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This paper investigates the dependence structure and market risk of the currency exchange rate portfolio from the Malaysian ringgit perspective.

The marginal return of the five major exchange rates series, i.e. United States dollar (USD), Japanese yen (JPY), Singapore dollar (SGD), Thai baht (THB) and Chinese Yuan Renminbi (CNY) are modelled by the Bayesian generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model with Student's t innovations. In addition, five different copulas, such as Gumbel, Clayton, Frank, Gaussian and Student's t, are applied for modelling the joint distribution for examining the dependence structure of the five currencies. Moreover, the portfolio risk is measured by Value at Risk (VaR) that considers the extreme events through the extreme value theory (EVT).

The finding shows that Gumbel and Student's t are the best-fitted Archimedean and elliptical copulas, for the five currencies. The dependence structure is asymmetric and heavy tailed.

The findings of this paper have important implications for diversification decision and hedging problems for investors who involving in foreign currencies. The authors found that the portfolio is diversified with the consideration of extreme events. Therefore, investors who are holding an individual currency with VaR higher than the portfolio may consider adding other currencies used in this paper for hedging.

This is the first paper estimating VaR of a currency exchange rate portfolio using a combination of Bayesian GARCH model, EVT and copula theory. Moreover, the VaR of the currency exchange rate portfolio can be used as a benchmark of the currency exchange market risk.

This study aims to investigate the influence of perceived government control (PGC) on cryptocurrency adoption and continuance intention among Indians through an integrated model of the extended Unified Theory of Acceptance and Use of Technology (UTAUT) with the Information System Success Model (ISSM).

This study examined the items of cryptocurrency adoption, continuance intention and PGC adopted from the information systems and cryptocurrency literature. The survey was administered to 391 Indians through an online questionnaire. Partial least squares structural equation modeling was used to analyze data.

Results have shown that social influence, effort expectancy and perceived trust are the major drivers for cryptocurrency adoption. All paths leading to cryptocurrency adoption were found to be significant in the hypothesized directions. The study also found that PGC moderates the relationship between adoption and continuance intention.

This study advances existing literature by empirically verifying the integrated UTAUT and ISSM in the context of cryptocurrency adoption for investment purposes. The findings offer crypto-developers and crypto-exchange insight into how adoption is diffusing in emerging markets. The findings provide policymakers with meaningful insights into the role of government regulations in cryptocurrency continuance intention.