Analysis · Concept hub

Stochastic Differential Equations

1944 CE20th-century Japan (Itô)

Through Stochastic Differential Equations: How can instantaneous change and long accumulation become one language?

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Understand it in one breath

Add random fluctuations such as Brownian motion to a deterministic rate of change. SDEs model finance, particle diffusion, and biology, but real stock prices do not exactly follow one simple geometric-Brownian model. The 1997 economics prize recognized Merton and Scholes for a new method of valuing derivatives; collaborator Black had died and Nobel prizes are not awarded posthumously.

At a glance

Model

SDE

Application

Geometric Brownian motion (GBM)

dS = μS dt + σS dW

Stock prices; Black–Scholes

Vasicek interest-rate model

dr = a(b−r) dt + σ dW

Mean-reverting interest-rate model

Heston volatility model

dv = κ(θ−v) dt + ξ√v dW

Volatility is stochastic too

Langevin equation

dX = −∇U dt + √(2D) dW

Particle physics and MCMC

Neural SDE

dh = f(h,t) dt + g(h,t) dW

Stochastic neural networks (2018+)

The Black–Scholes–Merton model supplies a benchmark option price under specific assumptions. Real markets require richer models and risk management for volatility smiles, jumps, transaction costs, and other effects.

Concept

Differential equations with a random noise term. Foundation of Black-Scholes option pricing (1973) and modern finance.

Key formula

dXt=μ(Xt,t)dt+σ(Xt,t)dWtdX_t = \mu(X_t, t)\, dt + \sigma(X_t, t)\, dW_t

Modern applications

Option pricing, interest-rate models, algorithmic trading, and policy-gradient methods in reinforcement learning.

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Stochastic Differential Equations

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