A Hybrid Forecast of Exchange Rate based on ARFIMA,Discrete Grey-Markov, and Fractal Kalman Model

TL;DR

This paper introduces a hybrid forecasting model combining extended discrete grey Markov, variable dimension Kalman, and grey incidence methods, significantly improving exchange rate prediction accuracy over traditional models.

Contribution

The paper presents a novel hybrid forecasting approach that integrates grey Markov, Kalman, and grey incidence techniques for enhanced exchange rate prediction.

Findings

Hybrid model outperforms traditional grey Markov and Kalman models

Hybrid approach shows superior accuracy compared to ARFIMA and Kalman methods

Simulation results confirm improved forecast performance

Abstract

We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combined with degree of grey incidence are better than grey Markov and ARFIMA model or Kalman methods.