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<p>In this paper, we propose a purely dual algorithm for pricing the high-dimensional Bermudan options. Our algorithm minimizes the empirical variance of dual formulations over a parameterized set of martingales. The set of martingales is constructed based on kernel methods such that it handles high-dimensional options. We show the convergence of the proposed algorithm and the flexibility of the parameterized martingales. The numerical experiments show that our method produces accurate results in high-dimensional situations with low variance.</p>

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Pricing High-Dimensional Bermudan Options via Kernel-Based Dual Variance Minimization

  • 期刊论文

  • 发表日期：

    2025年4月11日

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  • Nan Li

摘要

In this paper, we propose a purely dual algorithm for pricing the high-dimensional Bermudan options. Our algorithm minimizes the empirical variance of dual formulations over a parameterized set of martingales. The set of martingales is constructed based on kernel methods such that it handles high-dimensional options. We show the convergence of the proposed algorithm and the flexibility of the parameterized martingales. The numerical experiments show that our method produces accurate results in high-dimensional situations with low variance.