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Heterogeneous Views and Currency Swing Prediction: Evidence from Trade Repository Data

Author

Listed:
  • Kohei Maehashi

    (Bank of Japan)

  • Daisuke Miyakawa

    (Waseda University)

  • Takatoshi Sasaki

    (Bank of Japan)

Abstract

In this paper, we develop a model to predict large currency swings using transaction-level data on foreign exchange options, collected by trade repositories (TRs). These data allow us to capture heterogeneous currency risk perceptions of individual market participants. By applying a quantile regression combined with machine learning for variable selection, we find that market participants' views extracted from trade repository data significantly improve the predictions of large currency swings.

Suggested Citation

  • Kohei Maehashi & Daisuke Miyakawa & Takatoshi Sasaki, 2026. "Heterogeneous Views and Currency Swing Prediction: Evidence from Trade Repository Data," Bank of Japan Working Paper Series 26-E-10, Bank of Japan.
    • Handle: RePEc:boj:bojwps:wp26e10
    as

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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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