A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Path integral method calculates barrier option prices.
Path integral method calculates PDBS option prices with time-dependent parameters.
The paper uses moment matching method for pricing spread options under Lévy models.
Paper proposes a new method to compute cryptocurrency prices securely.
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 …
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.
Revisits SWIFT method for option pricing using Shannon wavelets.
New approach for pricing evaluation improves on existing methods.
Improved pricing method for illiquid assets using Lambert function.
Efficient method for pricing European and American options using Markov switching stochastic volatility model.
New method uses machine learning to optimize Fourier pricing methods.
Paper proposes a new method for demand forecasting in pricing contexts.
Paper uses AI methods to forecast Bitcoin prices.
The paper uses a Hamiltonian method to price barrier options under Vasicek interest rate model.
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…
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.
Tensor trains speed up option pricing for multi-asset options.
New method learns credit prices offline without interaction.
Fast probabilistic option price predictions using modular Bayesian inference.
Tensor networks improve exotic option pricing efficiency.
In the paper, the pricing of Quanto options is studied, where the underlying foreign asset and the exchange rate are correlated with each other. Firstly, we adopt Bayesian methods to estimate unknown parameters entering the pricing formula of Quanto options, including the volatility of stock, the volatility of exchange…
A new deep learning method for option pricing in rough volatility models.
Quantum computing speeds up pricing multi-asset derivatives.
A new NUFFT method speeds up option pricing for various strikes.
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…
New formulas derived for variance gamma model option pricing.
The paper prices energy spread options using a complex stochastic model.
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…
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A…
Efficient method for lookback option pricing under Markov models.
Paper presents deep LSMC method for efficient variable annuity pricing.
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-…
This work illustrates how several new pricing formulas for exotic options can be derived within a Levy framework by employing a unique pricing expression. Many existing pricing formulas of the traditional Gaussian model are obtained as a by-product.
A new SINC method for fast and accurate option pricing.
A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading…
Method determines asset prices in incomplete markets to optimize portfolios.
A new method forecasts hourly electricity prices considering product dynamics and limit order book signals.
Adapts Monte Carlo method to price π-options related to maximum drawdown.
Efficiently price high-dimensional Bermudan options using tensor compression.
The Libor market model is a mainstay term structure model of interest rates for derivatives pricing, especially for Bermudan swaptions, and other exotic Libor callable derivatives. For numerical implementation the pricing of derivatives with Libor market models is mainly carried out with Monte Carlo simulation. The PDE…
New data improves market impact estimation methods.
How does dynamic price information flow among Northern European electricity spot prices and prices of major electricity generation fuel sources? We use time series models combined with new advances in causal inference to answer these questions. Applying our methods to weekly Nordic and German electricity prices, and oi…
This paper applies an algorithm for the convolution of compactly supported Legendre series (the CONLeg method) (cf. Hale and Townsend 2014a), to pricing/hedging European-type, early-exercise and discrete-monitored barrier options under a Levy process. The paper employs Chebfun (cf. Trefethen et al. 2014) in computation…
Paper compares MCMC-based copula methods for exchange option pricing.
RL methods applied to option pricing using modified QLBS and RLOP models.
Mainstream financial econometrics methods are based on models well tuned to replicate price dynamics, but with little to no economic justification. In particular, the randomness in these models is assumed to result from a combination of exogenous factors. In this paper, we present a model originating from game theory, …
Contrary to the common view that exact pricing is prohibitive owing to the curse of dimensionality, this study proposes an efficient and unified method for pricing options under multivariate Black-Scholes-Merton (BSM) models, such as the basket, spread, and Asian options. The option price is expressed as a quadrature i…