Award Date

5-2011

Degree Type

Thesis

Degree Name

Master of Science in Mathematical Science

Department

Mathematical Sciences

First Committee Member

Chih-Hsiang Ho

Second Committee Member

Kaushik Ghosh

Third Committee Member

Amei Amei

Fourth Committee Member

Stephen M. Miller

Graduate Faculty Representative

Nasser Daneshvary

Number of Pages

67

Abstract

The number of bank failures has increased dramatically over the last twenty-two years. A common notion in economics is that some banks can become "too big to fail." Is this still a true statement? What is the relationship, if any, between bank sizes and bank failures? In this thesis, the proposed modeling techniques are applied to real bank failure data from the FDIC. In particular, quarterly data from 1989:Q1 to 2010:Q4 are used in the data analysis, which includes three major parts: 1) pairwise bank failure rate comparisons using the conditional test (Przyborowski and Wilenski, 1940); 2) development of the empirical recurrence rate (Ho, 2008) and the empirical recurrence rates ratio time series; and 3) the Autoregressive Integrated Moving Average (ARIMA) model selection, validation, and forecasting for the bank failures classified by the total assets.

Keywords

Autoregressive Integrated Moving Average (ARIMA); Bank Failures – Mathematical models; Big business; Business enterprises—Size; Conditional Test; ERR; ERRR; Poisson process

Disciplines

Applied Statistics | Banking and Finance Law | Finance and Financial Management | Multivariate Analysis | Statistical Models

Language

English