Modelling and forecasting US Dollar/Malaysian Ringgit exchange rate

Abstract

Exchange rate forecasts are important because these forecasts help in hedging decisions, capital budgeting decisions and earnings assessments. While numerous methods are available for forecasting exchange rates, the current study employs time series models to forecast daily data of US Dollar exchange rate against Malaysian Ringgit (USD/MYR). Using hybrid ARIMA-GARCH and hybrid ARIMA-EGARCH models, the modelling and forecasting performances are compared using Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE) respectively. Such findings are important since exchange rate forecasts can help to evaluate the foreign denominated cash flows involved in international transactions.

Forecasting Malaysian Ringgit Using Exponential Smoothing Techniques

“…Findings showed that the non-linear model, STAR performed better than the linear model, AR. On the other hand, hybrid ARIMA-GARCH and hybrid ARIMA-EGARCH models were employed to forecast daily data of the USD exchange rate against MYR (Mustafa, Ahmad, & Ismail, 2017). The volatility and leverage effect of the series fitted and performed better by ARIMA-EGARCH.…”

Multivariate Escher Transformed Laplace Distribution and Its Generalization

“…Because of the wide applicability of time series models with non-Gaussian marginals in various fields such as biological, engineering, telecommunications, etc., many researchers are concentrating on introducing time series models with non-Gaussian univariate and multivariate marginals. For more reference see, Jose and Seethalekshmi [9], Alexandre Trindade et al [10], Jayakumar and Kuttikrishanan [11], Dais et al [7] and Asma Mustafa et al [12].…”