wavelet transforms and maximum overlap discrete wavelet transform. Dow Jones Islamic country-based indexes are used from 2 September 2013 to 31 December 2019.

There is a high correlation between the United Kingdom (UK) Islamic stock market return with the Canadian, USA, Malaysian and Indian implying lesser diversification benefits for the investors. However, the results tend to indicate that UK Islamic stock market investors who have allocated their investment in Sri Lanka, Kuwait, Japan and Turkey have enjoyed diversification benefits. Besides, there is a declining correlation between UK Islamic stock markets and other selected markets aftermath of Brexit. Turkey seems the most volatile stock over the period, appealing to risk-lover investors to gain from price changes. When the shock occurs in the financial sector, the volatility is mean-reverting faster than other markets in Sri Lanka. On the other hand, Malaysia appears to have the least volatility implying a stable financial sector.

The results tend to shed light on effective portfolio diversification benefits in light of the recent shock (Brexit) between the UK Islamic stock index and other selected indexes that vary from country to country depending on investment horizons. This critically confirms the significance of heterogeneity in investment horizons and provides significant inferences for portfolio diversification strategies.

To the best of the authors’ knowledge, this study is the first study investigating the Brexit effect on Islamic stocks, guiding Shariah sensitive investors in their diversification strategies, providing information to investors to consider the implications of this incident on Islamic stocks for future shocks.

The purpose of this paper is to measure the effectiveness of the hedging with futures currency contracts. Measuring the effectiveness of hedging has become mandatory for Indian companies as the new Indian accounting standards, Ind-AS, specify that the effectiveness of hedges taken by the companies should be evaluated using quantitative methods but leaves it to the company to choose a method of evaluation.

The paper compares three models for evaluating the effectiveness of hedge – ordinary least square (OLS), vector error correction model (VECM) and dynamic conditional correlation multivariate GARCH (DCC-MGARCH) model. The OLS and VECM are the static models, whereas DCC-MGARCH is a dynamic model.

The overall results of the study show that dynamic model (DCC-MGARCH) is a better model for calculating the hedge effectiveness as it outperforms OLS and VECM models.

The new Indian accounting standards (Ind-AS) mandates the calculation of hedge effectiveness. The results of this study are useful for the treasurers in identifying appropriate method for evaluation of hedge effectiveness. Similarly, policymakers and auditors are benefitted as the study provides clarity on different methods of evaluation of hedging effectiveness.

Many previous studies have evaluated the efficiency of the Indian currency futures market, but with rising importance of hedging in the Indian companies, Reserve Bank of India’s initiatives and encouragement for the use of futures for hedging the currency risk and now the mandatory accounting requirement for measuring hedging effectiveness, it has become more relevant to evaluate the effectiveness of hedge. To the authors’ best knowledge, this is one of the first few papers which evaluate the effectiveness of the currency future hedging.

This study aims to observe the extent of asset diversification benefits in the Association of Southeast Asian Nations (ASEAN)-5 market by examining the effect of financial integration (FI) and financial development (FD) on domestic stock–bond co-movements, SBcorr.

The dynamic conditional correlation - multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) technique is adopted to construct FI and stock−bond co-movement variables. Then, the study uses static panel data analysis to examine the effect of FI on stock−bond co-movements.

FI does not provide asset diversification benefits due to high country risks in ASEAN-5. However, when FI is moderated by FD, FI × FD, the study shows that FI × FD provides higher asset diversification benefits in ASEAN-5.

This study shows the importance of incorporating the level of FD when assessing the effect of FI on stock–bond co-movements in ASEAN-5. In the presence of FI, a well-diversified investor should always consider the state of FD, which will show a better representation of asset diversification strategy in the emerging markets. Additionally, policymakers of ASEAN-5 countries should prioritise enhancing their financial system to attract more investment into the countries.

The purpose of the study is to measure cross-country stock market correlation and volatility transmission during the global coronavirus disease 2019 (COVID-19) pandemic. The paper traces trajectory of Islamic equity investments in order to get insights on the behavior of the markets during the crisis.

The paper uses generalized method of moments (GMM), autoregressive distributed lag (ARDL) and multivariate GARCH (MGARCH) models for analysis of dynamic causality, stock market cointegration, correlation and volatility transmission between Islamic stock indices.

The result of normal correlation analysis on the share indices show the markets move together. The result of ARDL cointegration test shows the markets returns are cointegrated as a group. To further make sense of the data; the indices were grouped into four different categories, then cointegration tests were conducted. The results of the analysis show that the subgroups are cointegrated except the low COVID-19 subgroup. Based on MGARCH findings, the possibility of volatility transmission between markets during the crisis is high. The market returns indices show the usual herd mentality common during the period of crisis.

Unlike other works in this area, this paper attempt to trace the trajectory of Islamic equity investment in order to get relevant insights and arrives at appropriate ways of responding to the crisis.