arXiv research

New algorithm reduces training time for deep learning in financial hedging.

Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…

Algorithm for hedging American options with transaction costs.

Optimizes credit index option hedging with reinforcement learning.

Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.

This paper compares eight DRL algorithms for dynamic hedging.

This article analyzes the relationship between co-persistence and hedging which indicates co-persistence ratio is just the long-term hedging ratio. The new method of exhaustive search algorithm for deriving co-persistence ratio is derived in the article. And we also develop a new hedging strategy of combining co-persis…

In this paper, we study the behavior of the Hedge algorithm in the online stochastic setting. We prove that anytime Hedge with decreasing learning rate, which is one of the simplest algorithm for the problem of prediction with expert advice, is surprisingly both worst-case optimal and adaptive to the easier stochastic …

New dual approach for hedging Bermudan options efficiently.

Deep Hedging learns optimal strategies for various risk levels.

The article is devoted to investigating the application of hedging strategies to online expert weight allocation under delayed feedback. As the main result, we develop the General Hedging algorithm $\mathcal{G}$ based on the exponential reweighing of experts' losses. We build the artificial probabilistic framework and …

Risk-averse reinforcement learning optimizes option hedging.

Reinforcement learning improves option pricing and hedging accuracy.

Hedging in the presence of transaction costs leads to complex optimization problems. These problems typically lack closed-form solutions, and their implementation relies on numerical methods that provide hedging strategies for specific parameter values. In this paper we use a genetic programming algorithm to derive exp…

This paper begins with a study on the dual representations of risk and regret measures and their impact on modeling multistage decision making under uncertainty. A relationship between risk envelopes and regret envelopes is established by using the Lagrangian duality theory. Such a relationship opens a door to a decomp…

New method for insurance valuation combining hedging and risk minimization.

A new algorithm for high-dimensional hedging problems.

Optimal transport reformulates multiple quantile hedging problem.

Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.

Enhanced hedging for S&P 500 options using volatility surface data.

A semi-static approach efficiently replicates and prices callable interest rate derivatives.

QLBS and RLOP methods improve option pricing and hedging performance.

This paper presents hedging strategies for European and exotic options in a Levy market. By applying Taylor's Theorem, dynamic hedging portfolios are con- structed under different market assumptions, such as the existence of power jump assets or moment swaps. In the case of European options or baskets of European optio…

RL and DTSOC for final quadratic hedging performance studied.

We present an algorithm producing a dynamic non-self-financing hedging strategy in an incomplete market corresponding to investor-relevant risk criterion. The optimization is a two stage process that first determines admissible model parameters that correspond to the market price of the option being hedged. The second …

New approach to goal-based investing using hedging and reinforcement learning.

This paper simplifies hedge ratios in financial models using pathwise algorithmic differentiation.

Model-free approach to hedge path-dependent options using min-max optimization.

We propose some machine-learning-based algorithms to solve hedging problems in incomplete markets. Sources of incompleteness cover illiquidity, untradable risk factors, discrete hedging dates and transaction costs. The proposed algorithms resulting strategies are compared to classical stochastic control techniques on s…

Study uses deep learning for efficient hedging of long-term financial derivatives.

This thesis proposes a derivatives hedging framework using deep learning and reinforcement learning.

In Electricity markets, illiquidity, transaction costs and market price characteristics prevent managers to replicate exactly contracts. A residual risk is always present and the hedging strategy depends on a risk criterion chosen. We present an algorithm to hedge a position for a mean variance criterion taking into ac…

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear r…

Proposes a new agent-based model for deep hedging that outperforms existing models.

This paper improves financial derivative pricing by incorporating multiple hedging instruments.

Paper presents a machine learning-based method for efficiently pricing and hedging autocallable structured notes with multiple underlying assets.

This paper applies an algorithm for the convolution of compactly supported Legendre series (the CONLeg method) (cf. Hale and Townsend 2014a), to pricing/hedging European-type, early-exercise and discrete-monitored barrier options under a Levy process. The paper employs Chebfun (cf. Trefethen et al. 2014) in computation…

Paper uses RL to optimize derivative hedging with reduced costs.

For a large class of vanilla contingent claims, we establish an explicit Föllmer-Schweizer decomposition when the underlying is an exponential of an additive process. This allows to provide an efficient algorithm for solving the mean variance hedging problem. Applications to models derived from the electricity market a…

Deep Q-learning agent outperforms traditional hedging in S&P 500 options.

Optimistic Hedge achieves optimal regret bounds in two-player zero-sum games.

New metric to measure liquidity position PNL, delta hedging algorithm for automated market makers.

The paper proposes a new algorithm for dealer markets that incorporates hedging and market impact.

We develop algorithms for the numerical computation of the quadratic hedging strategy in incomplete markets modeled by pure jump Markov process. Using the Hamilton-Jacobi-Bellman approach, the value function of the quadratic hedging problem can be related to a triangular system of parabolic partial integro-differential…

Paper proposes novel hedging strategies using LSTM models for diversified investment portfolios.

Paper uses RL for dynamic swaption hedging, outperforming traditional methods.

Method constructs hedging portfolio for carbon risk but not ESG risk.

Study develops efficient nested deep hedging method for derivatives pricing.