默认建模下参数损失的进一步研究

IF 0.3 4区 经济学 Q4 Economics, Econometrics and Finance
Phillip Li, M. Qi, Xiaofei Zhang, Xinlei Zhao
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引用次数: 19

摘要

我们对一些参数模型进行了全面的研究,这些模型被设计用于拟合给定违约损失(LGD)的异常有界和双峰分布。我们首先研究了涂抹估计器、蒙特卡罗估计器和全局调整方法,以改进处理LGD边界值问题的转换回归模型。虽然这些改进只略微提高了模型性能,但涂抹和蒙特卡罗估计器有助于降低变换回归对调整因子的敏感性。然后,我们在精细化的转换方法、五种特别适合LGD建模的参数模型(两步、膨胀beta、Tobit、删节伽玛和双层伽玛回归)、分数响应回归和标准线性回归之间进行了一场竞赛。我们发现复杂的参数模型在预测精度或排序能力、样本内、样本外或时间外都没有明显优于简单的模型。因此,对于建模者和研究人员来说,考虑到模型复杂性、计算负担、实现难易程度和模型性能的差异,选择适合其特定数据集的模型是很重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Further Investigation of Parametric Loss Given Default Modeling
We conduct a comprehensive study of some parametric models that are designed to fit the unusual bounded and bimodal distribution of loss given default (LGD). We first examine a smearing estimator, a Monte Carlo estimator and a global adjustment approach to refine transformation regression models that address issues with LGD boundary values. Although these refinements only marginally improve model performance, the smearing and Monte Carlo estimators help to reduce the sensitivity of transformation regressions to the adjustment factor. We then conduct a horse race among the refined transformation methods, five parametric models that are specifically suitable for LGD modeling (two-step, inflated beta, Tobit, censored gamma and two-tiered gamma regressions), fractional response regression and standard linear regression. We find that the sophisticated parametric models do not clearly outperform the simpler ones in either predictive accuracy or rank-ordering ability, in-sample, out-of-sample or out of time. Therefore, it is important for modelers and researchers to choose the model that is appropriate for their particular data set, considering differences in model complexity, computational burden, ease of implementation and model performance.
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来源期刊
Journal of Credit Risk
Journal of Credit Risk BUSINESS, FINANCE-
CiteScore
0.90
自引率
0.00%
发文量
10
期刊介绍: With the re-writing of the Basel accords in international banking and their ensuing application, interest in credit risk has never been greater. The Journal of Credit Risk focuses on the measurement and management of credit risk, the valuation and hedging of credit products, and aims to promote a greater understanding in the area of credit risk theory and practice. The Journal of Credit Risk considers submissions in the form of research papers and technical papers, on topics including, but not limited to: Modelling and management of portfolio credit risk Recent advances in parameterizing credit risk models: default probability estimation, copulas and credit risk correlation, recoveries and loss given default, collateral valuation, loss distributions and extreme events Pricing and hedging of credit derivatives Structured credit products and securitizations e.g. collateralized debt obligations, synthetic securitizations, credit baskets, etc. Measuring managing and hedging counterparty credit risk Credit risk transfer techniques Liquidity risk and extreme credit events Regulatory issues, such as Basel II, internal ratings systems, credit-scoring techniques and credit risk capital adequacy.
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