Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287127 
Year of Publication: 
2021
Citation: 
[Journal:] Computational Economics [ISSN:] 1572-9974 [Volume:] 59 [Issue:] 1 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 185-224
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Curse of dimensionality
Dynamic portfolio choice
Discrete time dynamic programming
Gradient-based optimization
Spatially adaptive sparse grids
Hierarchical B-splines
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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