title: |
A class of nonlinear stochastic volatility models |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
ISBN: |
978-90-78677-01-7 | |
ISSN: |
1951-6851 | |
DOI: |
doi:10.2991/jcis.2006.87 (how to use a DOI) | |
author(s): |
Jun Yu, Zhenlin Yang |
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corresponding author: |
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publication date: |
October 2006 |
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keywords: |
Box-Cox Transform, EMM, GARCH |
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abstract: |
This paper proposes a class of nonlinear stochastic volatility (SV) models based on the Box-Cox transformation. The proposed class encompasses many parametric SV models that have appeared in the literature, including the well
known lognormal SV model, and has an advantage in the ease with which different specifications on SV can be tested. In addition, the functional form of transformation which induces marginal normality of volatility is obtained as a byproduct of this general way of modeling SV. Efficient method of moments is used to estimate the model. Empirical results reveal that the lognormal SV model is rejected. |
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copyright: |
©
Atlantis Press. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |