This thesis centers around the pricing and risk-return tradeoff of credit and equity derivatives. The first essay studies the pricing in the CDS Index (CDX) tranche market, and whether these instruments have been reasonably priced and integrated within the financial market generally, both before and during the financial crisis. We first design a procedure to value CDO tranches using an intensity-based model which falls into the affine model class. The CDX tranche spreads are efficiently explained by a three-factor version of this model, before and during the crisis period. We then construct tradable CDX tranche portfolios, representing the three default intensity factors. These portfolios capture the same exposure as the S&P 500 index optionmarket, to a market crash. We regress these CDX factors against the underlying index, the volatility factor, and the smirk factor, extracted from the index option returns, and against the Fama-French market, size and book-to-market factors. We finally argue that the CDX spreads are integrated in the financial market, and their issuers have not made excess returns. The second essay explores the specifications of jumps for modeling stock price dynamics and cross-sectional option prices. We exploit a long sample of about 16 years of S&P500 returns and option prices for model estimation. We explicitly impose the time-series consistency when jointly fitting the return and option series. We specify a separate jump intensity process which affords a distinct source of uncertainty and persistence level from the volatility process. Our overall conclusion is that simultaneous jumps in return and volatility are helpful in fitting the return, volatility and jump intensity time series, while time-varying jump intensities improve the cross-section fit of the option prices. In the formulation with time-varying jump intensity, both the mean jump size and standard deviation of jump size premia are strengthened. Our MCMC approach to estimate the models is appropriate, because it has been found to be powerful by other authors, and it is suitable for dealing with jumps. To the best of our knowledge, our study provides the the most comprehensive application of the MCMC technique to option pricing in affine jump-diffusion models. / published_or_final_version / Economics and Finance / Doctoral / Doctor of Philosophy

We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered.

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate.

Thesis (Ph. D.)--UCLA, 2007. / Includes bibliographical references (leaves 125-126).

From option pricing using the Black and Scholes model, to determining the signi cance of regression coe cients in a capital asset pricing model (CAPM), the assumption of normality was pervasive throughout the eld of nance. This was despite evidence that nancial returns were non-normal, skewed and heavy- tailed. In addition to non-normality, there remained questions about the e ect of rm size on returns. Studies examining these di erences were limited to ex- amining the mean return, with respect to an asset pricing model, and did not consider higher moments. Janse van Rensburg, Sharp and Friskin (in press) attempted to address both the problem of non-normality and size simultaneously. They (Janse van Rens- burg et al in press) tted a mixture of two normal distributions, with common mean but di erent variances, to a small capitalisation portfolio and a large cap- italisation portfolio. Comparison of the mixture distributions yielded valuable insight into the di erences between the small and large capitalisation portfolios' risk. Janse van Rensburg et al (in press), however, identi ed several shortcom- ings within their work. These included data problems, such as survivorship bias and the exclusion of dividends, and the questionable use of standard statistical tests in the presence of non-normality. This study sought to correct the problems noted in the paper by Janse van Rensburg et al (in press) and to expand upon their research. To this end survivorship bias was eliminated and an e ective dividend was included into the return calculations. Weekly data were used, rather than the monthly data of Janse van Rensburg et al (in press). More portfolios, over shorter holding periods, were considered. This allowed the authors to test whether Janse van Rensburg et al's (in press) ndings remained valid under conditions di erent to their original study. Inference was also based on bootstrapped statistics, in order to circumvent problems associated with non-normality. Additionally, several di erent speci cations of the normal mixture distribution were considered, as opposed to only the two-component scale mixture. In the following, Chapter 2 provided a literature review of previous studies on return distributions and size e ects. The data, data preparation and portfolio formation were discussed in Chapter 3. Chapter 4 gave an overview of the statistical methods and tests used throughout the study. The empirical results of these tests, prior to risk adjustment, were presented in Chapter 5. The impact of risk adjustment on the distribution of returns was documented in Chapter 6. The study ended, Chapter 7, with a summary of the results and suggestions for future research.

We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered. / Business, Sauder School of / Finance, Division of / Graduate

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate. / Business, Sauder School of / Finance, Division of / Graduate

Thesis (Ph. D.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains x, 146 p. : col. ill. Includes abstract. Includes bibliographical references.

Thesis (M.Ec.)--University of Adelaide, School of Economics, 1998. / "August 1998" Bibliography: leaves 74-78.

Project (M.B.A.) - Simon Fraser University, 2004. / Theses (Faculty of Business Administration) / Simon Fraser University. MBA-GAWM Program. Senior supervisor: Dr. Robert R. Grauer.