The focus of this dissertation is the European Credit Default Swaps (CDSs) market. CDSs are the most popular credit derivative products. Three issues are discussed, the first, which is covered in chapter 2, is the investigation of non-diversifiable jump risk in iTraxx sector indices based on a multivariate model that explicitly admits discrete common jumps for an index and its components. Our empirical research shows that both the iTraxx Non-Financials and their components experience jumps during the sample period, which means that the jump risks in the iTraxx sector index are not diversifiable. The second issue, which is covered in chapter 3 is the component structure of credit default swap spreads and their determinants. We firstly extract a transitory component and a persistent component from two different maturities of the Markit iTraxx index and then regress these components against proxies for several commonly used explanatory variables. Our results show that these explanatory variables have significant but differing impacts on the extracted components, which indicates that a two-factor formulation may be needed to model CDS options. The last issue, which is covered in chapters 4, 5 and 6 is the investigation of the linkage between the credit default swap market and the equity market within the European area. We innovatively calibrate the CDS option with the Heston Model to get the implied volatility in the CDS market, which allows us to investigate both the characteristic of implied volatility in the CDS market and the relationship of the two markets not only on the level of daily changes but also with regard to its second moment. Our analysis shows that the stock market weakly leads the CDS market on daily changes but for implied volatility, the stock market leads the CDS market. A VECM analysis shows that only the stock market contributes to price discovery. For sub-investment grade entities, the interactivities between the implied volatility of the CDS market and the implied volatility of the stock market are stronger, especially during the recent credit crunch period. All these results have important implications for the construction of portfolios with credit-sensitive instruments.

The Basel Committee has released a consultative document (Basel (2003)) on the management and supervision of interest rate risk (IRR). This document outlines a standardised model to calculate a duration-based proxy for IRR in depository institution balance sheets. We utilise this methodology to define an IRR measure which we denote BIRRM (Basel Interest Rate Risk Measure). It is the change in the value of a financial institution produced by a 200 basis-point increase in interest rates at all maturities, relative to Tier I and Tier II capital. This study has three primary objectives. Firstly, we utilise BIRRM to provide an overview of IRR exposure of Australian Credit Unions and Building Societies (CUBS) over the period September 1997 to September 2007. Secondly, we seek an understanding of the relationship between BIRRM and measures of CUBS' interest rate sensitivity over a period of rising interest rates (December 1998 to September 2000) and another period of falling rates (September 2000 to December 2001). Finally, we seek an understanding of the economic factors that influence IRR exposure decisions of CUBS by modelling the determinants of CUBS' IRR exposure. We find that IRR exposure of CUBS is relatively low and, on average, CUBS are exposed to falling interest rates. We also find significant relationships between BIRRM and measures of CUBS' interest rates sensitivity consistent with a priori expectations, supporting the use of the Basel Committee's measure of IRR in identifying CUBS with large IRR exposures. The models examining the determinants of CUBS' IRR have relatively low explanatory power. There are however significant relationships between a number of factors and CUBS' exposure to changing rates.

Bibliography: leaves 355-365. / ix, 365 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Economics, 1996

Most lending institutions are aware of the importance of having a well-performing credit scoring model or scorecard and know that, in order to remain competitive in the credit industry, it is necessary to continuously improve their scorecards. This is because better scorecards result in substantial monetary savings that can be stated in terms of millions of dollars. Thus, there has been increasing interest in the application of new classifiers in credit scoring from both practitioners and researchers in the last few decades. Most of the recent work in this field has focused on the use of new and innovative techniques to classify applicants as either 'credit-worthy' or 'non-credit-worthy', with the aim of improving scorecard performance. In this thesis, we investigate the suitability of intelligent systems techniques for credit scoring. In particular, intelligent systems that use immunological metaphors are examined and used to build a learning and evolutionary classification algorithm. Our model, named Simple Artificial Immune System (SAIS), is based on the concepts of the natural immune system. The model uses applicants' credit details to classify them as either 'credit-worthy' or 'non-credit-worthy'. As part of the model development, we also investigate several techniques for selecting variables from the applicants' credit details. Variable selection is important as choosing the best set of variables can have a significant effect on the performance of scorecards. Interestingly, our results demonstrate that the traditional stepwise regression variable selection technique seems to perform better than many of the more recent techniques. A further contribution offered by this thesis is a detailed description of the scorecard development process. A detailed explanation of this process is not readily available in the literature and our description of the process is based on our own experiences and discussions with industry credit risk practitioners. We evaluate our model using both publicly available datasets as well as a very large set of real-world consumer credit scoring data obtained from a leading Australian bank. The evaluation results reveal that SAIS is a competitive classifier and is appropriate for developing scorecards which require a class decision as an outcome. Another conclusion reached is one confirmed by the existing literature, that even though more sophisticated scorecard development techniques, including SAIS, perform well compared to the traditional statistical methods, their performances are not statistically significantly different from the statistical methods. As with other intelligent systems techniques, SAIS is not explicitly designed to develop practical scorecards which require the generation of a score that represents the degree of confidence that an applicant will belong to a particular group. However, it is comparable to other intelligent systems techniques which are outperformed by statistical techniques for generating p ractical scorecards. Our final remark on this research is that even though SAIS does not seem to be quite suitable for developing practical scorecards, we still believe that there is room for improvement and that the natural immune system of the body has a number of avenues yet to be explored which could assist with the development of practical scorecards.

Thesis (doctoral)--Universität Dortmund, 1987. / Includes bibliographical references (p. 137-144).

Diss. Stockholm : Handelshögskolan, 2006.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 64-68) Also available in print.

Thesis (Ph.D.). / Written for the Desautels Faculty of Management. Title from title page of PDF (viewed 2008/01/12). Includes bibliographical references.

Thesis (doctoral)--Universität Hamburg, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--Michigan State University. Dept. of Accounting and Information Systems, 2008. / Title from PDF t.p. (viewed on Mar. 26, 2009). Includes bibliographical references (p. 62-71). Also issued in print.