Purpose The purpose of this paper is to analyze how monetary fundamentals affect exchange rate movements. Design/methodology/approach To develop this paper, a Bayesian Network modeling is applied to explore the causal interactions between monetary fundamentals and exchange rate fluctuations. Subsequently, a sensitivity analysis is performed to asses and estimate exchange rate behavior with uncertain monetary fundamentals. Furthermore, a Granger Causality test is used as suggested in the Econometric literature to determine the causality direction among factors. Findings The empirical findings show that money supply and interest rate have a significant positive effect on exchange rate, whereas inflation rate has a considerable negative effect on exchange rate. In addition, the authors deduce that real income has an indirect impact on exchange rate and a direct impact on inflation rate, interest rate and money supply. Furthermore, the sensitivity analysis shows that monetary uncertainty has a considerable effect on exchange rate fluctuations. Moreover, the Granger Causality test reveals that there is a unique unidirectional causality running from money supply to exchange rate. Practical implications The model can be considered as a vital management tool for international investors and financial analysts to explore the effect of monetary fundamentals on exchange rate behavior. It allows estimating exchange rate fluctuations with uncertain monetary factors. Originality/value This study is the first one which applied a Bayesian Network modeling to examine the exchange rate determination problem. Results of this research are presented under a clear graphical representation that can be easily useful by monetary policymakers and international traders to determine the influential monetary factors on exchange rate behavior. Also, the model will help them in estimating the effect of monetary uncertainty on exchange rate fluctuations.

This book series features volumes composed of select contributions from workshops and conferences in all areas of current research in mathematics and statistics, including OR and optimization. In addition to an overall evaluation of the interest, scientific quality, and timeliness of each proposal at the hands of the publisher, individual contributions are all refereed to the high quality standards of leading journals in the field. Thus, this series provides the research community with well-edited, authoritative reports on developments in the most exciting areas of mathematical and statistical research today.

In this paper, we introduced the infinite continuous mixture of Dirichlet distributions as a generalization of the infinite mixture of Dirichlet ones, in order to avoid the limitation of choosing the a priori sample size for the expectation a posteriori estimator. Monte-Carlo sampling was used in order to obtain the posterior distributions mixture, since this mixture is difficult to get analytically. A new parametrization of this proposed distribution was achieved. Then, we suggested a mixture expectation a posteriori estimator of the unknown parameters. The proposed estimator solves the problem of how to construct a Bayesian estimation of proportions without specifying particular parameters and sample size of the prior knowledge. Some asymptotic properties of this estimator were derived, specifically, its bias and variance. The consistency and asymptotic normality of the estimator were also established when the sample size tends to infinity and its credible interval was determined. The performance of the proposed estimator was illustrated theoretically and by means of a simulation study. Ultimately, a comparative simulation study between the learned estimates, the proposed mixture expectation a posteriori, standard Bayesian estimator, maximum likelihood and Jeffreys estimator, was established. According to this simulation, we were able to conclude that the prior infinite mixture of Dirichlet distributions offers higher accuracy and flexibility for modeling and learning data.