TL;DR
A mean-reverting SDE: \( dX_t = \theta(\mu - X_t)dt + \sigma dW_t \). The process is pulled toward \( \mu \) at rate \( \theta \), with solution \( X_t = \mu + (X_0 - \mu)e^{-\theta t} + \sigma \int_0^t e^{-\theta(t-s)}dW_s \).
Ornstein-Uhlenbeck Process
A mean-reverting SDE: \( dX_t = \theta(\mu - X_t)dt + \sigma dW_t \). The process is pulled toward \( \mu \) at rate \( \theta \), with solution \( X_t = \mu + (X_0 - \mu)e^{-\theta t} + \sigma \int_0^t e^{-\theta(t-s)}dW_s \).
Why it matters for interviews
The standard mean-reversion model in quantitative finance. Used for interest rate modeling (Vasicek), pairs trading (spread dynamics), and volatility modeling. Understanding its stationary distribution and half-life is interview material.
Definition and Mathematical Foundation
A mean-reverting SDE: \( dX_t = \theta(\mu - X_t)dt + \sigma dW_t \). The process is pulled toward \( \mu \) at rate \( \theta \), with solution \( X_t = \mu + (X_0 - \mu)e^{-\theta t} + \sigma \int_0^t e^{-\theta(t-s)}dW_s \).
Application in Quantitative Finance
The standard mean-reversion model in quantitative finance. Used for interest rate modeling (Vasicek), pairs trading (spread dynamics), and volatility modeling. Understanding its stationary distribution and half-life is interview material.
Related Concepts
Related Terms
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