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Modeling Stylized Facts in FX Markets with FINGAN-BiLSTM: A Deep Learning Approach to Financial Time Series.

Dong-Jun Kim1, Do-Hyeon Kim1, Sun-Yong Choi1

  • 1Department of Finance and Big Data, Gachon University, Seongnam 13120, Republic of Korea.

Entropy (Basel, Switzerland)
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The new financial generative adversarial network-bidirectional long short-term memory (FINGAN-BiLSTM) model accurately captures foreign exchange market dynamics. This deep learning approach improves financial risk assessment and derivative pricing by mimicking complex data patterns.

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Area of Science:

  • * Computational Finance
  • * Deep Learning
  • * Financial Econometrics

Background:

  • * Foreign exchange (FX) markets exhibit complex statistical properties like heavy tails, volatility clustering, and leverage effects.
  • * Traditional models often struggle to capture these stylized facts, leading to potential inaccuracies in financial applications.
  • * Unidirectional deep learning architectures can suffer from information loss, limiting their effectiveness in time-series analysis.

Purpose of the Study:

  • * To propose and evaluate the financial generative adversarial network-bidirectional long short-term memory (FINGAN-BiLSTM) model.
  • * To accurately reproduce the complex statistical properties and stylized facts of FX market log returns.
  • * To overcome information loss in unidirectional models by incorporating past and future information.

Main Methods:

  • * Integration of a bidirectional long short-term memory (BiLSTM) network into the FINGAN framework.
  • * Generator, discriminator, and predictor networks simultaneously process past and future information.
  • * Evaluation using metrics like the Kolmogorov-Smirnov statistic to assess distributional and dynamic pattern replication.

Main Results:

  • * FINGAN-BiLSTM effectively mimics distributional and dynamic patterns of actual FX data.
  • * Significant reduction in cumulative distribution discrepancy for high-volatility assets (e.g., CAD, MXN).
  • * Precise replication of volatility clustering and leverage effects, outperforming conventional models.

Conclusions:

  • * The FINGAN-BiLSTM model demonstrates strong performance in capturing FX market stylized facts.
  • * Proposed deep learning model shows promise for financial risk assessment, derivative pricing, and portfolio optimization.
  • * Further research is needed to enhance generalization by integrating exogenous economic variables.