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Monte Carlo methods have become a staple use in risk departments of many financial institutions as these methods are relatively fast to compute even at higher dimensions and provide risk metrics such as percentile values. Two classical methods used for derivatives with early exercise features are the Longstaff Schwartz Least-Squares method and Tilley bundling. This paper explains clearly the steps involved in evaluating the value of an American option and how these can be extended to evaluate risk metrics. While best estimate values are known to be fairly similar, discrepancies in risk pricing are noticed.

Evaluating Credit Counterparty Risk of American Options via Monte Carlo Methods: A Comparison of Tilley Bundling and Longstaff-Schwartz LSM