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Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH
2020
IEEE Access
The motivation of this study is built from the previous research to find a way to enhance the forecast of advanced and emerging market currency volatilities. Given the exchange rate's nonlinear and time-varying characteristics, we introduce the neural networks (NN) approach to enhance the Markov Switching Beta-Exponential Generalized Autoregressive Conditional Heteroscedasticity (MS-Betat-EGARCH) model. Our hybrid model synthesizes these two approaches' advantages to predict exchange rate
doi:10.1109/access.2020.3038564
fatcat:27ovlgomvjacbnlsef7csomnci