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Pricing American-Style Options by Monte Carlo Simulation: Alternatives to Ordinary Least Squares
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tompaidis, Stathis Yang, Chunyu |
| Copyright Year | 2014 |
| Abstract | We investigate the performance of the ordinary least squares (OLS) regression method in Monte Carlo simulation algorithms for pricing American options. We compare OLS regression against several alternatives and find that OLS regression underperforms methods that penalize the size of coefficient estimates when the number of simulation paths is small. Based on our findings, we recommend an alternative method based on a modification of the “matching projection pursuit” that we introduce in this paper. |
| Starting Page | 121 |
| Ending Page | 143 |
| Page Count | 23 |
| File Format | PDF HTM / HTML |
| DOI | 10.21314/JCF.2014.279 |
| Alternate Webpage(s) | https://faculty.mccombs.utexas.edu/Stathis.Tompaidis/code/LSM/lsm_paper.pdf |
| Alternate Webpage(s) | https://faculty.mccombs.utexas.edu/Stathis.Tompaidis/code/LSM/LSM_Paper_Code_Manual.pdf |
| Alternate Webpage(s) | http://faculty.mccombs.utexas.edu/Stathis.Tompaidis/code/LSM/lsm_paper.pdf |
| Alternate Webpage(s) | https://doi.org/10.21314/JCF.2014.279 |
| Volume Number | 18 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |