Does Introducing Futures Markets Affect Currency Variance Forecasts? Evidence from Asian Markets

Walker Hughen (), Loran Chollete (), Weijia Peng () and Teresa Starzecki ()
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Walker Hughen: Sacred Heart University
Loran Chollete: Sacred Heart University
Weijia Peng: Sacred Heart University
Teresa Starzecki: Salve Regina University

Eastern Economic Journal, 2025, vol. 51, issue 4, No 4, 627-654

Abstract: Abstract We investigate how currency volatility forecasts change after nations introduce markets for currency futures. We study three Asian markets—South Korea, India, and China. For each exchange rate, we estimate both a machine learning model—specifically the Long Short-Term Memory (LSTM) model—and GARCH models, and compare their ability to forecast realized volatility during two periods: one directly before and one directly after the introduction of futures. We find that a Naïve historical volatility forecast typically outperforms both in-sample and real-time GARCH forecasts, and that the LSTM model outperforms its GARCH counterparts. For both GARCH and LSTM model, the mean absolute percentage error and root mean squared percentage error always increase after the introduction of futures. This latter finding indicates that realized volatility is more difficult to forecast in the aftermath of futures introduction.

Keywords: Exchange rates; Forecasting; Futures; Realized volatility; Machine learning; F31; F47; G13; G15 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41302-025-00305-9

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