Path integral method calculates barrier option prices.
problem Barrier option pricing in finance.
method Path integral method applied to trapezoid and square potential barriers.
result Analytical expressions for option pricing derived.
Path integral method calculates PDBS option prices with time-dependent parameters.
problem Pricing proportional double-barrier step options with time-dependent interest rates and volatilities.
method Path integral method applied to a quantum mechanical analogy of barrier options.
result Derivation of pricing kernel for PDBS options with time-dependent parameters.
Study finds similar price changes across various assets.
problem Understanding changes in different asset prices over time.
method Used traditional and spectral methods to analyze asset prices.
result Discoveries of universal phenomena across asset classes.
Bayesian methods improve Quanto option pricing accuracy.
problem Improving Quanto option pricing accuracy using Bayesian methods.
method Bayesian estimation of parameters and Monte Carlo simulation.
result Bayesian methods outperform other methods in Quanto option pricing.
Paper proposes a new method to compute cryptocurrency prices securely.
problem Accurate price feeds without a third party.
method Algorithmic method to compute prices from potentially dishonest sources.
result The proposed method can report accurate prices even from dishonest sources.
The paper uses moment matching method for pricing spread options under Lévy models.
problem Pricing spread options under Lévy models with mean-variance mixture.
method Moment matching method applied to Lévy models with mean-variance mixture.
result Obtains semi-closed form formulas for spread option prices.
If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator …
New approach for pricing evaluation improves on existing methods.
problem Improving off-policy evaluation for personalized pricing.
method Balanced policy evaluation framework with worst-case optimization.
result Empirical advantage over existing methods in pricing applications.
An analytic method for pricing American call options is provided; followed by an empirical method for pricing Asian call options. The methodology is the pricing theory presented in "A Modern Theory of Random Variation", by Patrick Muldowney, 2012.
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.
Revisits SWIFT method for option pricing using Shannon wavelets.
problem Improving option pricing under known characteristic functions.
method SWIFT method based on Shannon wavelets.
result Exposes drawbacks and discusses improvements.
Efficient method for pricing European and American options using Markov switching stochastic volatility model.
problem Modeling and pricing options under varying volatility and mean-reversion speeds.
method Discrete-time Markov switching stochastic volatility with co-jump model, computationally efficient approach for European options, and conversion to European option pricing for American options.
result Efficient and accurate methods for pricing options, including variance swap analysis.
Formula calculates bond prices between payments.
problem No new bond pricing formula available.
method Closed-form formula derivation.
result Formula accurately calculates bond prices.
Study on price-volume correlation fractal features and market type effects.
problem Understanding the fractal features and market type effects of price-volume correlation.
method Applied MF-DXA method to analyze price, trading volume, and their coupling.
result Price, trading volume, and price-volume coupling exhibit power law and multifractal properties.
New method uses machine learning to optimize Fourier pricing methods.
problem Difficulty in tuning parameters for Fourier pricing methods.
method Learning tuning parameters of Fourier methods using machine learning.
result Very fast algorithms with full error control.
Paper proposes a new method for demand forecasting in pricing contexts.
problem Demand forecasting in pricing contexts, especially in a profit optimal manner.
method Combines Double Machine Learning for causal inference and transformer-based forecasting models.
result Our method outperforms other forecasting methods in off-policy settings.
Unified method for efficient pricing of multivariate options.
problem Efficient pricing of complex financial options under multivariate models.
method Unified method using quadrature integration of multi-asset BSM prices, state space rotation.
result Unified method provides accurate and efficient pricing for basket, spread, and Asian options.
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.
The paper uses a Hamiltonian method to price barrier options under Vasicek interest rate model.
problem Option pricing under Vasicek interest rate model with time-varying interest rates.
method Splitting time to maturity into infinite steps and using quantum mechanics methods for matrix elements, derived pricing kernel and integral expression.
result Numerical results of option prices as functions of underlying asset price, floating rate, and regression rate.
Paper uses AI methods to forecast Bitcoin prices.
problem Inaccurate Bitcoin price predictions in previous studies.
method Combines EEMD and LSTM for next-day price forecast.
result Improves Bitcoin price prediction accuracy.
We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the stock price. The daily price jump at the opening is considered to be the result of the unobserved evolution of an after-hours virtual tradin…
New method for personalized pricing using invalid instrumental variables.
problem Personalized pricing under endogeneity with limited standard methods.
method PRINT method for continuous treatment, solving conditional moment restrictions.
result Established optimal pricing strategy under endogeneity with invalid instrumental variables.
Volatility modelling has become a significant area of research within Financial Mathematics. Wiener process driven stochastic volatility models have become popular due their consistency with theoretical arguments and empirical observations. However such models lack the ability to take into account long term and fundame…
Tensor trains speed up option pricing for multi-asset options.
problem Speeding up option pricing for multi-asset options.
method Tensor train learning algorithms to compress functions with parameter dependence.
result The proposed method outperforms Monte Carlo-based pricing in computational complexity.
Regression decision trees outperform other methods in predicting electricity prices.
problem Short-term forecasting of electricity prices to manage risk and strategy.
method Comparison of regression decision trees and recurrent neural networks (RNNs) with ARIMA.
result Regression decision trees achieve high performance compared to other methods.
New methods for pricing and hedging options on multiple assets.
problem Pricing and hedging options on multiple assets given market prices for individual assets.
method Two numerical methods: discretisation and linear programming, and penalisation and deep neural networks.
result Proved convergence and compared numerical performance of methods.
New method learns credit prices offline without interaction.
problem Dynamic pricing of consumer credit.
method Offline deep reinforcement learning with Q-Learning.
result Effective personalized pricing policy learned without online interaction.
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.
A fast method for pricing swaptions in Gaussian models.
problem Pricing swaptions in multi-factor Gaussian term structure models efficiently.
method Approximating exercise boundary by a hyperplane and simplifying multi-dimensional integration.
result Our method is superior to previous methods in accuracy and speed.
Tensor networks improve exotic option pricing efficiency.
problem Challenges in pricing exotic financial derivatives using standard methods.
method Combining binomial pricing with tensor network techniques (Matrix Product States).
result Linear scaling with parameters and reduced computational complexity.
A new pricing model from game theory fits financial data well.
problem Financial models lack economic justification and randomness assumptions.
method CMMV pricing model based on game theory and information asymmetry.
result The CMMV model predicts option prices and volatility surface well.
A new deep learning method for option pricing in rough volatility models.
problem Efficient pricing of European options in high-dimensional rough volatility models.
method Time-stepping deep gradient flow method reformulating the option pricing PDE as an energy minimization problem.
result The method respects asymptotic behavior and known bounds for option prices.
Develops hybrid method for efficient option pricing.
problem Stability and accuracy in option pricing models.
method Hybrid approach combining tree and finite-difference methods.
result Hybrid methods allow efficient and accurate European and American option pricing.
Paper presents a multinomial method for option pricing under Variance Gamma.
problem Option pricing under non-standard stochastic processes.
method Discrete time Markov chain approximation of continuous time Variance Gamma process.
result Pricing American and Bermudan options is feasible with this method.
This paper presents a method to estimate mid-prices of European corporate bonds using real-time dealer information.
problem Estimating mid-prices in illiquid markets where direct market prices are not available.
method Bayesian approach using particle filtering and sequential Monte Carlo.
result A new method for real-time mid-price estimation of corporate bonds.
Study new Hawkes processes to model price changes in limit order books.
problem Model price volatility in limit order books.
method Prove LLN and FCLTs for general compound and regime-switching general compound Hawkes processes.
result Volatilities of price changes are expressed in terms of parameters describing arrival rates and price changes.
Paper proposes machine learning for pricing 3D printing services in marketplaces.
problem Inefficient pricing methods for 3D printing services in marketplaces.
method Data mining and machine learning methods to estimate price ranges based on supplier and customer characteristics.
result Machine learning model achieves 65% accuracy for US suppliers and 59% for Europe suppliers in classifying 3D printer listings.
Quantum computing speeds up pricing multi-asset derivatives.
problem Exponential growth in complexity for multi-asset derivatives pricing.
method Quantum algorithm based on quantum linear system algorithms for FDM.
result Exponential speedup in derivative pricing compared to classical methods.
The paper offers methods to price complex options using upper and lower bounds.
problem Pricing complex options like Asian and basket options.
method Develops a general framework using lower and upper bounds.
result Lower bounds simplify the problem and provide reasonable approximations.
The CONLeg method prices and hedges various option types using Legendre series.
problem Pricing and hedging European-type, early-exercise, and discrete-monitored barrier options.
method Algorithm for the convolution of Legendre series (CONLeg method) applied to Levy process.
result High accuracy in pricing and hedging, especially for deep out-of-the-money and long/mature options.
The paper prices energy spread options using a complex stochastic model.
problem Pricing energy spread options with specific stochastic dynamics.
method Uses an exponential Ornstein-Uhlenbeck process driven by variance gamma processes, applying the Esscher transform and FFT method.
result Derives an analytical formula for pricing forwards and spread options.
A new NUFFT method speeds up option pricing for various strikes.
problem Efficiently pricing many options of the same maturity but different strikes.
method Non-uniform fast Fourier transform (NUFFT) applied to the COS method.
result Significantly faster computation of option prices.
We consider the pricing of derivatives written on the discretely sampled realized variance of an underlying security. In the literature, the realized variance is usually approximated by its continuous-time limit, the quadratic variation of the underlying log-price. Here, we characterize the small-time limits of options…
This paper introduces a new method to price long-dated insurance contracts.
problem Pricing of long-dated, insurance-type contracts is complex and inconsistent.
method Loading pricing combines theoretically minimal and formally risk-neutral prices.
result Loading degree is constant for minimally fluctuating contracts and is a key characteristic.
New formulas derived for variance gamma model option pricing.
problem Option pricing for the variance gamma model.
method Combining randomization method and fractional derivatives.
result Closed-form formulas for European options.
Options financial instruments designed to protect investors from the stock market randomness. In 1973, Fisher Black, Myron Scholes and Robert Merton proposed a very popular option pricing method using stochastic differential equations within the Ito interpretation. Herein, we derive the Black-Scholes equation for the o…
New method improves option pricing for non-smooth functions.
problem Inefficiency of Fourier techniques with non-smooth probability density functions.
method Singular Fourier-Padé (SFP) method
result Restores global spectral convergence rate and fast error convergence.
In this paper we present a new multi-asset pricing model, which is built upon newly developed families of solvable multi-parameter single-asset diffusions with a nonlinear smile-shaped volatility and an affine drift. Our multi-asset pricing model arises by employing copula methods. In particular, all discounted single-…