| dc.contributor.author | Ebner, Bruno | |
| dc.contributor.author | Meintanis, Simos G. | |
| dc.contributor.author | Klar, Bernhard | |
| dc.date.accessioned | 2017-04-07T06:58:21Z | |
| dc.date.available | 2017-04-07T06:58:21Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | Ebner, B. et al. 2018. Fourier inference for stochastic volatility models with heavy-tailed innovations. Statistical papers, 59(3):1043-1060. [https://doi.org/10.1007/s00362-016-0803-6] | en_US |
| dc.identifier.issn | 0932-5026 | |
| dc.identifier.issn | 1613-9798 (Online) | |
| dc.identifier.uri | http://hdl.handle.net/10394/21163 | |
| dc.identifier.uri | https://link.springer.com/article/10.1007%2Fs00362-016-0803-6 | |
| dc.identifier.uri | https://doi.org/10.1007/s00362-016-0803-6 | |
| dc.description.abstract | We consider estimation of stochastic volatility models which are driven by a heavy-tailed innovation distribution. Exploiting the simple structure of the characteristic function of suitably transformed observations we propose an estimator which minimizes a weighted L2-type distance between the theoretical characteristic function of these observations and an empirical counterpart. A related goodness-of-fit test is also proposed. Monte-Carlo results are presented. The procedures are also applied to real data from the financial markets | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Stochastic volatility model | en_US |
| dc.subject | Minimum distance estimation | en_US |
| dc.subject | Heavy-tailed distribution | en_US |
| dc.subject | Characteristic function | en_US |
| dc.title | Fourier inference for stochastic volatility models with heavy-tailed innovations | en_US |
| dc.type | Article | en_US |
| dc.contributor.researchID | 21262977 - Meintanis, Simos George | |
