Modeling a Distribution of Mortgage Credit Losses

Social Science Research Network, 2017

This paper provides clear cut evidence that economic recession and distressed financial conditions, as well as political instability constitute the key factors for mortgage default. Banning foreclosure procedures, often adopted by governments to mitigate the effects of the above conditions on loan defaulting, are found to positively influence the loan default probability, and thus they make efforts of banks to restructure (or refinance) mortgage loans a difficult task. Our results add support to the view that foreclosure moratorium may raise moral hazard incentives that borrowers will not maintain their payments in long run. The empirical analysis of the paper is based on an extension of the discrete-time survival analysis model which allows for a structural break in its baseline hazard function and a unique set of individual loan accounts. We also consider alternative specifications of the binary link function between default events and covariates. Asymmetric link functions are found to be more appropriate under financial distressed conditions.

Journal of Banking & Finance, 2000

The new BIS 1998 capital requirements for market risks allows banks to use internal models to assess regulatory capital related to both general market risk and credit risk for their trading book. This paper reviews the current proposed industry sponsored Credit Value-at-Risk methodologies. First, the credit migration approach, as proposed by JP Morgan with CreditMetrics, is based on the probability of moving from one credit quality to another, including default, within a given time horizon. Second, the option pricing, or structural approach, as initiated by KMV and which is based on the asset value model originally proposed by Merton (Merton, R., 1974. Journal of Finance 28, 449±470). In this model the default process is endogenous, and relates to the capital structure of the ®rm. Default occurs when the value of the ®rmÕs assets falls below some critical level. Third, the actuarial approach as proposed by Credit Suisse Financial Products (CSFP) with CreditRisk+ and which only focuses on default. Default for individual bonds or loans is assumed to follow an exogenous Poisson process. Finally, McKinsey proposes CreditPortfolioView which is a discrete time multi-period model where default probabilities are conditional on the macro-variables like unemployment, the level of interest rates, the growth rate in the economy, F F F which to a large extent drive the credit cycle in the economy.

The Basel II Accord offers banks the opportunity to estimate Loss Given Default (LGD) if they wish to calculate their own value for the capital required to cover credit losses in extreme circumstances. This paper will analyze the various methods of modeling LGD and will provide an alternative estimate of LGD using Merton's model for the valuation of assets. Four components will be developed in this document: estimation of the minimum value that could have a financial asset, estimation of the loss given default LGD, development of a practical component, and finally validation of the proposed model. JEL classification numbers: G17, G24, G32

2011

This document outlines the underlying research, model characteristics, data, and validation results for Mortgage Portfolio Analyzer, which is an analytic tool to assess credit risk measures, capital levels and stress scenarios for portfolios of residential mortgages. Mortgage Portfolio Analyzer comprises loan-level econometric models for default, prepayment, and severity. These models are integrated through common dependence on local macro-economic factors, which can be either simulated at national, state, and Metropolitan Statistical Area (MSA) levels or input in the form of stress scenarios. This integration produces correlation in behaviors of loans across the portfolio. The simulation incorporates a multi-step Monte Carlo approach and generates monthly P&I cash flows and losses which enables the model to be used for ALM applications or to be combined with an external cash flow waterfall tool and used for simulation of RMBS transactions. Scenario and stress testing is also done i...

SSRN Electronic Journal, 2000

The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate.

SSRN Electronic Journal, 2007

This paper presents a simple version of the application of option based pricing models to mortgage credit risk. The approach is based on the notion that default can be viewed as exercising a put option, and that the place to look in modelling default is the extent to which the option is in the money (the extent to which the borrower has negative equity in the property) and, given that, the incentive, e.g., a trigger event and inability to withstand it, to exercise the option. The main focus is on how the probability of default can be estimated and how the default risk can be priced. The analysis considers both "first principles" and specific analysis about U. S. default experience.

This paper presents an analysis and default risk modeling on the non-performing loans of an emerging mortgage market. The analysis and the model, unprecedented for the market under study, utilize a large data set over several years with twenty-six variables that are contained in almost a hundred thousand records about the mortgage loan borrowers. The descriptive part of the analyses shows a statistical summary of all the available information on loans, defaults and loss exposures. The structure of the relation between the loan defaults and the borrower features is analyzed in detail with regression and logistic regression models. The exact and explicit probability distributions are derived for the default counts. Then, a compound Binomial distribution model is presented for the loss amounts arising from default events. Upon the obtained probability distributions, policy implications are discussed for the default risk management purposes.

Business Perspectives

Review of the literature on credit risk modeling: development of the past 10 years Absract This paper traces the developments of credit risk modeling in the past 10 years. Our work can be divided into two parts: selecting articles and summarizing results. On the one hand, by constructing an ordered logit model on historical Journal of Economic Literature (JEL) codes of articles about credit risk modeling, we sort out articles which are the most related to our topic. The result indicates that the JEL codes have become the standard to classify researches in credit risk modeling. On the other hand, comparing with the classical review Altman and Saunders (1998), we observe some important changes of research methods of credit risk. The main finding is that current focuses on credit risk modeling have moved from static individual-level models to dynamic portfolio models.

The recent financial crisis raised awareness of the need for a framework for conducting macroprudential policy. Identifying as early as possible and addressing the buildup of endogenous imbalances, exogenous shocks, and contagion from financial markets, market infrastructures, and financial institutions are key elements of a sound macroprudential framework. This paper contributes to this literature by estimating several models of default probability, two of which relax two key assumptions of the Merton model: the assumption of constant asset volatility and the assumption of a single debt maturity. The study uses market and banks' balance sheet data. It finds that systemic risk in Luxembourg banks, while mildly correlated with that of European banking groups, did not increase as dramatically as it did for the European banking groups during the heights of the financial crisis. In addition, it finds that systemic risk has declined during the second half of 2010, both for the banking groups as well as for the Luxembourg banks. Finally, this study illustrates how models of default probability can be used for event-study purposes, for simulation exercises, and for ranking default probabilities during a period of distress according to banks' business lines. As such, this study is a stepping stone toward developing an operational framework to produce quantitative judgments on systemic risk and financial stability in Luxembourg.

Banks and Bank Systems, 2010

Review of the literature on credit risk modeling: development of the past 10 years Absract This paper traces the developments of credit risk modeling in the past 10 years. Our work can be divided into two parts: selecting articles and summarizing results. On the one hand, by constructing an ordered logit model on historical Journal of Economic Literature (JEL) codes of articles about credit risk modeling, we sort out articles which are the most related to our topic. The result indicates that the JEL codes have become the standard to classify researches in credit risk modeling. On the other hand, comparing with the classical review Altman and Saunders (1998), we observe some important changes of research methods of credit risk. The main finding is that current focuses on credit risk modeling have moved from static individual-level models to dynamic portfolio models.