Mean–variance hedging of contingent claims with random maturity

Kamil Kladívko and Mihail Zervos

Mathematical Finance, 2023, vol. 33, issue 4, 1213-1247

Abstract: We study the mean–variance hedging of an American‐type contingent claim that is exercised at a random time in a Markovian setting. This problem is motivated by applications in the areas of employee stock option valuation, credit risk, or equity‐linked life insurance policies with an underlying risky asset value guarantee. Our analysis is based on dynamic programming and uses PDE techniques. In particular, we prove that the complete solution to the problem can be expressed in terms of the solution to a system of one quasi‐linear parabolic PDE and two linear parabolic PDEs. Using a suitable iterative scheme involving linear parabolic PDEs and Schauder's interior estimates for parabolic PDEs, we show that each of these PDEs has a classical C1, 2 solution. Using these results, we express the claim's mean–variance hedging value that we derive as its expected discounted payoff with respect to an equivalent martingale measure that does not coincide with the minimal martingale measure, which, in the context that we consider, identifies with the minimum entropy martingale measure as well as the variance‐optimal martingale measure. Furthermore, we present a numerical study that illustrates aspects of our theoretical results.

Date: 2023
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