Model risk of the implied GARCH-normal model

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105838

Title:	Model risk of the implied GARCH-normal model
Authors:	黃士峰;Huang, Shih-Feng;Guo, Meihui
Contributors:	理學院統計研究所
Keywords:	Conditional leptokurtic model;Dynamic semiparametric approach;Extended Girsanov principle;GARCH model;Hedging;Investment policy;Model risk;Option pricing;Rates of return;Risk management;Securities prices;Stochastic models;Studies
Date:	2014-12-13
Issue Date:	2026-04-23 12:56:27 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Bristol: Routledge
Abstract:	摘要： Model risk causes significant losses in financial derivative pricing and hedging. Investors may undertake relatively risky investments due to insufficient hedging or overpaying implied by flawed models. The GARCH model with normal innovations (GARCH-normal) has been adopted to depict the dynamics of the returns in many applications. The implied GARCH-normal model is the one minimizing the mean square error between the market option values and the GARCH-normal option prices. In this study, we investigate the model risk of the implied GARCH-normal model fitted to conditional leptokurtic returns, an important feature of financial data. The risk-neutral GARCH model with conditional leptokurtic innovations is derived by the extended Girsanov principle. The option prices and hedging positions of the conditional leptokurtic GARCH models are obtained by extending the dynamic semiparametric approach of Huang and Guo [Statist. Sin., 2009, 19, 1037-1054]. In the simulation study we find significant model risk of the implied GARCH-normal model in pricing and hedging barrier and lookback options when the underlying dynamics follow a GARCH-t model. 出版者： Bristol: Routledge 出版日期： 2014-12-02 出處： Quantitative finance, 2014-12, Vol.14 (12), p.2215-2224 資源來源： Taylor & Francis Journals 版權： 2011 Taylor & Francis 2011 版權： Copyright American Institute of Physics 2014 識別號： ISSN: 1469-7688 識別號： EISSN: 1469-7696 識別號： DOI: 10.1080/14697688.2011.630323
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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