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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | The benefits of returns and options in the estimation of GARCH models. A COMFORT insight |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 21 |
| Date | 2023 |
| Abstract Text | Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models have gained considerable attention since the contributions of Engle (1982) and Bollerslev (1986). These pioneers showed the time-varying volatility in financial asset returns. Building upon this, Duan (1995) further expanded the applicability of GARCH models by elucidating the framework and conditions necessary for integrating them into option pricing. In 2000, the well-know affine GARCH model (HN-GARCH), which was elaborated by S. L. Heston and Nandi (2000), brought an innovation by introducing closedform option pricing formulas, while capturing several stylized fact such as the price of risk, leverage effect (Black (1976) and Christie (1982)), news effect (Campbell and Hentschel (1992) and Bekaert and G. Wu (2000)), and time-varying conditional variances as expressed by a discrete-time GARCHtype process. As a consequence, many extensions have been emerged, which are called the affine GARCH family of models. Some examples : the GARCH(p,q) of Bollerslev (1986), the aysmmetric GARCH of Engle and Ng (1993) and the threshold GARCH of Glosten et al. (1993); for the empirical application into option pricing see Christoffersen, Jacobs, and Ornthanalai (2013); for comparisons between affine and non-affine GARCH models, see Hsieh and Ritchken (2006), Christoffersen, Jacobs, and Mimouni (2006) and Christoffersen, Dorion, et al. (2010). These affine GARCH extensions incorporate non-Gaussianity by Gaussian innovations (Christoffersen, Steve Heston, et al., 2006) or Levy jumps (Ornthanalai, 2014); and multivariate extensions which allows fast and accurate pricing of multi-asset options see Escobar-Anel, Rastegari, et al. (2020). Recently, the literature indicate the importance to include the stochastic jump into the stochastic volatility structure (see Chernov et al. (2003), Eraker et al. (2003), Eraker (2004) and Todorov and Tauchen (2011)) but is missing in GARCH models except for the model proposed by Chan and J. Maheu (2002) and J. M. Maheu and McCurdy (2004). The multivariate model combining GARCH and Stochastic Volatility by introducing a latent component is proposed by Paolella and Polak (2015) : A common market factor non-Gaussian returns model (COMFORT). |
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