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Leave-One-Out Least Square Monte Carlo Algorithm for Pricing American Options

Jeechul Woo, Chenru Liu and Jaehyuk Choi

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Abstract: The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz [2001] is widely used for pricing American options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias. We validate the method with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. We also obtain the convergence rates of look-ahead bias by measuring it using the LOOLSM method. The analysis and computational evidence support our findings.

New Economics Papers: this item is included in nep-big and nep-cmp
Date: 2018-10, Revised 2019-05
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