東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Stochastic modeling of retail mortga...

Liu, Chang.

Linked to FindBook

Google Book

Amazon

博客來

Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states./
Author:	Liu, Chang.
Description:	140 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4527.
Contained By:	Dissertation Abstracts International68-07B.
Subject:	Business Administration, Banking. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3270930
ISBN:	9780549111757

Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states.
Liu, Chang.

Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states. - 140 p.

Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4527.

Thesis (Ph.D.)--Louisiana Tech University, 2007.

Stochastic models were developed that provide important measures related to retail mortgages and credit cards for the management of a bank. Based on Markov theory, two models were developed that predict mortgage portfolio size and expected duration of stay in each of the states, which are defined according to the criteria of Basel Accord II and the Federal Reserve Bank. Also, to facilitate comparisons among different types of credit products and different time periods, a model was developed to generate a health index for a retail mortgage. This model could be easily extended, using multivariate regression or multivariate time series techniques, to analyze the interaction between a mortgage and local macroeconomic factors. Furthermore, the models in this dissertation address decision making on the part of the management of a bank concerning business strategy such as collection policies and loan officer compensation policies. Extending the basic assumption of the Markov property to a higher-order Markov model and a multivariate Markov model, this work also analyzed the correlation between the payment pattern for retail mortgages and credit cards. To complete this correlation analysis, a comparison among 3 models (higher-order, multivariate, and a higher-order multivariate Markov model (HMMM)) has also been provided. Finally, an interaction analysis between the payment behavior of a retail mortgage and local macroeconomic variables has been performed using an Interactive Hidden Markov Model (IHMM). For IHMM and HMMM models, the number of unknown parameters increases exponentially with the increase of the order of the models. Hence, to deal with this situation, a linear programming algorithm has been used to obtain solutions for the HMMM and IHMM.

ISBN: 9780549111757Subjects--Topical Terms:

1018458
Business Administration, Banking.

Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states.
LDR:03346nam 2200289 a 45 001 942522
005 20110519
008 110519s2007 eng d
020 $a 9780549111757
035 $a (UMI)AAI3270930
035 $a AAI3270930
040 $a UMI $c UMI
100 1 $a Liu, Chang. $3 700219
245 1 0 $a Stochastic modeling of retail mortgage loans based on past due, prepaid, and default states.
300 $a 140 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-07, Section: B, page: 4527.
502 $a Thesis (Ph.D.)--Louisiana Tech University, 2007.
520 $a Stochastic models were developed that provide important measures related to retail mortgages and credit cards for the management of a bank. Based on Markov theory, two models were developed that predict mortgage portfolio size and expected duration of stay in each of the states, which are defined according to the criteria of Basel Accord II and the Federal Reserve Bank. Also, to facilitate comparisons among different types of credit products and different time periods, a model was developed to generate a health index for a retail mortgage. This model could be easily extended, using multivariate regression or multivariate time series techniques, to analyze the interaction between a mortgage and local macroeconomic factors. Furthermore, the models in this dissertation address decision making on the part of the management of a bank concerning business strategy such as collection policies and loan officer compensation policies. Extending the basic assumption of the Markov property to a higher-order Markov model and a multivariate Markov model, this work also analyzed the correlation between the payment pattern for retail mortgages and credit cards. To complete this correlation analysis, a comparison among 3 models (higher-order, multivariate, and a higher-order multivariate Markov model (HMMM)) has also been provided. Finally, an interaction analysis between the payment behavior of a retail mortgage and local macroeconomic variables has been performed using an Interactive Hidden Markov Model (IHMM). For IHMM and HMMM models, the number of unknown parameters increases exponentially with the increase of the order of the models. Hence, to deal with this situation, a linear programming algorithm has been used to obtain solutions for the HMMM and IHMM.
520 $a The models provided in this study are of practical importance to the bank management. Not only do they give quantitative measures about loan stand-alone characteristics, but also they provide cross-section comparisons among different credit products and multi-period loan performance tracking as well. These models, used to analyze retail mortgages and credit cards, could be easily applied to other credit products issued by a commercial bank.
520 $a The data used in this study have been obtained from an Ohio local commercial bank. It includes monthly paid 20-year retail mortgages and personal credit cards. A contract has also been signed to guarantee that the data would be used only for academic research.
590 $a School code: 0109.
650 4 $a Business Administration, Banking. $3 1018458
650 4 $a Economics, Finance. $3 626650
650 4 $a Mathematics. $3 515831
690 $a 0405
690 $a 0508
690 $a 0770
710 2 $a Louisiana Tech University. $3 1018729
773 0 $t Dissertation Abstracts International $g 68-07B.
790 $a 0109
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3270930