<p>This paper uses logistic regression to assign risk of catastrophic loss (defined as a loss of 80% or more of market cap value) to companies, and analyzes the subsequent returns of high risk and low risk portfolios. In the final model, the low risk portfolio had a three-year mean return of approximately 47%, with a catastrophic loss rate of 1.1%. The high-risk portfolio had a three-year mean return of approximately .5%, with a catastrophic loss rate of 29%. The paper expands upon a model developed by Dr. Abhay Gaur and Dr. Leo Rebholz in Rebholz?s 2002 thesis, Bankruptcy as Cusp Catastrophe. This paper first validates the model, introduces a new variable, which examines financial momentum, and transforms the bankruptcy variable to catastrophic loss. The success of the model was viewed through a comparative approach of high and low risk portfolios.

<p> Time series models have been widely used in simulating financial data sets. Finding a nice way to estimate the parameters is really important. One of the traditional ways is to use maximum likelihood estimation to make an approach. However, when the error terms don&rsquo;t have normality, MLE would be less efficient. Quasi maximum likelihood estimation, also regarded as Gaussian MLE, would be more efficient. Considering the heavy-tailed financial data sets, we can use non-Gaussian quasi maximum likelihood, which needs less assumptions and conditions. We use real financial data sets to compare these estimators. </p>

<p> This study investigated earnings management in European firms. The private investors became victims of manipulated earnings where few laws offered regulatory oversight. The study forensically examined the attributes of earnings management identified using a discretionary accrual model published in Jones&rsquo; work and Schippers&rsquo; work. The firms&rsquo; managers should fulfil agency theory when they made reporting decisions, and they should act in the investors&rsquo; best interests to fulfil stewardship theory. The managers failed as they seemed to favor insiders when they reported manipulated earnings to outsiders like small investors even though the managers published financial reports conforming to the International Financial Reporting Standards. The investors depended on the decision usefulness of the reports. The study used the data of 432 listed firms in 11 code law nations. The paired t test identified significant differences between reported and economic earnings to find earnings management attributes and between economic and restated earnings to find earnings management cases. The research found that managers seemed to manipulate discretionary accruals to misstate earnings and reduce the decision usefulness of reporting. The data came from published financial reports and databases. The firms represented 11 nations and 9 industries that excluded banking and insurance. Almost 17% of nations and industry segments reflected earnings management attributes. About 29% of firms restated at least one annual earnings, and 84% of the restatements appeared to offset manipulation. The research results should prompt social change for small investors where regulators would redress the manipulation using stronger investor protection laws to improve the reported earnings quality and its decision usefulness.</p><p>

Thesis (Ph. D.)--University of Hong Kong, 2008. / Also available in print.

Thesis (Ph. D.)--University of California, San Diego, 2002. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Today, better numerical approximations are required for multi-dimensional SDEs to improve on the poor performance of the standard Monte Carlo integration. With this aim in mind, the material in the thesis is divided into two main categories, stochastic calculus and mathematical finance. In the former, we introduce a new scheme or discrete time approximation based on an idea of Paul Malliavin where, for some conditions, a better strong convergence order is obtained than the standard Milstein scheme without the expensive simulation of the Lévy Area. We demonstrate when the conditions of the 2−Dimensional problem permit this and give an exact solution for the orthogonal transformation (θ Scheme or Orthogonal Milstein Scheme). Our applications are focused on continuous time diffusion models for the volatility and variance with their discrete time approximations (ARV). Two theorems that measure with confidence the order of strong and weak convergence of schemes without an exact solution or expectation of the system are formally proved and tested with numerical examples. In addition, some methods for simulating the double integrals or Lévy Area in the Milstein approximation are introduced. For mathematical finance, we review evidence of non-constant volatility and consider the implications for option pricing using stochastic volatility models. A general stochastic volatility model that represents most of the stochastic volatility models that are outlined in the literature is proposed. This was necessary in order to both study and understand the option price properties. The analytic closed-form solution for a European/Digital option for both the Square Root Model and the 3/2 Model are given. We present the Multilevel Monte Carlo path simulation method which is a powerful tool for pricing exotic options. An improved/updated version of the ML-MC algorithm using multi-schemes and a non-zero starting level is introduced. To link the contents of the thesis, we present a wide variety of pricing exotic option examples where considerable computational savings are demonstrated using the new θ Scheme and the improved Multischeme Multilevel Monte Carlo method (MSL-MC). The computational cost to achieve an accuracy of O(e) is reduced from O(e−3) to O(e−2) for some applications.