The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz [2001] is the most widely used method for pricing options with early exercise features . The LSM estimator contains  look-ahead bias, and the conventional technique of removing it necessitates an independent set of simulations. This study proposes a new approach for efficiently eliminating look-ahead bias by using the leave-one-out method , a well-known cross-validation technique for machine learning applications. The leave-one-out LSM (LOOLSM) method is illustrated with examples, including  multi-asset options whose LSM price is biased high. The asymptotic behavior of look-ahead bias is also discussed with the LOOLSM approach .
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 .
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the most widely used method for pricing options with early exercise features
widely used for pricing American options
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undesirable
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an independent set of simulations. This study proposes a new approach
doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm
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by using the leave-one-out method , a well-known cross-validation technique for machine learning applications. The leave-one-out LSM (LOOLSM) method is illustrated with
. We validate the method with several option
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the
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options whose LSM price is biased high. The asymptotic behavior
cases that the LSM algorithm significantly overvalues. We also obtain the convergence rates
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is also discussed with the LOOLSM approach
by measuring it using the LOOLSM method. The analysis and computational evidence support our findings