Stochastic Calculus for Finance I: Binomial asset pricing model and Stochastic Calculus for Finance II: tochastic Calculus for Finance II: Continuous-Time Models. These two books are very good if you want to apply the theory to price derivatives. Stochastic Differential Equations: An Introduction with Applications Bernt Oksanda.
Stochastic differential equations (SDEs) provide a foundational framework for modelling systems subject to randomness, incorporating both continuous fluctuations and abrupt changes. In recent decades ...
Risk: Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
CU Boulder News & Events: Direct Numerical Solutions to Stochastic Differential Equations with Multiplicative Noise
Inspired by path integral solutions to the quantum relaxation problem, we develop a numerical method to solve classical stochastic differential equations with multiplicative noise that avoids ...
Backward Stochastic Differential Equations (BSDEs) constitute a powerful framework where the solution is determined by a terminal condition and then propagated backwards in time. This innovative ...
Fuzzy differential equations (FDEs) extend classical differential equations by incorporating uncertainty through fuzzy numbers. This mathematical framework is particularly valuable for modelling ...
JSTOR Daily: Euler-Lagrange equations of stochastic differential games: application to a game of a productive asset
Euler-Lagrange equations of stochastic differential games: application to a game of a productive asset
Risk: Estimating risks of European option books using neural stochastic differential equation market models
In this paper we examine the capacity of arbitrage-free neural stochastic differential equation market models to produce realistic scenarios for the joint dynamics of multiple European options on a ...