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IDENTIFIYING GROUPS WHO DEAL IN OVER-THE-COUNTER DERIVATIVES: HOW SPECULATION LEADS TO FINANCIAL TURMOIL

Description

Since 1994, the Performance Based Studies Research Group at Arizona State University has utilized an approach to industry called Best Value (BV). Since its origin, this approach has been used in 1860 tests creating $6.4 billion dollars of projects and services delivered, at a customer satisfaction rating of 95%. Best Value (BV) is rooted in simplicity, and seeks to help organizations hire experts, plan ahead, minimize risk, optimize resources, and optimize resources. This is accomplished largely through the use of a tool the PBSRG calls the Kashiwagi Solution Model (KSM). Kashiwagi Solution Models can be used across every industry from construction to Wall Street to help achieve sustainable success in what is perhaps the most efficient and effective manner available today. Using Best Value (BV) and the Kashiwagi Solution Model (KSM), the author identified groups on Wall Street and throughout the world who deal in a unique entity called "Over-The-Counter (OTC) Derivatives". More specifically, this paper focuses on the current status and ramifications of derivative contracts that two parties enter with the sole intention of speculating. KSMs are used in Information Measurement Theory, which seeks to take seemingly complex subjects and simplify them into terms that everyone can understand. This document uses Information Measurement Theory to explain what OTC derivatives are in the simplest possible way, so that little prior knowledge of finance is required to understand the material. Through research and observation, KSMs can be used to identify the characteristics of groups who deal in OTC derivatives, which contributed to the financial crisis in 2008 and have grown in size and complexity. This document uses dominant information in order to see the potential problems within the OTC derivatives market from 30,000 feet, and offer solutions to those problems. Keywords: simplicity, best value approach, identify characteristics, dominant information

ContributorsBills, Andrew Marius (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Rivera, Alfredo (Committee member) / Department of Finance (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)

Created2016-05

Accurate State Estimation From Uncertain Data and Models: An Application of Data Assimilation to Mathematical Models of Human Brain Tumors

Description

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.

ContributorsKostelich, Eric (Author) / Kuang, Yang (Author) / McDaniel, Joshua (Author) / Moore, Nina Z. (Author) / Martirosyan, Nikolay L. (Author) / Preul, Mark C. (Author) / College of Liberal Arts and Sciences (Contributor)

Created2011-12-21

Modeling Brain Tumors: Simulating individual Patient Cases of Glioblastoma Multiforme

Description

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.

ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)

Created2012-12