We develop a Monte Carlo procedure to project MSA-level house-price paths from 2013 to 2023. These price paths are applied to a fixed portfolio of synthetic mortgages in order to estimate credit risk spreads (CRS) for each MSA. Like the well-known annual percentage rate (APR)-which converts an array of fees into an all-encompassing annual measure of costs to borrowers-the CRS is a holistic measure that encompasses both expected losses from default plus the cost of capital (or unexpected credit losses) needed to cover losses in a stress scenario. We find variation in the CRS across MSAs, with the range spanning 37 basis points. This range spans 86 basis points for those carrying first-loss positions, such as private mortgage insurers. We conclude that, in order to accurately price credit risk, it is necessary to monitor more than borrower characteristics, but also local economic conditions.
机构:
Purdue Univ, Dept Stat, 150 N Univ St, W Lafayette, IN 47907 USA
Purdue Univ, Dept Math, 150 N Univ St, W Lafayette, IN 47907 USAPurdue Univ, Dept Stat, 150 N Univ St, W Lafayette, IN 47907 USA
Liu, Haibo
Tang, Qihe
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UNSW Sydney, Sch Risk & Actuarial Studies, UNSW Business Sch, Sydney, NSW 2052, AustraliaPurdue Univ, Dept Stat, 150 N Univ St, W Lafayette, IN 47907 USA
机构:
Shandong Normal Univ, Business Sch, Jinan 250358, Shandong, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250358, Shandong, Peoples R China
Dai, Haiyan
Dong, Xueqin
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Sichuan Univ, Jinjiang Coll, Sch Digital Econ, Meishan 620860, Sichuan, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250358, Shandong, Peoples R China
Dong, Xueqin
Xue, Fang
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Qingdao City Univ, Sch Management, Qingdao 266000, Shandong, Peoples R ChinaShandong Normal Univ, Business Sch, Jinan 250358, Shandong, Peoples R China