Modeling Nonlinear Dynamics in NASDAQ Stock Returns.

This dissertation investigates the nonlinear dynamics of the returns generation process of individual stocks listed on national market system from national association of security dealers automated quotation system (NASDAQ/NMS) and compares them to a similar sample from New York Stock Exchange (NYSE). One of the most prominent tools that has emerged for characterizing nonlinear processes is the Autoregressive Conditional Heteroscedasticity (ARCH) model, and its various extensions, the most significant being the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. From the stocks listed on the Center for Research in Security Prices (CRSP) tapes, a group of NASDAQ/NMS and NYSE stocks are chosen for the analysis. Weekly data for the years 1982 to 1988 are used for this study. Various forms of existing GARCH models are applied on the same data set with conditional error distributions of normal, Student-t, power exponential and mixed jump-diffusion process. Although attempts at exploring the relative merits of the models have been made on the foreign exchange market, no such study exists for individual stock returns. The performance of each model is evaluated by several diagnostics on the respective error distributions and evaluation of log likelihood values. In a simulation study on non-nested testing GARCH-PE is found to be more flexible as compared to GARCH-T. Only 36% stocks of the given sample from each market can be modeled using GARCH. However, on forming portfolios, three out of four can be modeled using GARCH

Sarcoma botryoides in a 14 year old girl: a rare case

Embryonal rhabdomyosarcoma (Sarcoma botryoides) of the uterine cervix is an uncommon entity. Because of extreme rarity its discussion has mainly been in the light of individual case reports. We report a case of a 14-year old female who presented with irregular vaginal bleeding and cervical polyp. Her biopsy specimen confirmed sarcoma botryoides and she underwent abdominal hysterectomy with wide excision of vaginal cuff after a multidisciplinary consultation

Utilizing Reduced Graphene Oxide-Iron Nanoparticles Composite to Enhance and Accelerate the Removal of Methyl Blue Organic Dye in Wastewater

In this work, a nano-composite is used to remove dye from wastewater of different industries. For this purpose, thesynthesis of a magnetic 1:1 composite made of iron nanoparticles (NPs) using reduced graphene oxide is a novel techniqueand tested for Methyl Blue (MB) dye adsorption from aqueous solution. In this study Fe nanoparticles in reduced Graphenecomposite (FGOC) has been prepared using Graphene Oxide (GO). X-ray diffraction, FTIR spectroscopy and Ramanspectroscopy, are used to identify the structures. Many methods have been developed for MB removal in wastewater. One ofthe most popular methods is adsorption because it is simple and high-efficiency, and the adsorbent is crucial. It reached amaximum MB adsorption at pH 7. The kinetic study indicated that the adsorption of MB process was fitted well to thequasi-first-order and quasi-second-order kinetic models. The isotherm study revealed that the MB adsorption process obeyedthe Langmuir and Freundlich adsorption Isotherms models. The GO adding content and absorption conditions on the methylblue removal efficiencies were investigated. This adsorbent is easily recovered by an external magnetic field from thetreated wastewater and has high reusability