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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 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.

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.

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.

The aim of this study is to measure portfolio diversification benefits of the Turkey-based equity investors into top trading partner countries. Portfolio diversification benefits are analyzed from the viewpoint of two types of investors in Turkey: conventional equities investors and Islamic equity investors.

In order to evaluate the time-varying correlations of the trading partner country's stock index returns with the Turkish stock index returns, the multivariate-generalized autoregressive conditional heteroskedasticity–dynamic conditional correlation (GARCH-DCC) is applied based on daily data covering 13 years' period between January 22, 2008 and January 22, 2021.

The results revealed that the US stock indices provide the most diversified benefit for both conventional and Islamic Turkey-based equity investors. In general, Islamic indices exhibit relatively lower correlation with trading partners than conventional indices. Turkey and Russia are recorded as the most volatile indices.

The diversification potential in trading partners for Turkey-based Islamic equity investors has not been studied yet. This study is to fill in this gap in the literature and to give fruitful insights to both conventional and Islamic investors.

Even though Bitcoin has been often labelled as a safe haven asset class in the literature, the influence of economic policy uncertainty (EPU) on the diversifying opportunities offered by Bitcoin in relation to other assets needs to be investigated. This paper aims to investigate how the EPU affects diversification of commodity, conventional, Islamic and sustainable equity returns in relation to its impact on Bitcoin returns.

The authors use advanced time-series econometrics, namely, multivariate generalized autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transformation, for the analysis of the daily returns for the aforementioned assets between 01 August 2011 and 01 September 2019.

First, the authors found a strong evidence of Bitcoin’s mean reverting trend in the long run while its volatility has decreased significantly since 2013. After separating the EPU into two regimes (high and low), diversification opportunities with Bitcoin seems to disappear in a high EPU period, while the hedging opportunity tends to prevail in a low EPU period for all classes of assets. Importantly, the findings indicate that Bitcoin offers short-term diversification for sustainable and Islamic equity as well as energy stocks during a low uncertainty period. Consequently, in relation to the policy uncertainty, Bitcoin provides similar hedging opportunities than commodities like Gold and Silver. Overall, the study shows that EPU is remarkably important in explaining the average portfolio returns of Bitcoin, suggesting that this indicator can be perceived as a decent explanatory factor for portfolio diversification.

The study significantly extends the empirical literature of Bitcoin’s portfolio diversification by taking EPU into consideration. To the best of authors’ knowledge, this is one of the few studies to investigate the asymmetric effects of US EPU on Bitcoin’s hedging capabilities by taking into account major conventional equity, sustainable equity, Islamic equity, gold, silver and oil.

The purpose of this paper is to analyze the optimal hedging strategy of the oil-stock dependence structure.

The methodology consists to model the data over the daily period spanning from January 02, 2002 to May 19, 2016 by a various copula functions to better modeling the dependence between crude oil market and stock markets, and to use dependence coefficients and conditional variance to calculate optimal portfolio weights and optimal hedge ratios, and to suggest the best hedging strategy for oil-stock portfolio.

The findings show that the Gumbel copula is the best model for modeling the conditional dependence structure of the oil and stock markets in most cases. They also indicate that the best hedging strategy for oil price by stock market varies considerably over time, but this variation depends on both the index introduced and the model used. However, the conditional copula method with skewed student more effective than the other models to minimize the risk of oil-stock portfolio.

This research implication can be valuable for portfolio managers and individual investors who seek to make earnings by diversifying their portfolios. The findings of this study provide evidence of the importance of stock assets for making an optimal portfolio consisting of oil in the case of investments in oil and stock markets. This paper attempts to fill the voids in the literature on volatility among oil prices and stock markets in two important areas. First, it uses copulas to investigate the conditional dependence structure of the oil crude and stock markets in the oil exporting and importing countries. Second, it uses the dependence coefficients and conditional variance to calculate dynamic hedge ratios and risk-minimizing optimal portfolio weights for oil–stock.

The purpose of this paper is to obtain a comprehensive structure of past empirical studies on financial contagion which can provide the present growth and future scope of research work on the field of contagion analysis.

Present study identifies 151 empirical studies on financial contagion and summarises all the studies on the basis of tools and methodology used, year of the studies, origin of the studies, sample period and sample countries taken, studies undertaken on the basis of different crisis period and markets considered and finally sources of the studies.

The results of the analysis show that the empirical studies on contagion increased continuously over the past five years. Higher order test of contagion with more number of sample countries may provide more accurate picture on financial contagion.

This paper collects, classifies and summarises past empirical studies on financial contagion and provides valuable conclusion on present growth and future scope of studies on financial contagion. The information given in this paper can be helpful for future researchers and academicians on this particular field; the summary of the conclusion (from past reviews) may be helpful for the policy makers for asset allocation and risk management.

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).