Multi-period Mean-Buffered Probability of Exceedance in Defined Contribution Portfolio Optimization
Duy-Minh Dang, Chang Chen

TL;DR
This paper explores multi-period portfolio optimization using buffered Probability of Exceedance (bPoE), a tail-focused risk measure, demonstrating its advantages over CVaR in long-term investment strategies for defined contribution plans.
Contribution
It formulates and analyzes pre-commitment and time-consistent mean-bPoE portfolio optimization problems under realistic constraints and jump-diffusion dynamics, establishing theoretical equivalences and developing numerical schemes.
Findings
Time-consistent mean-bPoE strategies resemble pre-commitment ones.
bPoE effectively guards against catastrophic shortfalls.
bPoE aligns with investors' wealth security preferences.
Abstract
We investigate multi-period mean-risk portfolio optimization for long-horizon Defined Contribution plans, focusing on buffered Probability of Exceedance (bPoE), a more intuitive, dollar-based alternative to Conditional Value-at-Risk (CVaR). We formulate both pre-commitment and time-consistent Mean-bPoE and Mean-CVaR portfolio optimization problems under realistic investment constraints (e.g., no leverage, no short selling) and jump-diffusion dynamics. These formulations are naturally framed as bilevel optimization problems, with an outer search over the shortfall threshold and an inner optimization over rebalancing decisions. We establish an equivalence between the pre-commitment formulations through a one-to-one correspondence of their scalarization optimal sets, while showing that no such equivalence holds in the time-consistent setting. We develop provably convergent numerical…
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Taxonomy
TopicsRisk and Portfolio Optimization · Stochastic processes and financial applications · Financial Markets and Investment Strategies
