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Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates

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Listed:
  • Indranil Ghosh
  • Tamal Datta Chaudhuri

Abstract

Foreign currency is bought and sold in the financial markets, every minute, every day, on trading days, like any commodity or stocks of companies. The players in this market are (a) people with underlying interest in foreign currency such as exporters and importers who are continuously hedging in futures or options markets, (b) speculators and (c) arbitrageurs. This paper focuses on this microeconomic flavour of foreign currency as a continuously tradable product and presents a granular framework for forecasting the exchange rate. We initially investigate year-wise inherent nature of movements of three exchange rates, namely Indian rupee/US dollar, Indian rupee/euro and Indian rupee/Japanese yen, during 2011–2016 through Mandelbrot’s single fractal model. Subsequently, maximal overlap discrete wavelet transformation (MODWT) is used to decompose the time series of the individual exchange rates. Random forest and bagging are applied on the decomposed components for predictive modelling.

Suggested Citation

  • Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.
    • Handle: RePEc:sae:miceco:v:5:y:2017:i:2:p:105-131
    DOI: 10.1177/2321022217724978
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    References listed on IDEAS

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