Fourier inference for stochastic volatility models with heavy-tailed innovations

被引：2

作者：

Ebner, Bruno ^{[1
]}

Klar, Bernhard ^{[1
]}

Meintanis, Simos G. ^{[2
,3
]}

机构：

[1] Karlsruhe Inst Technol, Inst Stochast, Karlsruhe, Germany

[2] Natl & Kapodistrian Univ Athens, Dept Econ, Athens, Greece

[3] North West Univ, Unit Business Math & Informat, Potchefstroom, South Africa

来源：

STATISTICAL PAPERS | 2018年 / 59卷 / 03期

关键词：

Stochastic volatility model; Minimum distance estimation; Heavy-tailed distribution; Characteristic function; EMPIRICAL CHARACTERISTIC FUNCTION; DISTRIBUTIONS;

D O I：

10.1007/s00362-016-0803-6

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 L-2-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.

引用

页码：1043 / 1060

页数：18

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