Fourier inference for stochastic volatility models with heavy-tailed innovations (Q1785815)
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scientific article; zbMATH DE number 6945914
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Fourier inference for stochastic volatility models with heavy-tailed innovations |
scientific article; zbMATH DE number 6945914 |
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Fourier inference for stochastic volatility models with heavy-tailed innovations (English)
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1 October 2018
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The authors study stochastic volatility models which are driven by a heavy-tailed innovation distribution. They propose an estimator which minimizes a weighted \(L_2\)-type distance between the theoretical characteristic function of the transformed observations and an empirical counterpart. A related goodness-of-fit test is also proposed and Monte Carlo results are presented. The procedures are applied to daily observations of the NASDAQ stock price index.
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stochastic volatility model
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minimum distance estimation
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heavy-tailed distribution
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characteristic function
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