The study reveals unspanned risks in equity option risk premiums, explaining negative premiums for certain options.
problem Explaining negative risk premiums for certain equity option types.
method Developed a decomposition of equity option risk premiums, operationalized the pricing kernel process, and incorporated unspanned risks.
result Empirical evidence supports the presence of unspanned risks, explaining negative risk premiums for certain options.
New framework identifies hidden risks and optionality in American options.
problem Underestimation of flexibility and convexity in early-exercise features.
method Introducing stochasticity into underlying determinants to quantify hidden risks and optionality.
result Remedies conventional pricing systems that underestimate optionality.
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.
The vast majority of works on option pricing operate on the assumption of risk neutral valuation, and consequently focus on the expected value of option returns, and do not consider risk parameters, such as variance. We show that it is possible to give explicit formulae for the variance of European option returns (vani…
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.
Enhances financial risk quantification in classical models.
problem Risk quantification in classical finance models.
method Nested risk measures, limiting behavior analysis.
result Uniqueness of risk-averse limit in classical models.
New volatility model for option pricing with time-varying risk premium.
problem Volatility risk premium is time-varying and not well captured by existing models.
method Combines Markov switching with Realized GARCH framework to derive a state-dependent pricing kernel.
result The model reduces option pricing errors by 15% or more compared to competing models.
New model explains option pricing with time-varying volatility risk aversion.
problem Time variations in the shape of the pricing kernel.
method Introduced a pricing kernel with time-varying volatility risk aversion combined with Heston-Nandi GARCH model.
result Variance risk ratio (VRR) emerges as a key variable in option pricing.
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.
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.
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.
Proposes a method to construct risk-neutral marginals from arbitrage-free option prices.
problem Lack of risk-neutral marginals that are free of arbitrage and easy to use.
method Explicit construction of risk-neutral marginals from discrete arbitrage-free option prices.
result Explicit construction guarantees risk-neutral marginals free of butterfly and calendar arbitrage.
The study finds flaws in methods used to estimate foreign exchange option prices.
problem Flaws in estimating foreign exchange option prices.
method Provided counterexamples of popular FX option interpolation methods.
result Popular FX option interpolation methods fail in certain scenarios.
This paper surveys options pricing under arithmetic Brownian motion and derives formulas for various types of options.
problem The use of arithmetic Brownian motion in finance is not widely adopted.
method Risk-neutral valuation and derivation of formulas for European options under three types of underlying assets.
result Derivation of formulas for European options and partial differential equations for American options.
New approach minimizes tail risk in option hedging.
problem Minimizing tail risk in option hedging strategies.
method Risk-sensitive reinforcement learning without parametric models.
result Significantly lower tail risk and higher mean P&L than delta hedging.
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.
We present a method of hedging Conditional Value at Risk of a position in stock using put options. The result leads to a linear programming problem that can be solved to optimise risk hedging.
Paper compares MCMC-based copula methods for exchange option pricing.
problem Pricing exchange options using copulas and MCMC.
method Risk-neutral pricing, copulas, and MCMC algorithm.
result Different copula models provide similar option prices except Gumbel.
The study finds no evidence of stochastic arbitrage opportunities in S&P 500 index options.
problem Identifying arbitrage opportunities in S&P 500 index options.
method Developed linear and mixed-integer linear programs to compute the maximum option premium.
result No evidence of systematic stochastic arbitrage opportunities in S&P 500 index options.
Study evaluates risk in options using volatility surface projections.
problem Risk assessment of options due to their non-linear price behavior and volatility fluctuations.
method Parametric surface projection method for implied volatility.
result Enhanced risk evaluation through dynamic volatility surface analysis.
Model predicts jump risk premia influencing cryptocurrency futures and option performance.
problem Capturing asymmetric and time-varying skewness in cryptocurrency returns.
method Bivariate Hawkes process with positive and negative jump premia.
result Inferred jump risk premia predict futures cost of carry and option performance.
This paper designs a new on-chain option that amortizes perpetual options for blockchain environments.
problem No equivalent standard for on-chain options exists, leading to high-frequency oracles and liquidation engines failures.
method Develops an amortizing perpetual option contract tailored to blockchain constraints, introducing a decentralized market framework.
result Demonstrates that the new contract functions as a risk primitive for DeFi, enabling applications like endogenous collateralization and de-peg insurance.
We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercis…
iCOS method estimates risk-neutral densities and option prices without model assumptions.
problem Estimating risk-neutral densities and option prices without model assumptions.
method Leverages Fourier-cosine technique using option-implied cosine series coefficients, without model assumptions.
result Effective in extracting information from option prices under various market conditions.
The option is a financial derivative, which is regularly employed in reducing the risk of its underlying securities. However, investing in option is still risky. Such risk becomes much severer for speculators who utilize option as a means of leverage to increase their potential returns. In order to mitigate risk on the…
This paper proposes a hybrid credit risk model, in closed form, to price vulnerable options with stochastic volatility. The distinctive features of the model are threefold. First, both the underlying and the option issuer's assets follow the Heston-Nandi GARCH model with their conditional variance being readily estimat…
New method uses DistRL to estimate entire payoff distribution for financial derivatives.
problem Traditional methods focus on expected option value; this tackles risk-aware pricing.
method Reinterprets and proposes a framework using Distributional Reinforcement Learning (DistRL).
result Demonstrates enhanced risk-aware pricing and uncertainty quantification on Asian options.
A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obt…
The study examines pricing American options with both exogenous and endogenous transaction costs.
problem Pricing American options with transaction costs and liquidity risks.
method Modeling liquidity risks as a mean-reverting process and transaction costs as proportional to trading amount. Two nonlinear PDEs are used to characterize option values. Numerical solution via ADI method and model calibration using maximum likelihood estimation.
result The model incorporating liquidity risks significantly outperforms the Leland model.
We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting N-component Gaussian mixture models to option quotes, where N is a small integer (here 4 or 5). These densities are…
The paper bounds payoffs and option prices in discrete models.
problem Measuring risk in discrete models and incomplete markets.
method Analytical and simulated bounds for payoff functions and option prices.
result Analytical and simulated bounds for European and American options.
The paper presents a practical method for evaluating investment projects using real options.
problem Evaluating investment projects under uncertainty and strategic risk management.
method Binomial trees and real options techniques for evaluating investment projects.
result The method can be used for most real options and introduces Project Value at Risk for feasibility.
The paper uses option theory to estimate corporate bond liquidity spreads.
problem Estimating liquidity spreads for corporate bonds.
method Option-theoretic approach considering risk-free rate volatility and credit risk.
result The model provides a robust tool for pricing illiquid bonds.
The risk-neutral option pricing method under GARCH intensity model is examined. The GARCH intensity model incorporates the characteristics of financial return series such as volatility clustering, leverage effect and conditional asymmetry. The GARCH intensity option pricing model has flexibility in changing the volatil…
The paper introduces and studies hedging for game (Israeli) style extension of swing options considered as multiple exercise derivatives. Assuming that the underlying security can be traded without restrictions we derive a formula for valuation of multiple exercise options via classical hedging arguments. Introducing t…
Method uses trinomial trees to price nontraditional options.
problem Pricing of random-expiry options with early expiry.
method Developed a trinomial tree approach to interpret early expiry.
result The method is free of arbitrage and can be implemented efficiently.
A new option pricing model handles non-constant risk aversion and transaction costs.
problem Deriving a pricing model for options with varying risk aversion.
method Developed a transformation method to solve the penalized nonlinear PDE and used finite difference discretization.
result Derived bounds on option prices and proposed a numerical scheme.
Deep RL solves dynamic risk pricing for complex financial models.
problem Dynamic risk measures in financial derivatives pricing.
method Deterministic actor-critic deep reinforcement learning (ACRL) for time-consistent expectile risk.
result High-quality hedging policies and prices for complex financial instruments.
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…
Model investor risk preferences to adjust real option valuation.
problem Investor risk preferences impact real option valuation.
method Model investor heterogeneity with different required returns, discounting cash flows with investor and market rates.
result Risk-adjusted valuation model facilitates subjective decision making.
This paper analyzes model risk in American put options using Heston volatility model.
problem Model risk in optimal exercise of American put options.
method Benchmark methodology of Hull and Suo [2002], Heston stochastic volatility model, numerical finite difference methods.
result Optimal exercise behavior is influenced by stochastic volatility dynamics and return-volatility correlation, creating model risk.
In this article, we look at the effect of volatility clustering on the risk indifference price of options described by Sircar and Sturm in their paper (Sircar, R., & Sturm, S. (2012). From smile asymptotics to market risk measures. Mathematical Finance. Advance online publication. doi:10.1111/mafi.12015). The indiffere…
Deep learning models predict option prices from 3D tensor data.
problem Predicting option prices for risk management and trading.
method 3D tensor representation of financial data, deep learning models (2D tensors in 3 channels).
result Proposed models outperform traditional methods like B-S model and vector-based LSTM.
The paper solves classical problems in option pricing.
problem Determining the law of the underlying and pricing options with convex payoffs.
method Formulates problems using inverse problem theory and provides proofs without special assumptions.
result Extends existing results in option pricing theory.
Develops a binary tree model for option pricing with skew dynamics.
problem Option pricing in incomplete markets with skew dynamics.
method Binary tree model with skew Brownian motion dynamics.
result Model preserves skewness under both discrete and continuous time limits.
Paper solves bond option pricing with credit risk using Black-Scholes equations.
problem Pricing options on bonds with credit risk.
method Solution representations of Black-Scholes equations for specific problems.
result Pricing formulae for puttable and callable bonds with credit risk.
Extends option pricing framework without risk-free asset using Levy jumps.
problem Valuing derivatives in markets without a traded risk-free bond.
method Introduces common Levy jump dynamics, uses Ito-Levy calculus, FFT, and COS algorithms.
result Calibrations show jump models reduce pricing errors and fit volatility smiles better than Black-Scholes.
This paper studies the risk-adjusted optimal timing to liquidate an option at the prevailing market price. In addition to maximizing the expected discounted return from option sale, we incorporate a path-dependent risk penalty based on shortfall or quadratic variation of the option price up to the liquidation time. We …