Need to Meet Investment Goals? Track Synthetic Indexes with the SDDP Method

Area of Science:

• Quantitative Finance
• Computational Economics
• Financial Engineering

Background:

• Traditional asset allocation models face challenges with large-scale portfolios and dynamic market conditions.
• Developing robust allocation policies requires advanced computational methods to handle complex financial data.
• User-defined performance benchmarks are crucial for evaluating portfolio management strategies.

Purpose of the Study:

• To introduce a novel application of Stochastic Dual Dynamic Programming (SDDP) for optimizing large-scale asset allocation.
• To develop and implement an SDDP-based model for generating portfolio allocation policies.
• To evaluate the performance of the proposed model against established benchmarks using real-world financial data.

Main Methods:

• Implementation of a Stochastic Dual Dynamic Programming (SDDP) algorithm within an open-source package.
• Construction of an asset allocation model that measures portfolio performance relative to synthetic indexes.
• Generation of Markovian regime-dependent returns using US economic cycle data and ETF information for a multi-asset, 28-period scenario.

Main Results:

• The SDDP-based asset allocation strategy demonstrated superior performance compared to its benchmark.
• The proposed model achieved higher profitability in the tested scenarios.
• The solution effectively minimized tracking error, indicating better adherence to desired performance metrics.

Conclusions:

• Stochastic Dual Dynamic Programming (SDDP) offers a powerful and effective framework for large-scale asset allocation problems.
• The developed model provides a practical tool for creating data-driven allocation policies that outperform traditional benchmarks.
• This research highlights the potential of advanced computational finance techniques in enhancing portfolio management strategies.