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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

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This research project endeavors to frame a methodology that can be used to categorize firm value strategies (production logics) and choices of factor inputs (tangible and intangible assets), which are used to fuel production cycles for goods and services outputs. A secondary goal of the research is to attempt to determine what asset group combinations (resources) are combined by various classes of firms to produce sustainable outcomes for the A/E/C firms in the survey. The National Bureau of Economic Research recently issued a system of national accounts (acknowledging both tangible and intangible assets) that reflects the macro-economy but at the same juncture, lamented the fact that a firm-level micro-economic schema did not exist to mirror the national system. This study makes an effort to redress that void by investigating how such a system of accounts - measured on the input side of the ledger -- could begin to fill in a gap in information and understanding as pointed out by participants in the National Academy of Sciences symposium of 2009 entitled "Intangible Assets: Measuring and Enhancing Their Contribution to Corporate Value and Growth." In brief, the research represents an effort to make a contribution to a growing body of knowledge about intangible assets by solidifying a framework within which both tangible and intangible assets may be more appropriately conceptualized and more adequately measured for purposes of current and future investigations. The research also provides a methodology for beginning to understand how some design and construction industry firms rely on specific asset categories for operating success, corporate stock value and business continuity. It is conceivable that managers would use a variation of the methodology to better balance ongoing investments in their firm's portfolio of tangible and intangible resources. The mixed methods used in this research support the following conclusions: 1) In terms of rank order of asset deployment categories by firms, intangible assets appear to have a modest edge over tangible assets for deployment by value shop firms (architecctural and engineering design firms), but these emphases are not consistent among value chain-oriented (construction) firms. 2) Although pronounced differences were expected, there was little evidence of differences in rank order of asset category accumulation and deployment by firms (according to the Delphi panel) regardless of whether the firm was focused on continuity and longevity or (alternatively) short-term profit maximization. 3) Because of their ambidexterity in production logic, the expert panel had difficulty placing EPC (Engineer - Procure - Construct), design-build and integrated services firms in a single Stabell - Fjeldstad value logic category, and a new composite category was posited based on Delphi panel feedback.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 124-130).

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 171-177).

This research examines the issues relating to evaluation of information systems investments made to facilitate engineering asset management. It follows an interpretive epistemology and suggests that information systems are social systems. An attempt to evaluate them, therefore, has strong contextual and social underpinnings. This research aims to develop understanding of the information systems based asset management, and provides conceptual frameworks for their evaluation in the social context of the organisation. / This research is motivated by the lack of appropiate theoretical frameworks for IS evaluation that could be applied to asset management paradigm. IS utilised for asset management are essentially social systems. Any attempt to evaluate IS should, therefore, be aimed at understanding the context within which they are deployed, as well as the processes that influence and are influenced by their use. In contrast to the traditional deterministic approaches to technology investment for asset management, this research adopts a multi-perspective context based approach. This research thus highlights the operational and conceptual issues posed to IS for engineering asset management evaluation. / The three case studies carried out in this research provide an analysis of the factors that contribute to the inability of asset managing organisations to implement, or make effective use of IS based asset management evaluation mechanisms. From these analyses this research highlights the factors that contribute to failure of asset managing organisations to effectively measure the contribution of IS in providing value for asset lifecycle management. The learnings gained from empirical research are further provide understandings of evaluation roles and responsibilities, integration of evaluation in organisational culture as a core strategic process, and continuous improvement of asset lifecycle strategies. This research then develops a conceptual framework for information systems based asset management evaluation that enables action able generative learning. / Thesis (PhD)--University of South Australia, 2007.