We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using crude oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value…
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48 results for “hedging effectiveness”
Behavioural effects on XVAq-fin.PR
Bank behaviour is important for pricing XVA because it links different counterparties and thus breaks the usual XVA pricing assumption of counterparty independence. Consider a typical case of a bank hedging a client trade via a CCP. On client default the hedge (effects) will be removed (rebalanced). On the other hand, …
This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dy…
Hedging in Field Theory Models of the Term Structurecond-mat.stat-mech
We use path integrals to calculate hedge parameters and efficacy of hedging in a quantum field theory generalization of the Heath, Jarrow and Morton (HJM) term structure model which parsimoniously describes the evolution of imperfectly correlated forward rates. We also calculate, within the model specification, the eff…
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.
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…
Deep Hedging learns optimal strategies for various risk levels.
problem Finding optimal hedging policies for diverse risk aversions.
method Continuous Reinforcement Learning with actor-critic algorithm.
result Demonstrated effectiveness in a stochastic volatility model.
Hedging with Bitcoin Futures: The Effect of Liquidation Loss Aversion and Aggressive Tradingq-fin.RM
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.
Proposes a new agent-based model for deep hedging that outperforms existing models.
problem Improving effectiveness of deep hedging strategies.
method Agent-based model with momentum, fundamental, and volatility traders following Heston volatility signal.
result Deep hedging agent trained with Chiarella-Heston model data outperforms baseline models in various transaction cost levels.
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.
This paper examines the volatility and covariance dynamics of cash and futures contracts that underlie the Optimal Hedge Ratio (OHR) across different hedging time horizons. We examine whether hedge ratios calculated over a short term hedging horizon can be scaled and successfully applied to longer term horizons. We als…
Neural-SDE models improve option hedging with lower errors and robustness.
problem Improving option hedging strategies using machine learning.
method Derive sensitivity-based and minimum-variance-based hedging strategies using neural-SDE market models.
result Neural-SDE models achieve lower hedging errors and are more robust than traditional models.
Learning to Hedge Swaptionsq-fin.RM
Paper uses RL for dynamic swaption hedging, outperforming traditional methods.
problem Dynamic hedging of swaptions using reinforcement learning.
method Design agents with three objective functions to adapt hedging strategies dynamically.
result Deep hedging strategies using two swaps outperform traditional methods, even with model misspecification.
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 , , or algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on predi…
Managing Systematic Mortality Risk in Life Annuities: An Application of Longevity Derivativesq-fin.CP
This paper assesses the hedge effectiveness of an index-based longevity swap and a longevity cap. Although swaps are a natural instrument for hedging longevity risk, derivatives with non-linear pay-offs, such as longevity caps, also provide downside protection. A tractable stochastic mortality model with age dependent …
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.
Paper develops a two-population model to assess longevity basis risk.
problem Mismatch between hedger's liability and hedging instrument causes longevity basis risk.
method Develops a two-population mortality model using Lee-Carter model and renewal process.
result Proposed model provides significant risk reduction when mortality jumps and sampling risk are considered.
The Feedback Effect of Hedging in Portfolio Optimizationcond-mat.other
In this short note, we will show how to optimize the portfolio of a large trader whose hedging strategy affects the price of his assets.
Adversarial deep hedging learns to hedge without specifying asset price models.
problem Lack of effective underlying asset models for deep hedging.
method Adversarial learning framework where a hedger and a generator compete to improve hedging performance.
result Adversarial deep hedging achieves competitive performance without explicit asset process modeling.
Shorting IG ETFs can hedge bond portfolios during market drawdowns effectively.
problem Managing downside risk in bond portfolios during market crises.
method Constructing three signals (Momentum, Liquidity, Credit) to dynamically hedge short IG positions.
result Dynamic hedge removes when predicted hedged return mean reverts, achieving higher returns and Sortino ratios.
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.
Deep Hedging Bermudan Swaptionsq-fin.CP
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.
In the paper a problem of risk measures on a discrete-time market model with transaction costs is studied. Strategy effectiveness and shortfall risk is introduced. This paper is a generalization of quantile hedging presented in [4].
New dual approach for hedging Bermudan options efficiently.
problem Computing efficient hedging portfolios for Bermudan options.
method Pure dual approach, rewriting dual pricing formula as excess reward representation, strict convexification, Monte Carlo method.
result Convergence and effectiveness of the new algorithm tested on various Bermudan options.
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.
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…
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.
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.
A quantum field theory generalization, Baaquie, of the Heath, Jarrow, and Morton (HJM) term structure model parsimoniously describes the evolution of imperfectly correlated forward rates. Field theory also offers powerful computational tools to compute path integrals which naturally arise from all forward rate models. …
We consider option hedging in a model where the underlying follows an exponential Lévy process. We derive approximations to the variance-optimal and to some suboptimal strategies as well as to their mean squared hedging errors. The results are obtained by considering the Lévy model as a perturbation of the Black-Schole…
Deep hedging strategies for Green PPAs in electricity markets reduce risk.
problem Risk management in Green Power Purchase Agreements (PPAs) due to price and weather risks.
method Utilizes machine learning to construct hedging strategies.
result Deep hedging strategies outperform static and dynamic benchmarks.
The study designs a green investment fund and a hedging strategy for insurance policies linked to it.
problem Hedging unit-linked life insurance policies with an environmentally sensitive investment fund.
method Developed a carbon-intensity-driven portfolio selection rule and a quadratic hedging approach.
result The hedging strategy minimizes the variance of hedging costs, as demonstrated through numerical analysis.
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.
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.
Hedging Cryptocurrency Optionsq-fin.PR
Study the hedging of cryptocurrency options in a volatile market.
problem Hedging options in a volatile, non-stationary cryptocurrency market.
method Calibrated to SVI-implied volatility surfaces, Monte Carlo price paths generated using SVCJ, GARCH, and historical data. Delta, Delta-Gamma, Delta-Vega, and Minimum Variance strategies applied. Wide range of market models tested.
result Calibration results indicate stochastic volatility, low jump frequency, and infinite activity. Short-dated options less sensitive to volatility or Gamma hedges; longer-dated options benefit from multiple-instrument hedges.
This paper examines pricing and hedging strategies for cross-currency equity protection swaps.
problem Dynamic requirements from EPS buyers in cross-currency equity protection swaps.
method Detailed analysis of two hedging paradigms, including separate and aggregated returns, with consideration of different types of returns.
result Proposes various hedging strategies with practical implications for EPS providers and investors.
Study uses machine learning and PolyModel to improve hedge fund performance.
problem Improving hedge fund investment performance with machine learning.
method Integration of machine learning techniques, PolyModel feature selection, and analysis of fund size.
result Machine learning enhances cumulative returns but increases annual volatility.
The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.
problem Endogenous volatility amplification in option markets during gamma-squeeze events.
method Developed a theoretical framework linking hedging behavior and market turbulence, incorporating beta-normalized volatility.
result Low-beta stocks amplify volatility more during gamma-squeeze events.
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.
Novel signature approach for pricing and hedging path-dependent options with market frictions.
problem Pricing and hedging path-dependent options with market frictions.
method Signature approach, mean-quadratic variation criterion, non-standard infinite-dimensional Riccati equations, time-augmented signature, non-Markovian stochastic control problem.
result Effective hedging strategies in frictional markets with low-truncated signature approximations.
Reinsurance counterparty credit risk (RCCR) is the risk of a loss arising from the fact that a reinsurance company is unable to fulfill her contractual obligations towards the ceding insurer. RCCR is an important risk category for insurance companies which, so far, has been addressed mostly via qualitative approaches. …
Study uses topic modeling and sentiment analysis to uncover hedge fund performance insights.
problem Hedge fund opacity and limited disclosure make them hard to analyze.
method Applied topic modeling and sentiment analysis to hedge fund documents using DistilBERT and Top2Vec.
result Automated topic modeling and sentiment analysis can predict hedge fund performance.
This paper aims to investigate the role of gold as a hedge and/or safe haven against oil price and currency market movements for medium (calm period) and large (extreme movement) fluctuations. In revisiting the role of gold, our study proposes new insights into the literature. First, our empirical design relaxes the as…
Cointegration helps insurers understand long-range mortality patterns.
problem Insurers struggle to detect long-range dependence in their mortality data.
method Cointegration techniques applied to mixed fractional Brownian motion (mfBm) to capture long-range dependence.
result Cointegration brings long-range dependence information from national mortality data to insurers' models.
Deep Hedgingq-fin.CP
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…
Paper studies pricing and hedging of nonreplicable insurance contracts using benchmark-neutral approach.
problem Pricing and hedging of long-term insurance contracts like variable annuities.
method Benchmark-neutral pricing framework using stock growth optimal portfolio as numéraire.
result Prices can be significantly lower than risk-neutral ones, offering attractive long-term risk-management.
GMWB Riders in a Binomial Framework - Pricing, Hedging, and Diversification of Mortality Riskq-fin.PR
We construct a binomial model for a guaranteed minimum withdrawal benefit (GMWB) rider to a variable annuity (VA) under optimal policyholder behaviour. The binomial model results in explicitly formulated perfect hedging strategies funded using only periodic fee income. We consider the separate perspectives of the insur…
This paper formulates a model of utility for a continuous time framework that captures the decision-maker's concern with ambiguity about both volatility and drift. Corresponding extensions of some basic results in asset pricing theory are presented. First, we derive arbitrage-free pricing rules based on hedging argumen…