Purpose
– The purpose of this paper is to identify the determinants for currency internationalization and forecast the potential of RMB as an international reserve currency.
Design/methodology/approach
– This paper performs linear or non-linear regressions of the shares of eight major international reserve currencies as the reserve assets in global central banks on the macro economic and financial variables of their corresponding countries to identify the determinants for their international positions, and conducts an “counter-factual simulation” for the potential of RMB as an international reserve currency.
Findings
– This paper finds that the economic size and the “network externalities” are the most important determinants for the international status of a reserve currency; that exchange rate volatility has negative impacts; the conditions for the RMB internationalization are basically available. The simulation for the potential of RMB as an international reserve currency reveals that the international role of RMB could surpass that of the Japanese Yen and the British Pound, and get close to Euro in the coming 15 years. Based on the empirical evidence, this paper suggests a promoting strategy for RMB internationalization.
Research limitations/implications
– This paper has not taken the influence of economic systemic and political factors on the process of RMB internationalization into account.
Practical implications
– RMB internationalization promotion should follow the strategy of “stably create RMB international demand in the initial period and dramatically release the RMB overseas supply in the latter period” in the coming 15 years.
Originality/value
– The conclusions and policy implications are from the results of the empirical analysis on the 45-year historical experience on the eight main international currencies.
Analysis of large volumes of data is very complex due to not only a high level of skewness and heteroscedasticity of variance but also the phenomenon of missing data. Expectile regression is a popular alternative method of analyzing heterogeneous data. In this paper, we consider fitting a linear expectile regression model for estimating conditional expectiles based on a large quantity of data with covariates missing at random. We construct a communication-efficient surrogate loss (CSL) function to estimate model parameters. The asymptotic normality of the proposed estimator is established. A proximal alternating direction method of multipliers (ADMM) algorithm is developed for distributed statistical optimization on a large quantity of data. Simulation studies are performed to assess the finite-sample performance of the proposed method. Survey data from the Behavioral Risk Factor Surveillance System (BRFSS) is used to demonstrate the utility of the proposed method in practice. INDEX TERMS CSL function, expectile regression, large-scale data, missing at random, proximal ADMM algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.