Robust Risk-Aware Reinforcement Learning

Jaimungal, Sebastian; Pesenti, Silvana M.; Wang, Ye Sheng; Tatsat, Hariom

doi:10.2139/ssrn.3910498

Cited by 6 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coache and Jaimungal (2021) develops a framework combining policy-gradient-based RL method and dynamic convex risk measures for solving time-consistent risk-sensitive stochastic optimization problems. However, there is no sample complexity or asymptotic convergence studied for the proposed algorithms in Jaimungal et al (2021); Coache and Jaimungal (2021).…”

Section: Further Developments For Mathematical Finance and Reinforcem...mentioning

confidence: 99%

“…Very recently, Jaimungal et al. (2021) proposed a robust risk‐aware RL framework via robust optimization and with a rank‐dependent expected utility function. Financial applications such as statistical arbitrage and portfolio optimization are discussed with detailed numerical examples.…”

Section: Further Developments For Mathematical Finance and Reinforcem...mentioning

confidence: 99%

“…However, there is no sample complexity or asymptotic convergence studied for the proposed algorithms in Jaimungal et al. (2021); Coache and Jaimungal (2021).…”

Section: Further Developments For Mathematical Finance and Reinforcem...mentioning

confidence: 99%

See 2 more Smart Citations

Recent advances in reinforcement learning in finance

Hambly

Yang

2023

Mathematical Finance

View full text Add to dashboard Cite

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision‐making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value‐ and policy‐based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. We then discuss in detail the application of these RL algorithms in a variety of decision‐making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo‐advising. Our survey concludes by pointing out a few possible future directions for research.

show abstract

Section: Further Developments For Mathematical Finance and Reinforcem...mentioning

confidence: 99%

Section: Further Developments For Mathematical Finance and Reinforcem...mentioning

confidence: 99%

See 1 more Smart Citation

Recent advances in reinforcement learning in finance

Hambly

Yang

2023

Mathematical Finance

View full text Add to dashboard Cite

show abstract

“…Although we here focus on the traditional presentation of RL in discrete time, let us mention that a continuous-time stochastic optimal control viewpoint on RL has been developed for the mean-variance portfolio problem [170] and for generic continuous time and space problems [169]. It has also been extended in several directions, such as risk-aware problems in [114] and mean-field games in [91]) and [67].…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Recent Developments in Machine Learning Methods for Stochastic Control and Games

Laurière

2022

SSRN Journal

View full text Add to dashboard Cite

“…Another possible perspective to account for model uncertainty is to modify the functional form of a risk measure by distorting the probability measure or a quantile, see for instance Belles-Sampera et al (2014); Wang and Ziegel (2018) for distorted versions of VaR or more recently Jaimungal et al (2021) for a distortion of a utility based risk measure. In Wang and Ziegel (2018) also different notions of law-invariance for robust versions of VaR and ES are discussed.…”

mentioning

confidence: 99%

Risk measures under model uncertainty: a Bayesian viewpoint

Cuchiero¹,

Gazzani²,

Klein³

2022

Preprint

View full text Add to dashboard Cite

We introduce two kinds of risk measures with respect to some reference probability measure, which both allow for a certain order structure and domination property.Analyzing their relation to each other leads to the question when a certain minimax inequality is actually an equality. We then provide conditions under which the corresponding robust risk measures, being defined as the supremum over all risk measures induced by a set of probability measures, can be represented classically in terms of one single probability measure. We focus in particular on the mixture probability measure obtained via mixing over a set of probability measures using some prior, which represents for instance the regulator's beliefs. The classical representation in terms of the mixture probability measure can then be interpreted as a Bayesian approach to robust risk measures.

show abstract

Robust Risk-Aware Reinforcement Learning

Cited by 6 publications

References 10 publications

Recent advances in reinforcement learning in finance

Recent advances in reinforcement learning in finance

Recent Developments in Machine Learning Methods for Stochastic Control and Games

Risk measures under model uncertainty: a Bayesian viewpoint

Contact Info

Product

Resources

About