Optimization of maintenance policy under parameter uncertainty using portfolio theory

Robustness-based portfolio optimization under epistemic uncertainty

In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a ...

Minimax Portfolio Optimization Under Interval Uncertainty

In the 1950s, Markowitz proposed to combine different investment instruments to design a portfolio that either maximizes the expected return under constraints on volatility (risk) or minimizes the risk under given expected return. Markowitz’s formulas are still widely used in financial practice. However, these formulas assume that we know the exact values of expected return and variance for eac...

Portfolio Selection with Parameter and Model Uncertainty ∗

In this paper, we extend the mean-variance portfolio model to explicitly account for uncertainty about the estimated expected returns and/or the underlying return-generating model. We do this by first imposing an additional constraint on the mean-variance portfolio optimization program that restricts each parameter to lie within a specified confidence interval of its estimated value, and then b...

Parameter Uncertainty in Credit Risk Portfolio Models

Truth Maintenance Under Uncertainty

This paper addresses the problem of resolving errors under uncertainty in a rule-based sys­ tem. A new approach has been developed that reformulates this problem as a neural-network learning problem. The strength and the fun­ damental limitations of this approach are ex­ plored and discussed. The main result is that neural heuristics can be applied to solve some but not all problems in rule-bas...