Multi-dimensional backward stochastic differential equations of diagonally quadratic generators: The general result

• Published in: Journal of Differential Equations Vol. 368; pp. 105 - 140
• Main Authors: Fan, Shengjun, Hu, Ying, Tang, Shanjian
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 25.09.2023; Elsevier
• Subjects: BMO martingale; Convex generator; Diagonally quadratic generator; Mathematics; Multi-dimensional BSDE; Probability; Unbounded terminal value; 60H10; Multi-dimensional BSDE; BMO martingale; Unbounded terminal value; Convex generator; Diagonally quadratic generator
• ISSN: 0022-0396; 1090-2732
• DOI: 10.1016/j.jde.2023.05.041

This paper is devoted to a general solvability of a multi-dimensional backward stochastic differential equation (BSDE) of a diagonally quadratic generator g(t,y,z), by relaxing the assumptions of Hu and Tang [15] on the generator and terminal value. More precisely, the generator g(t,y,z) can have more general growth and continuity in the first unknown variable y in the local solution; while in the global solution, the generator g(t,y,z) can have a skew sub-quadratic but in addition “strictly and diagonally” quadratic growth in the second unknown variable z, or the terminal value can be unbounded but the generator g(t,y,z) is “diagonally dependent” on the variable z (i.e., the i-th component gi of the generator g only depends on the i-th row zi of z for each i=1,⋯,n). Three new results are established on the local and global solutions when the terminal value is bounded and the generator g is subject to some general assumptions. When the terminal value is unbounded but is of exponential moments of arbitrary order, an existence and uniqueness result is given under the assumptions that the generator g(t,y,z) is Lipschitz continuous in y, and varies with z in a “diagonal”, “component-wisely convex or concave”, and “quadratically growing” way, which seems to be the first general solvability of system of quadratic BSDEs with unbounded terminal values. This generalizes and strengthens some existing results via some new ideas.