2011
DOI: 10.1016/j.jue.2011.05.001
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Subprime mortgage default

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Cited by 17 publications
(3 citation statements)
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“…Irish residential mortgage market with the U.S. subprime mortgage crises, Danis and Pennington-Cross (2008) applied a multinomial logit model and discussed the reasons for mortgage delinquencies in a certain geographical location at several borrower age classes and several housing market factors, Mian and Sufi (2009) investigated the U.S. mortgage default crisis as a consequence of the expanding size of the mortgage loans at the time, Qi and Yang (2009) presented a study on the loss-given-default measure for a large set of residential mortgage loan data that involve high loan-to-value ratios, Tsai, Liao, and Chiang (2009) presented an analysis on the yield, duration and convexity features of the mortgages that are risky due to the prepayment and ability-to-payment problems, Ali and Daly (2010) investigated an evidence on the macroeconomic determinants of credit risks in a cross country study, Bastos (2010) investigated the bank loans loss-given-default amounts and conducted a forecasting modeling, and Piskorski and Tchistyi (2010) proposed a theoretical risk minimizing optimal design for the mortgage loan businesses in general. In the following decade, Van Order (2011) andKau, Keenan, Lyubimov, andSlawson (2011) performed retrospective studies on the subprime mortgage crisis era from the default risk perspectives of economic conditions and market attitudes, Goodman and Smith (2010) and Lin, Lee, and Chen (2011) explored some demographic characteristics, country-specific legal frameworks, contract contents and collateral characteristics for residential mortgage defaults at loan levels, and Demyanyk and Hemert (2011) investigated the rise of the housing prices and the uncontrolled rise of mortgage defaults and presented the relation of these factors with the subprime crisis of year 2007. Lately, Magri and Pico (2011) gave the results of an investigation on a country case and discussed the relation between mortgage interest rates and rising risk-based pricing for the mortgage products, Brueckner, Calem, and Nakamura (2012) expressed that there is a strict link between the house price expectations of mortgage lenders and the extent of subprime mortgages, and Lin, Prather, Chu, and Tsat (2013) considered the differential default risks among the traditional and non-traditional mortgage products and presented a model to quantify the credit risks and to design the mortgage products with protective capital adequacy standards.…”
Section: Predictive Modeling and Expectable Loss Analysis 233mentioning
confidence: 99%
“…Irish residential mortgage market with the U.S. subprime mortgage crises, Danis and Pennington-Cross (2008) applied a multinomial logit model and discussed the reasons for mortgage delinquencies in a certain geographical location at several borrower age classes and several housing market factors, Mian and Sufi (2009) investigated the U.S. mortgage default crisis as a consequence of the expanding size of the mortgage loans at the time, Qi and Yang (2009) presented a study on the loss-given-default measure for a large set of residential mortgage loan data that involve high loan-to-value ratios, Tsai, Liao, and Chiang (2009) presented an analysis on the yield, duration and convexity features of the mortgages that are risky due to the prepayment and ability-to-payment problems, Ali and Daly (2010) investigated an evidence on the macroeconomic determinants of credit risks in a cross country study, Bastos (2010) investigated the bank loans loss-given-default amounts and conducted a forecasting modeling, and Piskorski and Tchistyi (2010) proposed a theoretical risk minimizing optimal design for the mortgage loan businesses in general. In the following decade, Van Order (2011) andKau, Keenan, Lyubimov, andSlawson (2011) performed retrospective studies on the subprime mortgage crisis era from the default risk perspectives of economic conditions and market attitudes, Goodman and Smith (2010) and Lin, Lee, and Chen (2011) explored some demographic characteristics, country-specific legal frameworks, contract contents and collateral characteristics for residential mortgage defaults at loan levels, and Demyanyk and Hemert (2011) investigated the rise of the housing prices and the uncontrolled rise of mortgage defaults and presented the relation of these factors with the subprime crisis of year 2007. Lately, Magri and Pico (2011) gave the results of an investigation on a country case and discussed the relation between mortgage interest rates and rising risk-based pricing for the mortgage products, Brueckner, Calem, and Nakamura (2012) expressed that there is a strict link between the house price expectations of mortgage lenders and the extent of subprime mortgages, and Lin, Prather, Chu, and Tsat (2013) considered the differential default risks among the traditional and non-traditional mortgage products and presented a model to quantify the credit risks and to design the mortgage products with protective capital adequacy standards.…”
Section: Predictive Modeling and Expectable Loss Analysis 233mentioning
confidence: 99%
“…W. Ambrose et al 1997) and subprime mortgages (Kau et al 2011;deRitis et al 2010;Danis and PenningtonCross 2008) prior to the housing crisis. However, from the 1990s through the mid-2000s, mortgage underwriting standards declined substantially, resulting in an unprecedented national wave of default and foreclosure when house prices subsequently fell and economic conditions deteriorated (Demyanyk and Van Hemert 2011;Haughwout et al 2008;Mian and Sufi 2009).…”
Section: Previous Literaturementioning
confidence: 99%
“…Using various data sources there is an emerging consensus that the subprime loans steadily grew from 2001 onwards and showed a sharp growth in the years 2004 -2006 followed by a large wave of defaults beginning in 2007 now known as subprime crisis (Mayer and Pence 2008;Demiyanyk 2009;Demiyanyk and Hemert 2011;Cross and Giang 2010;Kau et. al.…”
Section: Data and Sample Descriptionmentioning
confidence: 99%