Modeling Growth Stocks via Size Distribution

The inability to predict the earnings of growth stocks, such as biotechnology and internet stocks, leads to the high volatility of share prices and difficulty in applying the traditional valuation methods. This paper attempts to demonstrate that the high volatility of share prices can nevertheless be used in building a model that leads to a particular size distribution, which can then be applied to price a growth stock relative to its peers. The model focuses on both transient and steady state behavior of the market capitalization of the stock, which in turn is modeled as a birth-death process. In addition, the model gives an explanation to an empirical observation that the market capitalization of internet stocks tends to be a power function of their relative ranks.

Becoming American: The Hmong American Experience

Hmong Americans, who came from a pre-literate society and rural background, went through many acculturation barriers and have had many successes between the time they first arrived in 1975 and the year 2000. Their first decade was preoccupied with their struggle to overcome cultural shock and acculturation difficulties. The second decade is their turning point to be new Americans, beginning to run for political office, establish business enterprises, achieve in education, and reduce their high rate of unemployment and welfare participation. Hmong Americans in 2000 appeared to have achieved much, yet have some serious challenges still ahead

The Interactive Creation of 3D Objects Using Deformations - A User Based Study of Physical and Geometrical Paradigms

Submitted to the University of London for the degree of Doctor of Philosophy in Computer Scienc

Stochastic modeling in nanoscale biophysics: Subdiffusion within proteins

Advances in nanotechnology have allowed scientists to study biological processes on an unprecedented nanoscale molecule-by-molecule basis, opening the door to addressing many important biological problems. A phenomenon observed in recent nanoscale single-molecule biophysics experiments is subdiffusion, which largely departs from the classical Brownian diffusion theory. In this paper, by incorporating fractional Gaussian noise into the generalized Langevin equation, we formulate a model to describe subdiffusion. We conduct a detailed analysis of the model, including (i) a spectral analysis of the stochastic integro-differential equations introduced in the model and (ii) a microscopic derivation of the model from a system of interacting particles. In addition to its analytical tractability and clear physical underpinning, the model is capable of explaining data collected in fluorescence studies on single protein molecules. Excellent agreement between the model prediction and the single-molecule experimental data is seen.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS149 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Jump Diffusion Model for Option Pricing with Three Properties: Leptokurtic Feature, Volatility Smile, and Analytical Tractability

Brownian motion and normal distribution have been widely used, for example, in the Black-Scholes-Merton option pricing framework, to study the return of assets. However, two puzzles, emerged from many empirical investigations, have got much attention recently, namely (a) the leptokurtic feature that the return distribution of assets may have a higher peak and two (asymmetric) heavier tails than those of the normal distribution, and (b) an empirical abnormity called ``volatility smile'' in option pricing. To incorporate both the leptokurtic feature and ``volatility smile'', this paper proposes, for the purpose of studying option pricing, a jump diffusion model, in which the price of the underlying asset is modeled by two parts, a continuous part driven by Brownian motion, and a jump part with the logarithm of the jump sizes having a double exponential distribution. In addition to the above two desirable properties, leptokurtic feature and ``volatility smile'', the model is simple enough to produce analytical solutions for a variety of option pricing problems, including options, future options, and interest rate derivatives, such as caps and floors, in terms of the $Hh$ function. Although there are many models can incorporate some of the three properties (the leptokurtic feature, ``volatility smile'', and analytical tractability), the current model can incorporate all three under a unified framework.