Predicting recovery rates using logistic quantile regression with bounded outcomes

DOI10.1080/14697688.2015.1059952zbMath1469.62238OpenAlexW2276539226MaRDI QIDQ5001171

Ruey-Ching Hwang, Chih-Kang Chu, Jhao-Siang Siao

Publication date: 16 July 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2015.1059952

zbMATH Keywords

recovery rateinverse Gaussian regressionexpanding rolling window approachlogistic quantile regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Generalized linear models (logistic models) (62J12)

Related Items (3)

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Uses Software

• CAViaR

Cites Work

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• Quantile regression for modelling distributions of profit and loss
• Noncrossing quantile regression curve estimation
• Linear Programming Techniques for Regression Analysis
• Least Absolute Deviations Curve-Fitting
• The Minimum Sum of Absolute Errors Regression: A State of the Art Survey
• Regression Quantiles
• Microeconometrics

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