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Large Deviations of Factor Models with Regularly-Varying Tails: Asymptotics and Efficient Estimation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pourbabaee, Farzad Solari, Omid Shams |
| Copyright Year | 2017 |
| Abstract | We analyze the Large Deviation Probability (LDP) of linear factor models generated from non-identically distributed components with regularly-varying tails, a large subclass of heavy tailed distributions. An efficient sampling method for LDP estimation of this class is introduced and theoretically shown to exponentially outperform the crude Monte-Carlo estimator, in terms of the coverage probability and the confidence interval's length. The theoretical results are empirically validated through stochastic simulations on independent non-identically Pareto distributed factors. The proposed estimator is available as part of a more comprehensive CMC package. |
| File Format | PDF HTM / HTML |
| DOI | 10.2139/ssrn.2931587 |
| Alternate Webpage(s) | http://cdar.berkeley.edu/wp-content/uploads/2017/01/Risk-seminar-April-2017.pdf |
| Alternate Webpage(s) | https://arxiv.org/pdf/1903.12299v3.pdf |
| Alternate Webpage(s) | https://export.arxiv.org/pdf/1903.12299 |
| Alternate Webpage(s) | https://doi.org/10.2139/ssrn.2931587 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
