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

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2019
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Abstract
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.
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dominic2019frontiersevaluating Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Cortis, Dominic;
Journal frontiers in applied mathematics and statistics
Year 2019
DOI
doi:10.3389/fams.2019.00060
URL
https://doi.org/10.3389/fams.2019.00060
Keywords
credit counterparty risk expected potential exposure potential future exposure american options monte carlo methods monte carlo methods american options credit counterparty risk potential future exposure expected potential exposure

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