The copula GARCH model for time varying betas in the banking sector
| dc.contributor.author | Nikolaidis, Dimitris | en |
| dc.date.accessioned | 2015-03-23T11:33:55Z | |
| dc.date.available | 2015-09-27T06:05:05Z | |
| dc.date.issued | 2015-03-23 | |
| dc.identifier.uri | https://repository.ihu.edu.gr/xmlui/handle/11544/33 | |
| dc.rights | Default License | |
| dc.title | The copula GARCH model for time varying betas in the banking sector | en |
| heal.abstract | The Copula GARCH model for time varying betas in the banking sector. Dimitris Nikolaidis 2010 Copula functions have become an increasingly popular tool in nance when the distribution of asset returns is of extreme importance. The main features of copulas are that they seperate a multivariate distribution into the dependence structure and the margins, thus allowing two step estimation procedures for the distributional parameters that minimize the computational burden and also add exibility to the distribution since the dependence governed by the copula and the margins do not have to belong to the same parametric family, unlike standard multivariate distributions. The aim of this study is twofold. In the rst part, the statistical attributes of copulas are discussed in full detail while in the second part an empirical investigation of the evolution of stock betas during the modern global nancial crisis period is conducted. In the empirical part, it is evident that copula models clearly outperform other, traditional models, in terms of both statistical validity and accuracy in risk calculations | en |
| heal.academicPublisher | School of Economics, Business Administration and Legal Studies, MSc in Banking and Finance | en |
| heal.academicPublisherID | ihu | |
| heal.access | free | el |
| heal.advisorName | Rafael, Dr.Markellos | en |
| heal.bibliographicCitation | Nikolaidis Dimitris, 2010, The copula GARCH model for time varying betas in the banking sector / by Dimitris Nikolaidis,Master's Dissertation, International Hellenic University | en |
| heal.committeeMemberName | Markellos | en |
| heal.committeeMemberName | Chalamandaris | en |
| heal.committeeMemberName | Levis | en |
| heal.fullTextAvailability | true | |
| heal.keyword | Dissertations, Academic | en |
| heal.keyword | GARCH model | en |
| heal.keyword | Copulas (Mathematical statistics) | en |
| heal.language | en | |
| heal.license | http://creativecommons.org/licenses/by-nc/4.0 | |
| heal.numberOfPages | 88 | |
| heal.publicationDate | 2010-08 | |
| heal.recordProvider | School of Economics, Business Administration and Legal Studies, MSc in Banking and Finance | |
| heal.tableOfContents | The First Part - Statistical analysis of copulas 1 1 Introduction to Copulas 2 1.1 Intuition behind copulas. Measures of dependence . . . . . . . . . . . 2 1.1.1 Rank based measures of dependence . . . . . . . . . . . . . . 5 1.2 De nitions and fundamental properties . . . . . . . . . . . . . . . . . 7 1.3 Tail dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.4 Inference on copulas . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.4.1 Maximum likelihood method . . . . . . . . . . . . . . . . . . . 20 1.4.2 Inference functions for margins (IFM) . . . . . . . . . . . . . . 23 1.4.3 The pseudo likelihood method . . . . . . . . . . . . . . . . . . 25 1.5 Goodness of t tests for copulas . . . . . . . . . . . . . . . . . . . . . 28 1.5.1 Graphical inspection method . . . . . . . . . . . . . . . . . . . 29 1.5.2 Squared radius method . . . . . . . . . . . . . . . . . . . . . . 31 1.5.3 Bivariate probability integral transform method . . . . . . . . 36 1.6 Conditional Copulas . . . . . . . . . . . . . . . . . . . . . . . . . . . 38II The second part - Empirical application 44 2 Introduction 45 2.1 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.2 Data and methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 53 2.2.1 Data and descriptive statistics . . . . . . . . . . . . . . . . . . 56 2.3 Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 2.3.1 Copula results and time varying parameters . . . . . . . . . . 63 2.3.2 Model comparison and time varying betas . . . . . . . . . . . 65 2.4 Time evolution of betas . . . . . . . . . . . . . . . . . . . . . . . . . 70 2.5 Other copula models . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 | en |
| heal.type | masterThesis |
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