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Short-term Forecasting Ability of Hybrid Models for BRIC Currencies

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  • Latha Sreeram
  • Samie Ahmed Sayed

Abstract

This article proposes a new framework to improve short-term forecasting accuracy of exchange rates of BRIC nations, that is, Brazil (USD/BRL), Russia (USD/RUB), India (USD/INR) and China (USD/CNY). The study employs three methodologies for a 42-day forecast: hybrid models based on least square support vector machine, residual hybrid model and automatic hybrid model forecasting using R software. The results show that the proposed residual hybrid model framework, including autoregressive integrated moving average-artificial neural network (ARIMA–ANN)-TBATS, outperformed other models with Brazil and China return series reflecting the best accuracy in ANN model and India and Russia demonstrating the best accuracy in trigonometric seasonal, box-cox transformation, ARIMA residuals, trend and seasonality (TBATS) model. Further, the results indicate that Brazil and China return series follow a non-linear pattern, while India and Russia follow a non-linear complex seasonal pattern. The highest level of forecast accuracy has been observed in China followed by Brazil, India and Russia.

Suggested Citation

  • Latha Sreeram & Samie Ahmed Sayed, 2024. "Short-term Forecasting Ability of Hybrid Models for BRIC Currencies," Global Business Review, International Management Institute, vol. 25(3), pages 585-605, June.
    • Handle: RePEc:sae:globus:v:25:y:2024:i:3:p:585-605
    DOI: 10.1177/0972150920954615
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    References listed on IDEAS

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