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Modeling Defaults In Banking & Real Estate

Xu, Jiaqing / J.X. (2016) Modeling Defaults In Banking & Real Estate. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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In this thesis, we study two topics related to defaults. First, we provide a Probability of Default (PD) calculation method for privately-held U.S. regional banks, using free and transparent data from the Federal Deposit Insurance Corporation (FDIC). Our method is efficient and useful for both investors and regulators. We have improved Moody's proprietary RiskCalc PD model [17] by creating a new cautionary index, which is able to capture default behaviors very well and has a very high predictive power over both one-year and six-month time horizons as shown by our numerical results. We also find that this performance is robust over different historical periods. We describe the factors we chose, the modeling methodology, and the model's accuracy in detail.
Second, we propose two strategies to reduce the frequency of defaults in home mortgages (foreclosures). The first is a new mortgage insurance contract (American put option with the house as the underlying asset). Our analysis differs from that for the standard put option in equity markets in that our strike (the remaining value of the mortgage) is time dependent, and the drift and volatility in the Geometric Brownian Motion are time dependent (step functions) due to a regime switch from declining to increasing house prices. Both theoretical derivations and numerical results will be obtained. We will also analyze the Adjustable Balance Mortgage (ABM) in continuous time as a second alternative to avoiding foreclosures. Here the mortgage payments are reduced if the house price falls below the remaining value of the mortgage.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Xu, Jiaqing / J.X.jix24@pitt.eduJIX24
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChadam, John / J.C.chadam@pitt.eduCHADAM
Committee MemberDoiron, Brent / B.D.bdoiron@pitt.eduBDOIRON
Committee MemberYao, Song / S.Y.songyao@pitt.eduSONGYAO
Committee MemberIyengar, Satish / S.I.ssi@pitt.eduSSI
Date: 3 October 2016
Date Type: Publication
Defense Date: 20 May 2016
Approval Date: 3 October 2016
Submission Date: 20 July 2016
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 93
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Logistic Regression, Model Selection, ROC Curve, AUC, KS Statistic, American Put Option, Integral Equation, Early Exercise Boundary, Default Probability, Adjustable Balance Mortgage
Date Deposited: 03 Oct 2016 20:48
Last Modified: 15 Nov 2016 14:34


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