Hedging with Neural Networks

Ruf, Johannes; Wang, Weiguan

doi:10.2139/ssrn.3580132

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…This work provides a review of this literature. The motivation for this summary arose from our companion paper Ruf and Wang [2020]. There we continue the discussions of this note; in particular, of potentially problematic data leakage when training ANNs to historical financial data.…”

Section: Introductionmentioning

confidence: 81%

“…When an ANN is trained on a training set constructed in such a way, the error on the test set underestimates the generalisation error of the ANN. Yao et al [2000] and our companion paper Ruf and Wang [2020] provide more discussion on this point. Some papers only work with independent draws from various distributions, and therefore do not involve any time series structure.…”

Section: Data Partition Methodsmentioning

confidence: 99%

“…The first papers are Carverhill and Cheuk [2003], Chen and Sutcliffe [2012], and Shin and Ryu [2012]. More recently, Buehler et al [2019a,b] and Ruf and Wang [2020] follow up on this line of research. Buehler et al [2019b] consider also the hedging of exotic options such as barrier options.…”

Section: Outputsmentioning

confidence: 99%

“…In addition to the Black-Scholes formula, other widely used parametric benchmarks are stochastic volatility pricing models; e.g., used in Gençay and Gibson [2007], Jang and Lee [2019], or Liu et al [2019b]. Ruf and Wang [2020] observe that if a benchmark is chosen that incorporates both delta and vega hedging then an ANN does not outperform even a simple two-factor regression model. For American type options, benchmarks used are the Barone-Adesi and Whaley [1987] pricing method (e.g., Lajbcygier [2002]), and the Cox-Ross-Rubinstein model (e.g., Chen and Lee [1999]).…”

Section: Performance Measures and Benchmarksmentioning

confidence: 99%

“…As this is a subjective selection, we also would like to highlight our companion paper Ruf and Wang [2020], which provides a new benchmark based on delta-vega hedging and discusses data leakage issues.…”

Section: Recommended Papersmentioning

confidence: 99%

See 4 more Smart Citations

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

Self Cite

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Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

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Section: Introductionmentioning

confidence: 81%

Section: Data Partition Methodsmentioning

confidence: 99%

Section: Outputsmentioning

confidence: 99%

Section: Performance Measures and Benchmarksmentioning

confidence: 99%

Section: Recommended Papersmentioning

confidence: 99%

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Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

Self Cite

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Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Olorunnimbe

Viktor

2022

Artif Intell Rev

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The widespread usage of machine learning in different mainstream contexts has made deep learning the technique of choice in various domains, including finance. This systematic survey explores various scenarios employing deep learning in financial markets, especially the stock market. A key requirement for our methodology is its focus on research papers involving backtesting. That is, we consider whether the experimentation mode is sufficient for market practitioners to consider the work in a real-world use case. Works meeting this requirement are distributed across seven distinct specializations. Most studies focus on trade strategy, price prediction, and portfolio management, with a limited number considering market simulation, stock selection, hedging strategy, and risk management. We also recognize that domain-specific metrics such as “returns” and “volatility” appear most important for accurately representing model performance across specializations. Our study demonstrates that, although there have been some improvements in reproducibility, substantial work remains to be done regarding model explainability. Accordingly, we suggest several future directions, such as improving trust by creating reproducible, explainable, and accountable models and emphasizing prediction of longer-term horizons—potentially via the utilization of supplementary data—which continues to represent a significant unresolved challenge.

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Deep empirical risk minimization in finance: Looking into the future

Reppen

Soner

2022

Mathematical Finance

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Many modern computational approaches to classical problems in quantitative finance are formulated as empirical loss minimization (ERM), allowing direct applications of classical results from statistical machine learning. These methods, designed to directly construct the optimal feedback representation of hedging or investment decisions, are analyzed in this framework demonstrating their effectiveness as well as their susceptibility to generalization error. Use of classical techniques shows that over‐training renders trained investment decisions to become anticipative, and proves overlearning for large hypothesis spaces. On the other hand, nonasymptotic estimates based on Rademacher complexity show the convergence for sufficiently large training sets. These results emphasize the importance of synthetic data generation and the appropriate calibration of complex models to market data. A numerically studied stylized example illustrates these possibilities, including the importance of problem dimension in the degree of overlearning, and the effectiveness of this approach.

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Hedging with Neural Networks

Cited by 6 publications

References 23 publications

Neural networks for option pricing and hedging: a literature review

Neural networks for option pricing and hedging: a literature review

Deep learning in the stock market—a systematic survey of practice, backtesting, and applications

Deep empirical risk minimization in finance: Looking into the future

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