Master of Science in Mathematical Science
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Stephen M. Miller
Graduate Faculty Representative
Number of Pages
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.
Autoregressive Integrated Moving Average (ARIMA); Bank Failures – Mathematical models; Big business; Business enterprises—Size; Conditional Test; ERR; ERRR; Poisson process
Applied Statistics | Banking and Finance Law | Finance and Financial Management | Multivariate Analysis | Statistical Models
Cui, Fangjin, "ARIMA models for bank failures: Prediction and comparison" (2011). UNLV Theses, Dissertations, Professional Papers, and Capstones. 1027.