Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.
problem Optimizing financial portfolio hedging with derivatives and trading frictions.
method Actor-critic reinforcement learning algorithm with continuous state and action spaces.
result Trained model provides optimal hedge for any initial portfolio and market state.
The paper explores neural networks for improving delta hedging in financial markets.
problem Real-world financial markets do not perfectly match the assumptions of the Black-Scholes model.
method The authors test various neural architectures (RNN, TCN, Attention, MLP) for delta hedging and combine them with traditional models.
result NNHedge framework provides a pipeline for model development and assessment.
Study uses deep learning for efficient hedging of long-term financial derivatives.
problem Optimizing hedging strategies for long-term financial derivatives with various penalties and stylized facts.
method Deep reinforcement learning applied to neural networks optimizing hedging policies with quadratic and non-quadratic penalties.
result Non-quadratic global hedging policies result in significantly smaller downside risk metrics and significant hedging gains.
New method reduces training time for deep hedging networks.
problem Challenges in training deep hedging networks with large batch sizes.
method Integrates topological features to reduce batch sizes.
result Practical training of deep hedging models without sacrificing performance.
ANADDH uses deep learning to improve volatility risk management.
problem Traditional Vega hedging strategies are inadequate for rapidly changing markets.
method Combines distributional reinforcement learning with adaptive Nesterov acceleration.
result Significant performance gains over existing hedging techniques.
In this paper, we consider the problem of hedging Asian options in financial markets with transaction costs. For this, we use the asymptotic hedging approach. The main task of asymptotic hedging in financial markets with transaction costs is to prove the probability convergence of the terminal value of the investment p…
The paper approximates financial derivatives using neural networks and iterated integrals.
problem Approximating p-integrable financial derivatives. method Using iterated Stratonovich integrals and neural networks.
result Approximate solutions to the Lp-hedging problem. DHLNN improves deep hedging for financial derivatives with faster convergence and better stability.
problem Challenges in computational inefficiency, sensitivity to noisy data, and optimization complexity in deep hedging methods.
method Integrates periodic fixed-gradient optimization and linearized training dynamics to stabilize and accelerate deep learning model training.
result Demonstrates faster convergence, improved stability, and superior hedging performance across diverse market scenarios.
HedgeNet uses neural networks to reduce hedging errors for financial options.
problem Reducing hedging errors for financial options.
method Designing HedgeNet to minimize hedging error, trained on S&P 500 and Euro Stoxx 50 options.
result HedgeNet significantly reduces hedging error compared to Black-Scholes benchmark.
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
Study compares model-free valuation to actual financial outcomes, finds it slightly conservative.
problem Evaluating the quality of model-free valuation approaches for financial derivatives.
method Empirical analysis using historical option prices from S&P 500 constituents.
result Model-free valuation approaches are only marginally more conservative than industry-standard models.
The paper compares traditional regression with modern neural network methods for financial hedging and risk compression.
problem Finding optimal hedge ratios and managing portfolio risk using traditional regression methods has limitations.
method The paper introduces regularization techniques and common factor analyses using neural networks to improve upon regression methods.
result Neural network methods provide better performance in hedge ratio estimation and risk compression compared to traditional regression.
This paper improves financial derivative pricing by incorporating multiple hedging instruments.
problem Valuation of financial derivatives with multiple hedging instruments.
method Deep hedging algorithm and reinforcement learning to solve global hedging problems.
result Including options as hedging instruments can significantly decrease equal risk prices and market incompleteness.
The paper tests if optimal hedge ratios for Bitcoin are position-dependent.
problem Testing if optimal hedge ratios for Bitcoin are position-dependent.
method Explicit and efficient method for testing symmetric vs. asymmetric optimal hedge ratios in a multivariate setting.
result The optimal hedge ratio for Bitcoin is position-dependent, with long positions having a higher ratio than short positions.
New AI models improve financial hedging by reducing shortfall and tail risk.
problem Static model calibration gaps in derivatives markets.
method Two reinforcement learning frameworks: RLOP and QLBS.
result RLOP reduces shortfall frequency and improves tail risk in stress scenarios.
We study hedging and pricing of unattainable contingent claims in a non-Markovian regime-switching financial model. Our financial market consists of a bank account and a risky asset whose dynamics are driven by a Brownian motion and a multivariate counting process with stochastic intensities. The interest rate, drift, …
LLMs improve stock price forecasting from financial news and reports.
problem Predicting stock prices with high accuracy and robustness.
method Analyzing financial news, reports, and transcripts using LLMs.
result LLMs can improve stock price forecasting but face practical challenges.
Model for hedging price and quantity risks in electricity markets.
problem Hedging risks for energy retailers in a regulated electricity market.
method Closed-form solution for optimal portfolio using financial instruments based on price and weather indexes.
result Closed-form solution for mean-var model in discrete setting without distributional assumptions.
Framework for robust control under model uncertainty, improving financial derivatives hedging.
problem Model uncertainty in financial derivatives hedging.
method Dynamic programming principle for solving one-step optimization problems.
result Robust hedging strategy outperforms model-based strategies during adverse scenarios.
This report was originally written as an industry white paper on Hedge Funds. This paper gives an overview to Hedge Funds, with a focus on risk management issues. We define and explain the general characteristics of Hedge Funds, their main investment strategies and the risk models employed. We address the problems in H…
Delta hedging, which plays a crucial rôle in modern financial engineering, is a tracking control design for a "risk-free" management. We utilize the existence of trends in financial time series (Fliess M., Join C.: A mathematical proof of the existence of trends in financial time series, Proc. Int. Conf. Systems Theory…
New financial model revises risk measure under NA condition.
problem Revising classical financial mathematics with coherent risk measure on L0. method Developed a new version of the fundamental theorem of asset pricing and provided dual representations.
result Set of risk-hedging prices is closed under NA condition.
The research presented in this work is motivated by some recent papers regarding hedging and valuation of financial securities subject to funding costs, collateralization and counterparty credit risk. Our goal is to provide a sound theoretical underpinning for some results presented in these papers by developing a unif…
Monte Carlo Tree Search improves financial derivative hedging efficiency.
problem Optimizing pricing and hedging of derivative contracts in incomplete markets.
method Integrates tree search techniques with Reinforcement Learning for optimal control problems.
result Monte Carlo Tree Search outperforms Q-learning in sample efficiency and learning speed. Enhances Deep Hedging with K-FAC for financial data.
problem High computational burden in training neural networks for financial applications.
method Integrates Kronecker-Factored Approximate Curvature (K-FAC) optimization with LSTM networks.
result Significant improvements in convergence and hedging efficacy, reducing transaction costs and P&L variance.
This thesis proposes a derivatives hedging framework using deep learning and reinforcement learning.
problem Traditional hedging models fail in complex, uncertain markets due to assumptions like continuous trading and zero transaction costs.
method Integrates deep learning and reinforcement learning, using a spatiotemporal attention-based Transformer for probabilistic forecasting and hedging.
result The proposed method significantly outperforms traditional approaches in U.S. and Chinese financial markets.
This paper analyzes hedge errors in Black-Scholes models using finite difference techniques.
problem Accurate hedging strategies in dynamic market environments.
method Asymptotic approach and finite difference techniques.
result Reduction of hedge errors and enhancement of option pricing model robustness.
Solves super-hedging for financial models with uncertain prices.
problem Super-hedging European or Asian options in discrete-time models with uncertain prices.
method Numerical procedure under AIP condition to compute infimum price.
result Solves super-hedging problem under weak no-arbitrage condition.
New method uses neural networks for better financial hedging.
problem Spanning multi-asset payoffs with vanilla options.
method One-hidden-layer feedforward neural networks for numerical solution.
result Better hedging results with neural networks compared to single-asset approaches.
New algorithm reduces training time for deep learning in financial hedging.
problem Optimal hedging in markets with transaction costs.
method ST-Hedging algorithm combining deep learning and FBSDE solver.
result Achieves state-of-the-art performance and scalability.
Deep learning for financial derivatives pricing and hedging.
problem Model-free pricing and optimal hedging of financial derivatives.
method Neural networks for offline training and online application.
result Accurate model-free price bounds and optimal hedging strategies.
Machine learning risks in finance pricing and hedging
problem Understanding and managing risks in financial models
method Analyzing machine learning applications in finance, focusing on pricing and hedging of financial options
result Identifies various sources of risk and potential mitigation strategies
Proposes hedging strategies for mortgage prepayment risk.
problem Mortgage prepayment risk and its impact on financial institutions.
method Develops a model incorporating non-linear financial instruments for hedging.
result Shows non-linear risk in prepayment options and validates a functional form of prepayments.
HedgeAgents boosts financial trading with balanced strategies.
problem Inefficient trading strategies under rapid market changes.
method Integrates LLMs with multi-agent system for robust decision-making.
result 70% annualized return and 400% total return over 3 years.
Study proposes a new approach for deep hedging using artificial market simulations.
problem Challenges in selecting the best model for underlying asset simulations in deep hedging.
method Proposes artificial market simulations to replicate financial market stylized facts.
result Achieves similar performance to traditional approaches without mathematical finance models.
The paper values and hedges EPS products with jumps and default risks.
problem Valuation and risk management of EPS products under financial crises and default risks.
method Developed pricing frameworks using jump-diffusion and default models, derived closed-form formulas, and analysed hedging strategies.
result Quantified residual losses from counterparty default risk and defined default-adjusted premiums.
Option pricing is an integral part of modern financial risk management. The well-known Black and Scholes (1973) formula is commonly used for this purpose. This paper is an attempt to extend their work to a situation in which the unconditional volatility of the original asset is increasing during a certain period of tim…
Reinforcement learning improves option pricing and hedging accuracy.
problem Improving financial instrument pricing and hedging accuracy.
method Q-Learning Black Scholes approach applied to option pricing and hedging.
result The reinforcement learning model accurately estimates option prices and hedging strategies under various volatility and moneyness levels.
The paper models insurance market dynamics under uncertainty and financial frictions.
problem Modeling insurer behavior under uncertainty and financial frictions.
method Dynamic equilibrium model of insurance market with competitive insurers maximizing shareholder value.
result Investment can lead to lower insurance prices and negative loadings under certain conditions.
Paper uses AI for more efficient hedging of financial options.
problem Inefficient hedging in financial models.
method RL agents and GANs for delta hedging.
result RL-based hedging outperforms classic models in Q-world.
Paper examines financial engineering problems and introduces AlphaZero for better replication strategies.
problem Replication portfolio construction in incomplete markets with non-convex constraints.
method Introduces AlphaZero-based system to compare with deep hedging method.
result AlphaZero outperforms deep hedging in non-convex environments, finding near-optimal strategies.
In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hed…
Paper develops a continuous-time framework for financial markets without stochastic calculus.
problem Developing continuous-time financial models without stochastic calculus.
method A general framework using conditional topologies and pseudo-distance topologies.
result No-arbitrage conditions hold in continuous time if and only if they hold in discrete time.
Mathematical framework for differential machine learning in finance.
problem Theoretical assumptions in financial models and their impact on machine learning algorithms.
method Rigorous mathematical framework for differential machine learning in finance.
result Theoretical grounding enhances the predictive capabilities of neural networks in financial applications.
This paper compares eight DRL algorithms for dynamic hedging.
problem Optimal dynamic hedging strategies using Deep Reinforcement Learning.
method Eight DRL algorithms (MCPG, PPO, DQL, DDPG) compared using a GJR-GARCH(1,1) simulated dataset.
result MCPG and PPO outperform the Black-Scholes delta hedge baseline.
In a financial market model, we consider the variance-optimal semi-static hedging of a given contingent claim, a generalization of the classic variance-optimal hedging. To obtain a tractable formula for the expected squared hedging error and the optimal hedging strategy, we use a Fourier approach in a general multidime…
We create a robust hedging method for American options.
problem Hedging American options in uncertain financial markets.
method Aggregated Snell envelopes in a semi-martingale setting.
result Existence of a minimal hedging strategy in general settings.
New method uses nested optimal transport for financial time series evaluation.
problem Lack of consensus metric for evaluating generative models in finance.
method Nested optimal transport distance for time-causal tasks, with a parallelizable algorithm.
result Substantial speedups and robustness to financial tasks.