Date | 2018-11-28 |
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Speaker | 최재혁 |

Dept. | Peking University, HSBC Business School |

Room | 27-325 |

Time | 17:00-18:30 |

The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz

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 effciently 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.

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