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 .
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 also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. The LOOLSM method can be extended to other regression-based algorithms improving the LSM method .
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validate the method
also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated
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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
The LOOLSM method can be extended to other regression-based algorithms improving the LSM method