Deep Structural Estimation:With an Application to Option Pricing

Hui Chen, Antoine Didisheim and Simon Scheidegger

Cahiers de Recherches Economiques du Département d'économie from Université de Lausanne, Faculté des HEC, Département d’économie

Abstract: We propose a novel structural estimation framework in which we train a surrogateof an economic model with deep neural networks. Our methodology alleviates the curse of dimensionality and speeds up the evaluation and parameter estimation by orders of magnitudes, which significantly enhances one's ability to conduct analyses that require frequent parameter re-estimation. As an empirical application, we compare two popular option pricing models (the Heston and the Bates model with double-exponential jumps)against a non-parametric random forest model. We document that: a) the Bates model produces better out-of-sample pricing on average, but both structural models fail to outperform random forest for large areas of the volatility surface; b) random forest is more competitive at short horizons (e.g., 1-day), for short-dated options (with less than 7 days to maturity), and on days with poor liquidity; c) both structural models outperform random forest in out-of-sample delta hedging; d) the Heston model's relative performance has deteriorated significantly after the 2008 financial crisis.

Keywords: Deep Learning; Structural Estimation; Option Pricing; Parameter Stability (search for similar items in EconPapers)
JEL-codes: C45 C52 C58 C61 G17 (search for similar items in EconPapers)
Pages: 57 pp.
Date: 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Deep Structural Estimation: With an Application to Option Pricing (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:lau:crdeep:21.14

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