Ebner, BrunoMeintanis, Simos G.Klar, Bernhard2017-04-072017-04-072018Ebner, 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]0932-50261613-9798 (Online)http://hdl.handle.net/10394/21163https://link.springer.com/article/10.1007%2Fs00362-016-0803-6https://doi.org/10.1007/s00362-016-0803-6We 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 marketsenStochastic volatility modelMinimum distance estimationHeavy-tailed distributionCharacteristic functionArticle