Dynamic mean variance asset allocation: Tests for robustness

Peter A. Forsyth and Kenneth R. Vetzal ()
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Peter A. Forsyth: David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1, Canada
Kenneth R. Vetzal: #x2020;School of Accounting and Finance, University of Waterloo, Waterloo, ON, Canada N2L 3G1, Canada

International Journal of Financial Engineering (IJFE), 2017, vol. 04, issue 02n03, 1-37

Abstract: We consider a portfolio consisting of a risk-free bond and an equity index which follows a jump diffusion process. Parameters for the inflation-adjusted return of the stock index and the risk-free bond are determined by examining 89 years of data. The optimal dynamic asset allocation strategy for a long-term pre-commitment mean variance (MV) investor is determined by numerically solving a Hamilton–Jacobi–Bellman partial integro-differential equation. The MV strategy is mathematically equivalent to minimizing the quadratic shortfall of the target terminal wealth. We incorporate realistic constraints on the strategy: discrete rebalancing (yearly), maximum leverage, and no trading if insolvent. Extensive synthetic market tests and resampled backtests of historical data indicate that the multi-period MV strategy achieves approximately the same expected terminal wealth as a constant weight strategy, but with much smaller variance and probability of shortfall.

Keywords: Mean variance; dynamic asset allocation; resampled backtests (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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DOI: 10.1142/S2424786317500219

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