Deep Learning, Jumps, and Volatility Bursts

Oksana Bashchenko and Alexis Marchal
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Oksana Bashchenko: HEC Lausanne; Swiss Finance Institute
Alexis Marchal: EPFL; SFI

No 20-10, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: Jumps; Volatility Burst; High-Frequency Data; Deep Learning; LSTM (search for similar items in EconPapers)
JEL-codes: C14 C32 C45 C58 G17 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2020-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2010

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