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Deep Learning, Jumps, and Volatility Bursts

Author

Listed:
  • Oksana Bashchenko

    (HEC Lausanne; Swiss Finance Institute)

  • Alexis Marchal

    (EPFL; SFI)

Abstract

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.

Suggested Citation

  • Oksana Bashchenko & Alexis Marchal, 2020. "Deep Learning, Jumps, and Volatility Bursts," Swiss Finance Institute Research Paper Series 20-10, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2010
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    More about this item

    Keywords

    Jumps; Volatility Burst; High-Frequency Data; Deep Learning; LSTM;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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