Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids

Peter Schober (), Julian Valentin () and Dirk Pflüger ()
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Peter Schober: Goethe University Frankfurt
Julian Valentin: University of Stuttgart
Dirk Pflüger: University of Stuttgart

Computational Economics, 2022, vol. 59, issue 1, No 10, 185-224

Abstract: Abstract Discrete time dynamic programming to solve dynamic portfolio choice models has three immanent issues: firstly, the curse of dimensionality prohibits more than a handful of continuous states. Secondly, in higher dimensions, even regular sparse grid discretizations need too many grid points for sufficiently accurate approximations of the value function. Thirdly, the models usually require continuous control variables, and hence gradient-based optimization with smooth approximations of the value function is necessary to obtain accurate solutions to the optimization problem. For the first time, we enable accurate and fast numerical solutions with gradient-based optimization while still allowing for spatial adaptivity using hierarchical B-splines on sparse grids. When compared to the standard linear bases on sparse grids or finite difference approximations of the gradient, our approach saves an order of magnitude in total computational complexity for a representative dynamic portfolio choice model with varying state space dimensionality, stochastic sample space, and choice variables.

Keywords: Curse of dimensionality; Dynamic portfolio choice; Discrete time dynamic programming; Gradient-based optimization; Spatially adaptive sparse grids; Hierarchical B-splines (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-020-10061-x

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