Accuracy of mortgage portfolio risk forecasts during financial crises

Lee, Yongwoong; Rösch, Daniel; Scheule, Harald

doi:10.1016/j.ejor.2015.09.007

Cited by 14 publications

(9 citation statements)

References 29 publications

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“…In this model, we include two systematic variables: real GDP growth and mean log recovery, which are lagged by one period. Both variables have been shown to be powerful systematic control variables for default risk (see, for example, Lee et al 2016). We avoid circular references by computing ORR as 1 OLGD based on the risk-free rate.…”

Section: Assumptionsmentioning

confidence: 99%

Benchmarking loss given default discount rates

Scheule¹,

Jortzik²

2020

JRMV

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This paper provides a theoretical and empirical analysis of alternative discount rate concepts for computing loss given default (LGD) rates using historical bank workout data. It benchmarks five discount rate concepts for workout recovery cashflows in order to derive observed LGDs in terms of economic robustness and empirical implications: contract rate at origination, loan-weighted average cost of capital, return on equity (ROE), market return on defaulted debt and market equilibrium return. The paper develops guiding principles for LGD discount rates and argues that the weighted average cost of capital and market equilibrium return dominate the popular contract rate method. The empirical analysis of data provided by Global Credit Data (GCD) shows that declining risk-free rates are in part offset by increasing market risk premiums. Common empirical discount rates lie between the risk-free rate and the ROE. The variation in empirical LGDs is moderate for the various discount rate approaches. Further, a simple correction technique for resolution bias is developed and increases observed LGDs for all periods, particularly recent periods.

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Section: Assumptionsmentioning

confidence: 99%

Benchmarking loss given default discount rates

Scheule¹,

Jortzik²

2020

JRMV

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“…Mitra et al (2013) argued that as interest rates increase, banks charge more for business loans, resulting in reducing the ability of customers to buy products and services. This phenomenon can provoke price fluctuations in supply activities (Lee et al , 2016).…”

Section: Sc Risks and Research Hypothesesmentioning

confidence: 99%

Supply chain risk management: manufacturing- and service-oriented firms

Truong

Hara

2018

JMTM

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Purpose A risk, when it occurs, causes negative effects on outputs. Typically, risks are not independent as multiple risks occur simultaneously. The purpose of this paper is to compare the impact various risks have on the performance of manufacturing-oriented and service-oriented firms from a supply chain (SC) perspective. Design/methodology/approach First, SC risks were identified and classified into two criteria: context and probability. Then, the different characteristics between manufacturing- and service-oriented firms were distinguished by the theory of goods-dominant logic (GDL) and service-dominant logic (SDL). Structural equations modeling and multiple-group analysis were then used to validate research hypotheses and compare the two groups. Findings The empirical evidence, gathered from the Vietnamese construction sector, indicated that in a serious situation all of the five risks proposed occur at the same time, thus the remarkable 87.1% variance in SC performance was explained. Furthermore, this rate is significant when the two groups are compared: manufacturing-oriented firms (88.3 percent) and service-oriented firms (85.6 percent), implying that risks in the manufacturing-oriented group have a greater effect on SC performance. While manufacturing-oriented companies should pay close attention to the operational and demand risks that adversely affect SC performance, they should treat information risk as an opportunity to improve. Service-oriented companies, however, need to manage supply risk which, in their case, can be attributed to a 51.2 percent variance in SC performance. Moreover, service quality can also be improved remarkably if information risk is well managed. Research limitations/implications This study provides a detailed picture of the relationship between risks and performance in the SC. Risks are illustrated as affecting the SC performance simultaneously, (not separately) and so the approach outlined here will give firms a comprehensive view of their SCs and provide guidelines for predicting the impact risks will have on the SC performance. Moreover, by comparing manufacturing- and service-oriented firms, a thorough overview of risk behaviors is provided and appropriate solutions for each type of company can be determined. Originality/value The “novelty of approach” of this study is in applying GDL and SDL theory to classify the manufacturing-oriented and service-oriented firms. The different characteristics between the two groups are identified and explained in terms of resources, value, network, effectiveness vs efficiency and communication, thus providing an insight into risk management activities in the SC network.

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“…Age Avanzini (2015) [3]; Campbell (2015) [4]; Csizmady (2017) [5]; Gerlach-Kristen (2018) [6]; Lydon (2013) [7]; Van Ooijena (2016) [8] Credit score Elul (2010) [9]; Foote (2018) [10]; Fuster (2015) [11]; Goodman (2010) [12]; Gyourko (2014) [13]; Harrison (2011) [14]; Jones (2015) [15]; Lee (2016) [16] Debt service ratio [17]; Foote (2018) [10]; Harrison (2011) [14]; Jones (2015) [15]; Kelly (2018) [18]; Kukk (2016) [19]; Lee (2016) [16]; Lydon (2013) [7]; Van Ooijena (2016) [8] Education Avanzini (2015) [3]; Gerlach-Kristen (2018) [6]; Van Ooijena (2016) [8] Employment conditions Gyourko (2014) [13]; Harrison (2011) [14]; Jones (2015) [15]; Lydon (2013) [7]; Van Ooijena (2016) [8] Family status Csizmady (2017) [5]; Jones (2015) [15] etc.…”

Section: Borrowermentioning

confidence: 99%

“…Avanzini (2015) [3]; Campbell (2015) [4]; Elul (2010) [9]; Foote (2018) [10]; Fuster (2015) [11]; Goodman (2010) [12]; Harrison (2011) [14]; Hott (2015) [20]; Jones (2015) [15]; Kelly (2018) [18]; Lee (2016) [16]; Lydon (2013) [7]; Van Ooijena (2016) [8] Current LTV Avanzini (2015) [3]; Bian (2018) [21]; Campbell (2015) [4]; Elul (2010) [9]; Foote (2008) [22]; Foote (2018) [10]; Fuster (2015) [11]; Gerlach-Kristen (2018) [6]; Goodman (2010) [12]; Gyourko (2014) [13]; Hott (2015) [20]; Jones (2015) [15]; Kelly (2018) [18]; Lee (2016) [16]; Lydon (2013) [7]; Van Ooijena (2016) [8] Interest rate volatility Campbell (2015) [4]; Csizmady (2017) [5]; Foote (2018) [10]; Fuster (2015) [11]; Harrison (2011) …”

Section: Initial Ltvmentioning

confidence: 99%

“…GDP growth Hott (2015) [20]; Lee (2016) [16] House price index Aron (2016) [25]; Avanzini (2015) [3]; Campbell (2015) [4]; Foote (2018) [10]; Gerlach-Kristen (2018) [6]; Harrison (2011) [14]; Hott (2015) [20]; Jones (2015) [15]; Lydon (2013) [7] Income volatility Foote (2018) [10] Interest rate volatility Beckmann (2015) [23]; Campbell (2015) [4]; Fuster (2015) [11]; Goodman (2010) [12]; Harrison (2011) [14]; Hott (2015) [20]; Lee (2016) [16] Unemployment rate Aron (2016) [25]; Elul (2010) [9]; Foote (2018) [10]; Gerlach-Kristen (2018) [6]; Gyourko (2014) [13]; Hott (2015) [20]; Lydon (2013) [7] etc. Aron and Muellbauer (2016) based on mortgage data from UK analysis discovered strong correlation between arrears and aggregate debt-service ratio, the proportion of mortgages in negative equity and the unemployment rate [25].…”

Section: Macro Factorsmentioning

confidence: 99%

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Residential Mortgage Loans Delinquencies Analysis and Risk Drivers Assessment

Spilbergs

2020

Emerg Sci J

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Residential mortgage loans play an important role in improving living conditions in developed countries. In Latvia, however, residential mortgage volumes have declined throughout the post-crisis year’s and were at the end of 2018 12% below the end 2008 level, while the house price index ca. 25% below pre-crisis level. The main reasons for this are banks credit losses, which resulted in a revision of credit granting standards and a deteriorating in their availability. On the other hand, households have experienced increased uncertainty, both as a result of financial difficulties, experienced during the crisis years and political instability in the post-crisis years. It is therefore essential to identify the true risk drivers and to analyse them. Based on existing researches in other countries, the author has identified several dozen macro-economic indicators, such as the unemployment rate, wage growth, housing price index, etc. and micro factors such as the age of the borrower, total debt to income, loan-to-value, etc., have developed univariate and multivariate econometric models and have examined their statistical stability. Consequently, through a consistent application, it is possible to take sound credit decisions, both in banks and by households, and to contribute to the sustainable development of the housing market.

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Accuracy of mortgage portfolio risk forecasts during financial crises

Cited by 14 publications

References 29 publications

Benchmarking loss given default discount rates

Benchmarking loss given default discount rates

Supply chain risk management: manufacturing- and service-oriented firms

Residential Mortgage Loans Delinquencies Analysis and Risk Drivers Assessment

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