The predictive strength of MBS yield spreads during asset bubbles
Alper Kara () and
Artur Semeyutin ()
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Alper Kara: University of Huddersfield
Artur Semeyutin: Coventry University
Review of Quantitative Finance and Accounting, 2021, vol. 56, issue 1, No 5, 142 pages
Abstract We examine whether the predictive power of initial yield spreads of mortgage-backed securities (MBS) vary with the financial cycle. Using a cross-country sample of 4203 MBS, we find that initial yield spreads of MBS incorporate more information than credit ratings and predict future downgrades, even after conditioning on initial credit ratings. Predictive power of spreads is higher during credit and housing bubbles and for the least risky AAA-rated MBS. We find that initial yield spreads capture the magnitude of rating downgrades, especially during asset bubble periods. As a novel approach in this literature, we also utilise machine learning techniques (regression trees, naïve Bayes, support vector machines and random forests) to confirm our results.
Keywords: Securitization; MBS pricing; Credit ratings; Asset bubbles; Machine learning (search for similar items in EconPapers)
JEL-codes: G21 G28 (search for similar items in EconPapers)
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