Quadratic Hedging in the SVV Model

Giulia Di Nunno (), Yuliya Mishura () and Anton Yurchenko-Tytarenko ()
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Giulia Di Nunno: University of Oslo, Department of Mathematics
Yuliya Mishura: Taras Shevchenko National University of Kyiv, Department of Probability Theory, Statistics and Actuarial Mathematics
Anton Yurchenko-Tytarenko: Statkraft Energi AS

Chapter 9 in Volterra Volatility Models, 2026, pp 295-326 from Springer

Abstract: Abstract This chapter concludes the study of the Sandwiched Volterra Volatility (SVV) model by addressing the problem of quadratic mean-variance hedging. As perfect replication is not possible within the SVV framework, we focus on minimizing the expected squared hedging error for European-type payoffs. The optimal solution, classically characterized by the Galtchouk–Kunita–Watanabe decomposition, admits an explicit representation via the non-anticipating derivative. To make this representation numerically tractable, we build on previous developments by replacing the original volatility process with its Markovian approximation. Based on this approximation, we propose two numerical algorithms for computing the optimal hedging strategy: Nested Monte Carlo (NMC) and Least Squares Monte Carlo (LSMC).

Date: 2026
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DOI: 10.1007/978-3-032-26576-0_9

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