A backward stochastic differential equation (BSDE) is a stochastic differential equation with a terminal condition in which the solution is required to be adapted with respect to an underlying filtration. BSDEs naturally arise in various applications such as stochastic control, mathematical finance, and nonlinear Feynman-Kac formulae.^[1]

Background

Backward stochastic differential equations were introduced by Jean-Michel Bismut in 1973 in the linear case^[2] and by Étienne Pardoux and Shige Peng in 1990 in the nonlinear case.^[3]

Mathematical framework

Fix a terminal time ${\displaystyle T>0}$ and a probability space ${\displaystyle (\Omega ,{\mathcal {F}},\mathbb {P} )}$. Let ${\displaystyle (B_{t})_{t\in [0,T]}}$ be a Brownian motion with natural filtration ${\displaystyle ({\mathcal {F}}_{t})_{t\in [0,T]}}$. A backward stochastic differential equation is an integral equation of the type

${\displaystyle Y_{t}=\xi -\int _{t}^{T}f(s,Y_{s},Z_{s})\mathrm {d} s-\int _{t}^{T}Z_{s}\mathrm {d} B_{s},\quad t\in [0,T],}$

(1)

where ${\displaystyle f:[0,T]\times \mathbb {R} \times \mathbb {R} \to \mathbb {R} }$ is called the generator of the BSDE, the terminal condition ${\displaystyle \xi }$ is an ${\displaystyle {\mathcal {F}}_{T}}$-measurable random variable, and the solution ${\displaystyle (Y_{t},Z_{t})_{t\in [0,T]}}$ consists of stochastic processes ${\displaystyle (Y_{t})_{t\in [0,T]}}$ and ${\displaystyle (Z_{t})_{t\in [0,T]}}$ which are adapted to the filtration ${\displaystyle ({\mathcal {F}}_{t})_{t\in [0,T]}}$.

See also

• Martingale representation theorem
• Stochastic control
• Stochastic differential equation

References

Further reading

• Pardoux, Etienne; Rӑşcanu, Aurel (2014). Stochastic Differential Equations, Backward SDEs, Partial Differential Equations. Stochastic modeling and applied probability. Springer International Publishing Switzerland.
• Zhang, Jianfeng (2017). Backward stochastic differential equations. Probability theory and stochastic modeling. Springer New York, NY.