Optimized options portfolio with a specific payoff function.
problem Optimizing an options portfolio with a fixed payoff function.
method Formulated as an integer linear programming problem, including an objective payoff function and constraints.
result Optimum solution for European call and put options on Taiwan Futures Exchange.
New method optimizes portfolios with options, addressing asymmetry, dimensionality, and dependence.
problem Optimizing portfolios with options, especially when distributions are asymmetric, dimensions are high, and payoffs are dependent.
method Developed a new dependency matrix based on conditional probabilities of options' payoffs, computed using copula structures.
result Empirical evidence shows the approach is efficient, fast, and scalable to large portfolios of options.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
DeltaHedge uses AI to optimize portfolio options trading.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
RL accelerates portfolio optimization and option pricing by dynamically adjusting preconditioner sizes.
problem Large linear systems in portfolio optimization and option pricing lead to slow convergence.
method Reinforcement Learning (RL) dynamically adjusts block-preconditioner sizes to accelerate convergence.
result RL-driven solver significantly reduces computational cost and accelerates convergence.
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.
Combines option pricing and portfolio theory for optimal hedging.
problem Optimal hedging of European options in various price dynamics.
method Derives optimal holdings and unhedged risk for different price dynamics.
result Derives solutions for various price dynamics including binomial, diffusion, volatility, volatility-of-volatility, and jump diffusion.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
problem Optimal option portfolios under Sharpe Ratio maximization
method Formulation for explicit portfolio weights
result Different optimal portfolios for Sharpe Ratio and return-to-Value-at-Risk (VaR) ratio
Optimizes option portfolios for skewed-t returns using VaR and variance measures.
problem Optimizing portfolios for skewed-t returns with heavy tails and skewness.
method Uses variance and VaR measures, departing from normal returns, and provides explicit portfolio weights.
result Optimal portfolio weights differ significantly from variance optimal weights due to skewness.
Paper uses neural networks to compress large portfolios of options, reducing risk and capital requirements.
problem Managing risk and capital requirements for large portfolios of financial options.
method Artificial neural network framework for portfolio compression, static hedging, and risk management.
result The compressed portfolio's risk profiles align closely with the target portfolio's, reducing capital requirements.
Proposes a deep learning approach for optimizing portfolios with stocks and options.
problem Optimizing portfolios with time-inconsistent objectives and trading constraints.
method Neural networks with adaptive activation functions for asset allocation and option strike prices.
result Adding options leads to more stable and consistent stock allocations.
The paper extends portfolio theory to include contingent claim functions for option pricing.
problem Developing a method to price options using portfolio generating functions.
method Extending portfolio theory to include contingent claim functions and applying partial differential equations.
result A method to price options using portfolio generating functions and replicable contingent claim functions.
Model-free approach to hedge path-dependent options using min-max optimization.
problem Hedging path-dependent options with maturity T using a static portfolio of vanilla options.
method Model-free approach based on primal-dual Martingale Optimal Transport (MOT) problem, solving a min-max optimization problem.
result Provides theoretical bounds on hedging error at maturity T.
Kelly investing improved with options to reduce estimation risk.
problem Estimation risk in Kelly investing leads to suboptimal portfolios.
method Introduced European options into the Kelly framework in a binomial model.
result Constructed growth optimal portfolios robust to estimation risk.
We consider a portfolio with call option and the corresponding underlying asset under the standard assumption that stock-market price represents a random variable with lognormal distribution. Minimizing the variance (hedging risk) of the portfolio on the date of maturity of the call option we find a fraction of the ass…
Deep BSDE method for pricing and hedging complex financial portfolios.
problem Simultaneous pricing and delta-gamma hedging of large portfolios of multi-asset Bermudan options.
method Discretely reflected BSDEs, One Step Malliavin scheme, neural network regression Monte Carlo method.
result Efficient and accurate pricing and hedging strategies for high-dimensional portfolios.
We consider the fundamental theorem of asset pricing (FTAP) and hedging prices of options under non-dominated model uncertainty and portfolio constrains in discrete time. We first show that no arbitrage holds if and only if there exists some family of probability measures such that any admissible portfolio value proces…
Combines CPPI and option-based strategy to ensure equity exposure.
problem Cash-in risk and equity market participation in CPPI.
method Two-step strategy: CPPI with guaranteed minimum equity exposure.
result Shows effectiveness through numerical analysis of option prices.
Unified econometric model for portfolio optimization and option valuation.
problem Time-varying volatility and heavy tails in asset returns.
method Multivariate affine GARCH(1,1) with Normal Inverse Gaussian innovations.
result Substantial wealth-equivalent utility losses from ignoring correlation and tail risk.
Neural-SDE model accurately simulates option risks.
problem Estimating accurate risk scenarios for option portfolios.
method Arbitrage-free neural-SDE market model for joint option dynamics.
result Models produce more efficient and accurate VaR evaluations.
Expands QLBS model with NuQLear topics, improving option pricing and hedging.
problem Improving option pricing and hedging using Q-Learning and RL.
method Fitted Q Iteration, Inverse Reinforcement Learning, and portfolio analysis.
result NuQLear outperforms traditional methods in option pricing and hedging.
Enhanced indexation uses equity and index options for better performance.
problem Improving portfolio performance through enhanced indexation.
method Integrating index options into an enhanced indexation strategy based on second-order stochastic dominance.
result Introducing option strategies in enhanced indexation leads to improved out-of-sample performance.
Study optimal semistatic portfolios using martingale Schrödinger bridges.
problem Optimizing semistatic portfolios in a dynamic stock market.
method Minimizing entropy among calibrated martingale measures.
result Explicit solution for optimal semistatic portfolios exists.
Optimal portfolio yields a digital option payoff.
problem Portfolio optimization under generalized dual theory of choice.
method Characterized optimal solution and derived it in closed form.
result Payoff is a digital option that yields in-the-money payoff in good market scenarios.
We justify and give error estimates for binomial approximations of game (Israeli) options in the Black--Scholes market with Lipschitz continuous path dependent payoffs which are new also for usual American style options. We show also that rational (optimal) exercise times and hedging self-financing portfolios of binomi…
Paper models foreign exchange markets and develops an on-line portfolio selection algorithm.
problem Modeling and predicting returns in foreign exchange markets.
method Matrix-valued time series model, trading matrices, and cross rate method.
result Proves the profitability and universality of the on-line portfolio selection algorithm.
The paper redefines semi-static hedging as derivatives and calculates hedging errors.
problem The costs of maintaining hedging portfolios and the limitations of semi-static hedging.
method New integral representations, approximations, and efficient numerical methods for calculating Wiener-Hopf factors and Laplace-Fourier inversion.
result The hedging error of static hedging portfolios can be larger than variance-minimizing portfolios.
This paper optimizes crypto portfolios and valuates crypto options.
problem High volatility and lack of standard pricing models for crypto assets.
method Optimization techniques to minimize tail risk, dynamic pricing model for crypto assets, Esscher transform for fair valuation.
result Optimized crypto portfolios outperform major stock indices.
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.
Our goal here is to discuss the pricing problem of European and American options in discrete time using elementary calculus so as to be an easy reference for first year undergraduate students. Using the binomial model we compute the fair price of European and American options. We explain the notion of Arbitrage and the…
Entropy based ideas find wide-ranging applications in finance for calibrating models of portfolio risk as well as options pricing. The abstracted problem, extensively studied in the literature, corresponds to finding a probability measure that minimizes relative entropy with respect to a specified measure while satisfy…
Market makers use a simplified approach for options trading.
problem Optimal control of a high-dimensional portfolio of options.
method Approximating portfolio vega, using a low-dimensional functional equation, and numerical methods.
result The problem of an option market maker is reduced to a tractable, low-dimensional problem.
Project estimates risk-neutral dependence from option prices.
problem Extracting risk-neutral dependence from option prices.
method Projection estimator using portfolios of observed options.
result Estimates risk-neutral dependence in incomplete markets.
Unified framework for ESG-inclusive portfolio optimization and pricing.
problem Incorporating ESG ratings into dynamic asset pricing theory.
method Introducing ESG-valued return as a linear transformation of financial and ESG scores, preserving traditional risk aversion with an ESG affinity parameter.
result Developed a more complex portfolio optimization problem in a space governed by reward, risk, and ESG score.
RL methods applied to option pricing using modified QLBS and RLOP models.
problem Applying reinforcement learning to price options accurately.
method Developed modified QLBS and RLOP models, implemented RL learning algorithm with neural networks.
result Optimal hedging strategies learned by RL outperform baseline models.
Study examines hedging options on asset portfolios against one underlying asset with transaction costs.
problem Hedging options on asset portfolios when one underlying asset is expensive to trade.
method Simulated data analysis with varying trading intervals, correlation coefficients, and transaction costs.
result Trading the wrong asset can be beneficial when correlation is high and transaction costs are low.
Quantum algorithms speed up financial portfolio valuation.
problem Efficiently pricing and valuing complex financial portfolios.
method Quantum Monte Carlo (QMC) algorithms enhanced with quantum amplitude estimation.
result Quantum algorithms significantly accelerate CVA and portfolio pricing.
The paper addresses hedging Asian options with transaction costs using asymptotic hedging.
problem Hedging Asian options in markets with transaction costs.
method Asymptotic hedging approach.
result Probability convergence of investment portfolio value to payment function as revision count approaches infinity.
When the planning horizon is long, and the safe asset grows indefinitely, isoelastic portfolios are nearly optimal for investors who are close to isoelastic for high wealth, and not too risk averse for low wealth. We prove this result in a general arbitrage-free, frictionless, semimartingale model. As a consequence, op…
Perfect hedging of options with a dynamic portfolio in rough volatility models.
problem Hedging options in rough volatility models.
method Presented a simple but general result showing perfect hedging with a dynamic portfolio of underlying and variance swap.
result Rough volatility models significantly reduce hedging error compared to diffusion-based models.
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.
The paper shows that benchmark-neutral pricing minimizes option prices.
problem Pricing extreme-maturity European put options on diversified indices.
method Benchmark-neutral pricing applied to a drifted time-transformed squared Bessel process.
result Benchmark-neutral price is the minimal possible price, risk-neutral price is more expensive.
We develop a trinomial tree model for pricing perpetual derivatives and European options.
problem Pricing perpetual derivatives and European options in a market with two risky assets and a perpetual derivative of one of them.
method We introduce a recombining trinomial tree model, consider a market with two risky assets and a perpetual derivative, and use a replicating portfolio to price options and generate relationships between risk-neutral and real-world parameters.
result We develop implied parameter surfaces for real-world parameters in the model using historical data.
The paper develops a new model-free formula for option initial margins.
problem Calculating initial margins for option portfolios is complex and risky.
method The authors derive a new approximation formula for VaR without assuming a model.
result The new formula performs better than existing methods in simulations.
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…
We show how Adjoint Algorithmic Differentiation (AAD) allows an extremely efficient calculation of correlation Risk of option prices computed with Monte Carlo simulations. A key point in the construction is the use of binning to simultaneously achieve computational efficiency and accurate confidence intervals. We illus…
Develops a new framework for integrating satellite allocations in small portfolios.
problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.
The paper optimizes financial derivatives for market completion in SV models.
problem Optimizing financial derivatives for market completion in stochastic volatility models.
method Simulation-based method to approximate optimal portfolio strategy, using double optimization approach (utility maximization and risk exposure minimization).
result Strangle options are the best choices for market completion in equity options.