Distance-to-Default的鲁棒性

Cathrine Jessen, D. Lando
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引用次数: 68

摘要

违约距离(DD)是使用Merton(1974)的结构性信用风险模型从观察到的股票价格和账面杠杆中得出的违约风险度量。尽管简化了其推导的假设,但从经验上来看,DD已被证明是一个强有力的违约预测指标。我们使用模拟来表明,DD的经验成功很可能是其对模型错误规范的强大鲁棒性的结果。我们考虑了默顿模型的一些偏差,这些偏差涉及不同的资产价值动态和不同的违约触发机制。我们表明,一般来说,即使潜在的模型假设被改变,DD也能成功地对企业的违约概率进行排名。资产价值大幅跃升或随机波动的可能性对DD的稳健性提出了挑战。我们提出了对违约距离度量的波动率调整,该度量显着提高了具有随机波动的公司的排名,但该度量对模型错误规范的鲁棒性不如DD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robustness of Distance-to-Default
Distance-to-default (DD) is a measure of default risk derived from observed stock prices and book leverage using the structural credit risk model of Merton (1974). Despite the simplifying assumptions that underlie its derivation, DD has proven empirically to be a strong predictor of default. We use simulations to show that the empirical success of DD may well be a result of its strong robustness to model misspecifications. We consider a number of deviations from the Merton model which involve different asset value dynamics and different default triggering mechanisms. We show that, in general, DD is successful in ranking firms’ default probabilities, even if the underlying model assumptions are altered. A possibility of large jumps in asset value or stochastic volatility challenge the robustness of DD. We propose a volatility adjustment of the distance-to-default measure that significantly improves the ranking of firms with stochastic volatility, but this measure is less robust to model misspecifications than DD.
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