Browsing by Author "Wang, Xun"
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Item A Three-Factor Mortgage Default Option Pricing Model with Applications to the Loan Modifications(2011-08-18) Wang, Xun; Wang, Xun; Gentle, James E.The classic contingent-claims pricing model views the borrower’s right to default on a mortgage as a put option. By defaulting on a mortgage the borrower effectively sells the property to the lender with the current value of the mortgage. The primary goal of this dissertation is to develop a three-factor structural default option pricing model to explain and evaluate the default options in the residential mortgage contracts. Home price, interest rate and net transaction cost are the three underlying factors of this model. Because a borrower can default at any time when a mortgage payment is due, the mortgage default option is by nature a path dependent Bermudan-American type option. Similar to the American type equity options, there is no analytical solution to the mortgage default option price. By applying the least-squares Monte Carlo (LSM) method to numerically evaluate the mortgage default option prices under different economic scenarios, this dissertation attempts to explain the borrowers’ behaviors of strategic defaulting on their mortgages. In addition, this dissertation applies the mortgage default pricing model to an important mortgage research area - loan modifications. The effectiveness of the strategic default prevention of the payment reduction modification method and the equity sharing modification method are quantitatively compared. This dissertation also proposes a flexible parametrized loan modification framework by generalizing and extending the existing modification methods.Item Detecting Threatening Behavior Using Bayesian Networks(2006-03-06T15:11:39Z) AlGhamdi, Ghazi; Laskey, Kathryn B.; Wang, Xun; Barbará, Daniel; Shackelford, Thomas; Wright, Edward J.; Fitzgerald, JulieThis paper presents an innovative use of human behavior models for detecting insider threats to information systems. While most work in information security concerns detecting and responding to intruders, violations of system security policy by authorized computer users present a major threat to information security. A promising approach to detection and response is to model behavior of normal users and threats, and apply sophisticated inference methods to detect patterns of behavior that deviate from normal behavior in ways suggesting a possible security threat. This paper presents an approach, based on multi-entity Bayesian networks, to modeling user queries and detecting situations in which users in sensitive positions may be accessing documents outside their assigned areas of responsibility. Such unusual access patterns might be characteristic of users attempting illegal activities such as disclosure of classified information. We present a scalable proof of concept behavior model, provide an experimental demonstration of its ability to detect unusual access patterns in simulated situations, and describe future plans to increase the realism and fidelity of the model.