Approximates call option prices for Barndorff-Nielsen and Shephard model.
problem Calculating exact option prices for complex models is computationally expensive.
method Developed approximate expressions using decomposition formula.
result Approximations are effective as shown by numerical experiments.
Neural networks approximate superhedging prices in financial models.
problem Approximating superhedging prices in financial markets.
method Neural networks for approximating α-quantile hedging prices and their essential supremum. result Neural networks provide an approximation for superhedging prices and strategies.
Improved spread option pricing with a new approximation method.
problem Inaccuracies in the original Kirk's formula for high correlation cases.
method Developed a new approximation method for spread option pricing.
result The Modified Kirk's Approximation method is extremely accurate and improves upon Kirk's approach.
New model approximates slow volatility factor using parabolic arcs.
problem Modeling slow factor of volatility in stochastic volatility models.
method Perturbation technique to derive approximate European option prices.
result Simplified expression for European option prices around modified Black-Scholes price.
Approximates derivative pricing under fractional stochastic volatility.
problem Derivative pricing under fractional stochastic volatility model.
method Approximate expression derived from deterministic functions and fractional Ornstein-Uhlenbeck process.
result Numerical simulations show the feasibility and effect of long-range dependencies on derivative prices.
Using classical Taylor series techniques, we develop a unified approach to pricing and implied volatility for European-style options in a general local-stochastic volatility setting. Our price approximations require only a normal CDF and our implied volatility approximations are fully explicit (ie, they require no spec…
The paper presents an approximate formula for European mortgage options pricing.
problem Pricing European mortgage options with accuracy and efficiency.
method Approximation of the underlying price distribution using lognormal distributions and matching moments.
result The proposed formula provides a good approximation with high accuracy compared to Monte Carlo simulations.
Paper prices geometric Asian options using a multifactor stochastic volatility model.
problem Pricing continuous geometric Asian options under multifactor stochastic volatility.
method Asymptotic expansion and perturbation techniques for both floating and fixed strike GAOs.
result Simplified pricing formulae for GAOs derived in a multifactor stochastic volatility framework.
Improved pricing method for American options in various models.
problem Efficient pricing of American options in jump-diffusion models and barrier options.
method Hybrid method combining perturbative arguments and quadratic approximation.
result Higher order approximations provide significantly more pricing accuracy.
Kristensen and Mele developed a method for closed-form derivatives pricing approximations.
problem Derivatives pricing models are often intractable, making approximation methods necessary.
method Power series expansion of pricing bias between models, leading to closed-form approximations.
result The method provides stable numerical approximations for various derivatives models.
We consider a general local-stochastic volatility model and an investor with exponential utility. For a European-style contingent claim, whose payoff may depend on either a traded or non-traded asset, we derive an explicit approximation for both the buyer's and seller's indifference price. For European calls on a trade…
The paper approximates CARMA models for option pricing.
problem Approximating the transition density of CARMA(p, q) models.
method Using Gauss-Laguerre quadrature and time changed Brownian Motion.
result Provides an analytical formula for option prices.
Study provides error estimates for approximating game options with diffusion asset prices.
problem Approximating fair prices of game options with diffusion asset prices.
method Error estimates for discrete approximations of diffusion processes, applied to game options.
result Effective tool for computing fair prices of game options in multi-asset markets.
The Volterra Heston model is used to price American options.
problem Pricing American options in the Volterra Heston model.
method Kernel-based approximations and simulation techniques.
result Convergence of American option prices in approximating models to the Volterra Heston model.
Approximates option prices in Barndorff-Nielsen and Shephard models using Taylor expansion.
problem Approximating option prices in complex stochastic volatility models.
method Taylor expansion and recursive algorithm for closed-form approximations.
result Explicit results for inverse Gaussian and gamma stationary distributions, with favorable comparisons to characteristic function.
Improved approximations for call option prices in stochastic volatility models.
problem Improving accuracy of call option pricing in models with stochastic volatility.
method Transformed decomposition formula into Taylor series with stochastic terms, derived new approximations.
result New approximations with sharper error estimates, especially effective in high volatility scenarios.
Study exchange option pricing with stochastic volatility and correlation.
problem Pricing exchange options under stochastic volatility and correlation.
method Approximation using a closed-form solution with Taylor expansion.
result Numerical results show the effectiveness of the proposed method.
The study approximates option prices using Hermite polynomials without assuming a specific distribution.
problem Approximating option prices without assuming a specific distribution of returns.
method Approximating the logarithmic return's density by a linear combination of rescaled Hermite polynomials.
result Empirical results suggest reasonable performance for options with moderate strike prices.
The Wiener-Hopf factorization is obtained in closed form for a phase type approximation to the CGMY Lévy process. This allows, for the approximation, exact computation of first passage times to barrier levels via Laplace transform inversion. Calibration of the CGMY model to market option prices defines the risk neutral…
Fast probabilistic option price predictions using modular Bayesian inference.
problem Accurate probabilistic predictions of future option prices.
method Modular approximate Bayesian inference framework that combines multiple data sources.
result Accurate probabilistic option-price predictions in realistic scenarios.
Study Asian option pricing under uncertain volatility, approximating prices with small volatility intervals.
problem Asian option pricing in uncertain volatility conditions.
method Procedure to approximate Asian option prices with small volatility intervals, solving fully nonlinear PDE.
result Approximation method for solving fully nonlinear PDE.
The paper provides approximations for pricing Asian options using a mixed fractional Brownian motion with jumps.
problem Pricing Asian options under a mixed fractional Brownian motion with jumps.
method Approximate closed-form solutions for arithmetic Asian options and power options.
result Analytical formulas for pricing arithmetic Asian options and power options are derived.
Paper derives option pricing formula for SVJ models with jumps.
problem Option pricing for models with finite activity jumps.
method Generic decomposition and explicit approximation formulas for SVJ models.
result Improved computation efficiency and insight into volatility smiles.
Deep neural networks can accurately approximate option prices in stochastic volatility models.
problem Approximating option prices in complex stochastic volatility models.
method Use deep neural networks to approximate option prices for a general class of stochastic volatility models.
result Deep neural networks can approximate option prices up to small error ε with sub-polynomial network size growth.
This paper compares linear regression and neural networks for pricing swing options.
problem Pricing swing options using approximation methods.
method Linear regression and neural networks for approximating the continuation value and swing price.
result The approximation methods converge to the actual swing price as the number of functions or Monte Carlo samples increases.
This paper develops methods for pricing American Parisian options under general Markov models.
problem Pricing American Parisian options with various types and payoff functions.
method General approaches using CTMC approximation for time-inhomogeneous Markov models, including state augmentation and variational inequalities.
result Efficient algorithms for pricing American Parisian options confirmed with numerical experiments.
Model shows firms with computational limits can lead to inefficient economies.
problem Firms with limited computational power can lead to inefficient market outcomes.
method Developed an equilibrium model where firms use polynomial approximations to price future output.
result The model predicts multiple equilibria with inefficiently low output.
Improved pricing method for illiquid assets using Lambert function.
problem Inaccurate pricing of illiquid assets using traditional methods.
method Deterministic decomposition of reservation price using Lambert function; improved Monte Carlo method (LMC).
result Improved accuracy in pricing illiquid assets through LMC method.
Paper derives second order approximations for limit order books.
problem Modeling fluctuations in price and volume processes.
method Second order approximation of infinite dimensional limit order book dynamics.
result Confidence intervals for optimal portfolio liquidation models.
Approximates discounted moments for financial products using polynomial expansions.
problem Approximating discounted moments of stochastic processes for financial applications.
method High-order power series expansion of the infinitesimal generator.
result Error decreases to around 10 to 100 times machine precision for higher orders.
Matrix approximation method for Bachelier option pricing and Greeks under stochastic volatility models
problem Computing option prices and Greeks for stochastic volatility models
method Matrix approximation using elementary linear algebra
result Option prices and Greeks computed for infinitely many strikes with a finite number of expectations
New model improves option pricing with faster convergence and better generalization.
problem Improving classical option pricing models.
method Introducing a time value related decision function and proving a universal approximation theorem.
result The new decision function approximates on the entire domain of definition by neural networks.
Unified framework for pricing various debt securities.
problem Pricing of different types of debt securities under general short-rate processes.
method Unifying framework using continuous-time Markov chain approximations and bi-dimensional diffusion processes.
result Closed-form matrix expressions and efficient algorithms for pricing various debt securities.
In this article we propose a novel approach to reduce the computational complexity of various approximation methods for pricing discrete time American options. Given a sequence of continuation values estimates corresponding to different levels of spatial approximation and time discretization, we propose a multi-level l…
The Hull-White one factor model is used to price interest rate options. The parameters of the model are often calibrated to simple liquid instruments, in particular European swaptions. It is therefore very important to have very efficient pricing formula for simple instruments. Such a formula is proposed here for Europ…
A computational technique borrowed from the physical sciences is introduced to obtain accurate closed-form approximations for the transition probability of arbitrary diffusion processes. Within the path integral framework the same technique allows one to obtain remarkably good approximations of the pricing kernels of f…
We consider a defaultable asset whose risk-neutral pricing dynamics are described by an exponential Levy-type martingale subject to default. This class of models allows for local volatility, local default intensity, and a locally dependent Levy measure. Generalizing and extending the novel adjoint expansion technique o…
In this paper we use Bernstein and Chebyshev polynomials to approximate the price of some basket options under a bivariate Black-Scholes model. The method consists in expanding the price of a univariate related contract after conditioning on the remaining underlying assets and calculating the mixed exponential-power mo…
Study examines pricing of target volatility options in fractional SABR model.
problem Pricing target volatility options in the lognormal fractional SABR model.
method Used Ito's calculus for a theoretical replicating strategy and derived approximations and closed-form expressions.
result Accuracy of approximations for target volatility option pricing in various parameter ranges.
We consider a discrete-time approximation of paths of an Ornstein--Uhlenbeck process as a mean for estimation of a price of European call option in the model of financial market with stochastic volatility. The Euler--Maruyama approximation scheme is implemented. We determine the estimates for the option price for prede…
New neural network approximates convex option prices.
problem Approximating prices of options with convex payoffs.
method Input Convex Neural Network (ICNN) architecture, with a scrambling phase.
result Validated convergence and effectiveness in estimating option prices.
Paper approximates first passage time for tempered stable process for option pricing.
problem Pricing perpetual American options and barrier options using first passage time.
method Approximates characteristic function using martingale approach.
result Provides explicit or indirect numerical method for characteristic function of first passage time.
Develops optimal liquidation strategies with stochastic price impact.
problem Optimal liquidation under price impact with stochastic parameters.
method Coefficient expansion on Hamilton-Jacobi-Bellman equation, solving PDEs for value function and optimal strategy.
result Closed-form approximations to value function and optimal liquidation strategy.
In this paper we derive an easily computed approximation to European basket call prices for a local volatility jump-diffusion model. We apply the asymptotic expansion method to find the approximate value of the lower bound of European basket call prices. If the local volatility function is time independent then there i…
In this paper we consider the pricing of options on interest rates such as caplets and swaptions in the Lévy Libor model developed by Eberlein and Özkan (2005). This model is an extension to Lévy driving processes of the classical log-normal Libor market model (LMM) driven by a Brownian motion. Option pricing is signif…
The article calculates the most-likely path for Asian option pricing in local volatility models.
problem Approximating the price of Asian options in local volatility models.
method Path-integral approach using Brownian bridge and Laplace asymptotic formula.
result The most-likely path (MLP) is found to approximate the option price in the limit of small sampling time.
We derive semi-analytic approximation formulae for bond and swaption prices in a Black-Karasiński interest rate model. Approximations are obtained using a novel technique based on the Karhunen-Loève expansion. Formulas are easily computable and prove to be very accurate in numerical tests. This makes them useful for nu…
New method improves credit risk estimation and pricing.
problem Estimating risk measures and CDO tranche prices in credit portfolios.
method Mod-Poisson approximation schemes based on mod-φ convergence.
result The method provides more accurate estimates with less computational time.