Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

Ruafan Liao, Woraphon Yamaka, Songsak Sriboonchitta
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
more » ... lity. We validate the performance of our proposed model by comparing it with various traditional volatility forecasting models. In-sample and out-of-sample volatility forecasts are considered to achieve our comparison. The empirical results suggest that our hybrid NN-MS Beta-t-EGARCH outperforms the other models for both emerging and advanced market currencies. INDEX TERMS Exchange rate volatility, neural networks, Markov-switching Beta-t-EGARCH.
doi:10.1109/access.2020.3038564 fatcat:27ovlgomvjacbnlsef7csomnci