EX-DRL improves extreme quantile prediction for financial risk management.
problem Inaccurate estimation of extreme quantiles in loss distributions.
method EX-DRL uses Generalized Pareto Distribution (GPD) to model the tail of the loss distribution and Quantile Regression (QR) to improve extreme quantile prediction.
result EX-DRL provides more precise estimates of extreme quantiles, improving risk metrics reliability.
Study optimizes Bitcoin futures hedging to reduce liquidation risk.
problem Optimizing hedging strategies to minimize liquidation risk in Bitcoin futures.
method Derived a semi-closed form optimal hedging strategy considering spot and futures extreme returns, loss aversion, leverage, and collateral management.
result Optimal strategy reduces both hedged portfolio variance and liquidation probability.
Investigates how options can control systemic risk in portfolios.
problem Systemic risk in optioned portfolios.
method Correlation hedging, extreme loss hedging, and SOCP formulation.
result Options can make systemic risk controllable and enhance return-risk performance.
Improved estimation of hedge fund tail risks using a novel model.
problem Estimation inefficiencies and need for manual threshold selection in extreme value regression models.
method Extended tail regression model with automatic threshold selection and artificial censoring.
result Significant link between tail risks and factors like equity momentum and financial stability index.
The study analyzes pricing and hedging of STCDOs using an affine model with a catastrophic risk component.
problem Pricing and hedging of collateralized debt obligations (CDOs) with specific focus on mezzanine and equity tranches.
method Specified an affine two-factor model with a catastrophic risk component, estimated using QML and Kalman filter, derived variance-minimizing strategy, analyzed actual performance and simulated extreme loss scenarios.
result The variance-minimizing strategy is most effective for mezzanine tranches but fails for equity tranches.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
problem Behavioral anomalies in portfolio choice under stochastic volatility.
method Smooth, nonlinear, asymmetric extrapolation function; CRRA investor; Heston stochastic volatility; Hamilton-Jacobi-Bellman equation; Numerical solutions (finite-difference ADI, deep learning-driven iterative).
result Saturation acts as an endogenous correction mechanism, reducing welfare loss.
The paper examines bounds for stop-loss payoffs using transformed random variables.
problem Bounding stop-loss payoffs for a difference of two random variables.
method Analyzes crossing points of cdfs of original and transformed random variables.
result Unique pairwise crossing points for mortality-linked securities under symmetric copulas.
The paper shows real market exists free lunches with vanishing risks.
problem The hypothesis of no free lunches with vanishing risk in real markets.
method Accurately hedged extreme-maturity zero-coupon bond.
result FLVRs naturally exist in the real market.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
problem Optimal portfolio choice under stochastic volatility
method Smooth, nonlinear extrapolation function with sentiment and variance hedging
result Lower welfare loss with asymmetric nonlinear extrapolation
This paper solves hedging in incomplete markets using neural networks.
problem Hedging in incomplete markets with risk factor, illiquidity, and discrete transaction dates.
method Proposes a jump-diffusion model and uses RNN, LSTM, and Mogrifier-LSTM neural networks for hedging strategies.
result Mogrifier-LSTM is the fastest and most effective model for hedging.
Study on optimal fees in hedge funds with first-loss compensation.
problem Determining the best fee structure for hedge funds with first-loss compensation.
method Solved the manager's non-concave utility maximization problem, calculated Pareto optimal first-loss schemes, and maximized a decision criterion on this set.
result Traditional fees are not Pareto optimal, and the preferred first-loss coverage guarantee varies with investor and market factors.
Algorithm for hedging American options with transaction costs.
problem Hedging American options considering transaction costs.
method Backward Hedging algorithm minimizing loss function.
result Optimal hedging strategy determined by minimizing loss function.
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
problem Impermanent loss in automated market makers (AMMs).
method Analytical derivation of a static replication formula using European options, and numerical example with real data.
result Guaranteed hedging coverage for all final prices within a predefined interval.
In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distrib…
Paper proposes a deep hedging method for Bermudan swaptions to manage residual profit and loss.
problem Real-world market conditions differ from ideal assumptions in traditional hedging methods, leading to residual profit and loss.
method Deep hedging framework applied to Bermudan swaptions, allowing flexible risk measures and hedge strategies.
result Effective residual profit and loss management demonstrated through numerical analysis.
Develops a robust hedging valuation adjustment measure for dynamic hedging under liquidity-demand stress.
problem Dynamic hedging under liquidity-demand stress
method Define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of the loss distribution generated by simulated rebalancing and maturity-unwind trades.
result Distinguishes fixed-radius convention from fixed benchmark-stress convention and shows wider no-trade bands lower rebalancing costs but raise hedge-error risk.
The article provides formulas to hedge impermanent loss in decentralized markets.
problem Impermanent loss in concentrated liquidity provision in decentralized markets.
method Analytical characterizations and static replication formulas using European calls or puts.
result Static replication formulas accurately hedge impermanent loss.
Paper develops a robust HVA measure for dynamic hedging under liquidity stress.
problem Valuation of dynamic hedging under liquidity stress.
method Defines robust HVA as worst-case expected loss over a relative-entropy neighborhood of loss distributions for no-trade bands.
result Wider no-trade bands lower rebalancing costs but increase hedge-error risk.
Trains neural nets for gamma hedging with model uncertainty.
problem Gamma hedging with model mismatch.
method Trains neural networks using loss functions that reward model uncertainty.
result Networks can learn optimal gamma hedging even with model mismatch.
We consider the problem of option hedging in a market with proportional transaction costs. Since super-replication is very costly in such markets, we replace perfect hedging with an expected loss constraint. Asymptotic analysis for small transactions is used to obtain a tractable model. A general expansion theory is de…
Geometric Mean Market Makers super-hedge impermanent loss without models.
problem Super-hedging impermanent loss in Geometric Mean Market Makers.
method Model-free rebalancing strategy.
result Loss-versus-rebalancing vanishes due to finite variation exchange rate.
Risk-averse reinforcement learning optimizes option hedging.
problem Optimizing option hedging under risk aversion and realistic market conditions.
method Applied Trust Region Volatility Optimization (TRVO) to a vanilla option hedging environment.
result The derived hedging strategy outperforms Black & Scholes and is robust to market variations.
This paper analyzes extreme flooding risks and proposes insurance and bond solutions.
problem Severe rise in magnitude and frequency of floods causing catastrophic losses.
method Extremes analysis using Peaks-Over-Threshold method and Point Process model; Value-at-Risk (VaR) and Conditional VaR (CVaR) estimation; Flood zoning insurance and catastrophic bond design.
result Developed flood risk vulnerability and threat analysis considering geography and economic factors; Proposed flood zoning insurance and catastrophic bond design.
This study examines how earnings announcements affect option volatility and pricing.
problem The impact of earnings announcements on option volatility and pricing.
method Analysis of extremely short-term options data to study bimodality and concavity in IV curves.
result Investors pay a premium to hedge against extreme volatility during earnings announcements in the presence of concave IV smiles.
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 G based on the exponential reweighing of experts' losses. We build the artificial probabilistic framework and …
New metric to measure liquidity position PNL, delta hedging algorithm for automated market makers.
problem Vulnerability of liquidity positions to price changes in underlying assets.
method Proposes a new metric for measuring PNL, delta hedging algorithm for various AMMs.
result New metric more accurately measures net value change due to price movement.
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.
Study on hedging CVA in jump-diffusion setting using Monte Carlo simulations.
problem Hedging Credit Valuation Adjustment (CVA) in financial portfolios.
method Monte Carlo simulation in Black-Scholes and Merton jump-diffusion settings.
result Hedging CVA is crucial for stable trading strategies, especially in jump-diffusion settings.
The study improves gold's role as a hedge and safe haven using new correlation measures.
problem Investing in gold for medium and extreme market movements.
method Developed a new correlation measure based on fractal approach.
result Gold is a better hedge than safe haven for extreme market movements.
Based on a recent theorem due to the authors, it is shown how the extreme tail dependence between an asset and a factor or index or between two assets can be easily calibrated. Portfolios constructed with stocks with minimal tail dependence with the market exhibit a remarkable degree of decorrelation with the market at…
Unified methods for hedging impermanent loss in decentralized exchanges.
problem Hedging impermanent loss in liquidity provision at decentralized exchanges.
method Static and dynamic approaches using arbitrage-based methods for valuation and risk management.
result Unified valuation and hedging formulas for IL protection claims.
Deep learning enhances options hedging performance.
problem Improving delta hedging for options using neural networks.
method Learning residuals between hedging function and implied Black-Scholes delta using neural networks.
result Deep learning significantly improves hedging performance, often by more than 100%.
We investigate the problem of pricing and hedging derivatives of Electricity Futures contract when the underlying asset is not available. We propose to use a cross hedging strategy based on the Futures contract covering the larger delivery period. A quick overview of market data shows a basis risk for this market incom…
The paper uses EVT to improve tail risk measures under ambiguity sets.
problem Misspecification of tail risk measures leads to inflated risk estimates.
method Applies Extreme Value Theory to derive worst-case tail risk under ambiguity sets.
result Proposes a tail-calibrated ambiguity design that preserves nominal tail asymptotic scaling.
NDI aims to forecast future natural disasters risk for insurers.
problem Increasing intensity and frequency of natural disasters.
method Develops a Natural Disasters Index (NDI) based on NOAA data.
result NDI forecasts future natural disasters risk for insurers.
Paper presents a machine learning algorithm for hedging ETF options, outperforming static hedging methods.
problem Semi-static hedging of ETF options with transaction costs and varying market conditions.
method Data-driven machine learning algorithm considering transaction costs, automated portfolio management, and PnL attribution analysis.
result The static hedging approach outperforms dynamic hedging methods in terms of profit and loss.
Optimizes forecast distributions for financial risk management.
problem Improving risk management through better forecast distributions.
method Optimizes forecast distributions using scoring rules relevant to financial risk management.
result Tail-focused predictive distributions yield better outcomes in hedging strategies involving VIX futures.
The probability minimizing problem of large losses of portfolio in discrete and continuous time models is studied. This gives a generalization of quantile hedging presented in [3].
The paper studies how to protect hedge funds from losses using reinsurance.
problem Identifying and protecting downside risk in hedge fund investments.
method Two approaches: Analytic closed-form solution and numerical simulation.
result The additional reinsurance contract provides full portfolio protection beyond a certain threshold.
Hedge has been proposed as an adaptive scheme, which guides an agent's decision in resource selection and distribution problems that can be modeled as a multi-armed bandit full information game. Such problems are encountered in the areas of computer and communication networks, e.g. network path selection, load distribu…
Optimal portfolios for fat-tailed risks using a new tail risk measure.
problem Optimizing portfolios for pension funds and insurance liabilities with extreme risk sensitivity.
method Developed a new tail risk measure (Extreme Deviation, XD) and optimized portfolios based on this measure.
result Optimal portfolios maximize return per unit of XD, balancing hedging and risk contributions.
Adapting Hedge algorithm for semi-adversarial data with root-entropy regularization.
problem Minimizing regret in prediction with expert advice under varying distributions.
method Follow-the-Regularized-Leader (FTRL) with root-entropy regularization.
result Adaptive minimax optimal regret across all levels of constraint sets.
Paper uses deep learning to price and hedge options in incomplete markets.
problem Incomplete markets lack unique no-arbitrage solutions for pricing and hedging European options.
method Constrained deep learning approach with a single neural network representing option prices and hedging strategies.
result Constrained networks produce superior P&L distributions compared to unconstrained networks.
This paper develops a new framework to assess crypto portfolio risk using simulation methods.
problem Traditional financial risk models fail to capture crypto market characteristics like volatility and contagion.
method The framework integrates four components: volatility stress testing, hedging, contagion modeling, and Monte Carlo simulation.
result The framework robustly assesses crypto portfolio risk and is validated with real data.
In this paper, we provide some results on Skorokhod embedding with local time and its applications to the robust hedging problem in finance. First we investigate the robust hedging of options depending on the local time by using the recently introduced stochastic control approach, in order to identify the optimal hedgi…
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.
A new algorithm for high-dimensional hedging problems.
problem High-dimensional, path-dependent hedging problems.
method Signature-based algorithm using operator-valued kernels and geometric rough paths.
result Theoretical guarantees on existence and uniqueness of a global minimum.
The problem of stock hedging is reconsidered in this paper, where a put option is chosen from a set of available put options to hedge the market risk of a stock. A formula is proposed to determine the probability that the potential loss exceeds a predetermined level of Value-at-Risk, which is used to find the optimal s…