Derivatives Portfolio Optimization and Parameter Uncertainty

6 Pages Posted: 14 May 2024 Last revised: 15 May 2024

Date Written: May 13, 2024

Abstract

Portfolio optimization in practice almost always needs to account for parameter uncertainty. Resampled portfolio optimization is a common heuristic to tackle the parameter uncertainty issue. The recently introduced Exposure Stacking method makes the resampled approach even more attractive. While resampled optimization of cash portfolios is straightforward and well-known, resampled optimization of portfolios containing derivatives is a largely unexplored area. Derivatives introduce an additional layer of complexity, because there needs to be a logical consistency between the parameter uncertainty of the underlying, risk factors such as implied volatilities, and the derivative instrument's P&L. This article presents an elegant solution to the problem and introduces a new class of portfolio optimization with fully general risk factor parameter uncertainty.

Keywords: Portfolio optimization, parameter uncertainty, derivatives, risk factors, Exposure Stacking, mean-CVaR, tail risk, efficient portfolio, efficient frontier, mean squared error, bias-variance trade-off, stacked generalization, quadratic programming, convex optimization, Python Programming Language

JEL Classification: C00, C01, C02, C58, C60, C61, G00, G10, G11, G17

Suggested Citation

Vorobets, Anton, Derivatives Portfolio Optimization and Parameter Uncertainty (May 13, 2024). Available at SSRN: https://ssrn.com/abstract=4825945 or http://dx.doi.org/10.2139/ssrn.4825945

Anton Vorobets (Contact Author)

Fortitudo Technologies ( email )

Østre Stationsvej 39B, 8. th.
Odense C, 5000
Denmark

HOME PAGE: http://fortitudo.tech

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