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

May 29, 2026
Kohei Maehashi*1
Daisuke Miyakawa*2
Takatoshi Sasaki*3

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.

JEL Classification
C22, C55, F31, G17.

Keywords
Currency swing, granular data, trade repository, foreign exchange option, quantile regression, variable selection

The authors thank Yoshihiko Hogen, Mikari Kashima, Tomiyuki Kitamura, Kohei Matsumura, Makoto Minegishi, Koji Nakamura, Yoshihiko Norimasa for their valuable comments and discussions. The authors acknowledge the provision of confidential data from Japan's Financial Services Agency. Any errors in this paper are those of the authors themselves. The views here are the author's own and do not reflect the views of any other organization or entity mentioned in the work.

  1. *1 Previously in Financial Markets Department, Bank of Japan
  2. *2School of Commerce, Waseda University
    E-mail : damiyak@waseda.jp
  3. *3Financial Markets Department, Bank of Japan
    E-mail : takatoshi.sasaki@boj.or.jp

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