Deep hedging

Buehler, Hans; Gonon, Lukas; Teichmann, Josef; Wood, B. Dan

doi:10.1080/14697688.2019.1571683

Cited by 222 publications

(106 citation statements)

References 26 publications

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“…our method learns a dynamic hedging strategy in the spirit of Han et al (2018) and Buehler et al (2019). However, here, the continuation value approximations learned during the construction of the optimal stopping strategy can be used to break the hedging problem down into a sequence of smaller problems that learn the hedging portfolio only from one possible exercise date to the next.…”

Section: Introductionmentioning

confidence: 99%

Pricing and Hedging American-Style Options with Deep Learning

Becker

Cheridito

Jentzen

2020

JRFM

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In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.

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Section: Introductionmentioning

confidence: 99%

Pricing and Hedging American-Style Options with Deep Learning

Becker

Cheridito

Jentzen

2020

JRFM

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“…Until now, the replication paradigm has led to very sophisticated models. The future may belong to deep hedging arguments [ 107 ]. Still, whether we consider models or algorithms, interaction will always be a topic of interest.…”

Section: Discussionmentioning

confidence: 99%

Learning agents in Black–Scholes financial markets

2020

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Black–Scholes (BS) is a remarkable quotation model for European option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes; however, in practice, it varies. How do traders come to learn these parameters? We introduce natural agent-based models, in which traders update their beliefs about the true implied volatility based on the opinions of other agents. We prove exponentially fast convergence of these opinion dynamics, using techniques from control theory and leader-follower models, thus providing a resolution between theory and market practices. We allow for two different models, one with feedback and one with an unknown leader.

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“…Reinforcement learning (RL, Sutton and Barto 2018) is nowadays an established approach to address a variety of real-world sequential decision making problems. Successful results have been achieved in numerous fields such as robotics (eg., Kober et al 2013;Levine et al 2016), recommender systems (eg., Shani et al 2005;Warlop et al 2018), financial trading (eg., Dempster and Romahi 2002;Nevmyvaka et al 2006;Buehler et al 2019), and autonomous driving (eg., Kiran et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

et al. 2021

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In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

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Deep hedging

Cited by 222 publications

References 26 publications

Pricing and Hedging American-Style Options with Deep Learning

Pricing and Hedging American-Style Options with Deep Learning

Learning agents in Black–Scholes financial markets

Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

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