Deep Learning, Jumps, and Volatility Bursts

Bashchenko, Oksana; Marchal, Alexis

doi:10.2139/ssrn.3452933

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2020

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“…However in order to estimate σ(t , S t ) we need to obtain the paths of pure diffusion processes. To do so we rely on the method developed by Bashchenko and Marchal (2019) in order to detect and remove the jumps in the data. Then we perform the bubble detection on the synthetic pure-diffusion path and after that trade on the original path (with jumps included).…”

Section: Application To Real Datamentioning

confidence: 99%

Deep Learning for Asset Bubbles Detection

Bashchenko

Marchal

2020

SSRN Journal

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We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection of bubbles. We show the outperformance of our algorithm over the existing statistical method in a laboratory created with simulated data. We then apply the network classification to real data and build a zero net exposure trading strategy that exploits the risky arbitrage emanating from the presence of bubbles in the US equity market from 2006 to 2008. The profitability of the strategy provides an estimation of the economical magnitude of bubbles as well as support for the theoretical assumptions relied on.

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Section: Application To Real Datamentioning

confidence: 99%