Hui Chen, Antoine Didisheim, Simon Scheidegger
arXiv 18 Feb 2021 · Econometrics · 3 citations (OpenAlex)
arXiv:2102.09209 · PDF · DOI · OpenAlex · Extracted main text
We propose a novel structural estimation framework in which we train a surrogate of 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andersen, Torben G, Nicola Fusari, and Viktor Todorov (2015) Parametric inference and dynamic state recovery from option panels, Econometrica\/ 83, 1081–1145 | 1.000 | 6 | 4 | 100% |
| 2 | Bates, David S (1996) Jumps and stochastic volatility: Exchange rate processes implicit in deutsche mark options, The Review of Financial Studies\/ 9,… | 1.000 | 5 | 3 | 100% |
| 3 | Heston, Steven L (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options, The review of fina… | 0.843 | 3 | 3 | 100% |
| 4 | Andersen, Torben G, Nicola Fusari, and Viktor Todorov (2017) Short-term market risks implied by weekly options, The Journal of Finance\/ 72, 1335–1386 | 0.737 | 3 | 2 | 100% |
| 5 | Ramachandran, Prajit, Barret Zoph, and Quoc V. Le (2017) Swish: a self-gated activation function, arXiv: Neural and Evolutionary Computing\/ | 0.644 | 2 | 2 | 100% |
| 6 | Tripathy, Rohit K., and Ilias Bilionis (2018) b, Deep uq: Learning deep neural network surrogate models for high dimensional uncertainty quantification, Journal of Computatio… | 0.644 | 2 | 2 | 100% |
| 7 | Hornik, Kurt, Maxwell Stinchcombe, and Halbert White (1989) Multilayer feedforward networks are universal approximators, Neural networks\/ 2, 359–366 | 0.511 | 2 | 1 | 100% |
| 8 | Igami, Mitsuru (2020) Artificial intelligence as structural estimation: Deep Blue, Bonanza, and AlphaGo, The Econometrics Journal\/ 23, S1–S24 | 0.405 | 1 | 1 | 100% |
| 9 | Iskhakov, Fedor, John Rust, and Bertel Schjerning (2020) Machine learning and structural econometrics: contrasts and synergies, The Econometrics Journal\/ 23, S81–S124 | 0.405 | 1 | 1 | 100% |
| 10 | Welch, Ivo, and Amit Goyal (2007) A Comprehensive Look at The Empirical Performance of Equity Premium Prediction, The Review of Financial Studies\/ 21, 1455–1508 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 69 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Deep Learning for Individual Heterogeneity | 0.405 | 1 | 1 |