CV@R penalized portfolio optimization with biased stochastic mirror descent

Sébastien Gadat, Manon Costa and Lorick Huang

No 22-1342, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: This article studies and solves the problem of optimal portfolio allocation with CV@R penalty when dealing with imperfectly simulated financial assets. We use a Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio whose underlying assets cannot be generated exactly and may only be approximated with a numerical scheme that satisfies suitable error bounds, under a risk management constraint. We establish almost sure asymptotic properties as well as the rate of convergence for the averaged algorithm. We then focus on the optimal tuning of the overall procedure to obtain an optimized numerical cost. Our results are then illustrated numerically on simulated as well as real data sets.

Keywords: Stochastic Mirror Descent; Biased observations,; Risk management constraint; Portfolio selection; Discretization (search for similar items in EconPapers)
Date: 2022-06-21, Revised 2023-11
New Economics Papers: this item is included in nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:127041

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