建立和测试产险收益曲线生成器

G. Venter, Kailan Shang
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引用次数: 0

摘要

利率风险是影响财险公司资本的关键因素。财险公司的杠杆率往往很高,其债券持有量远高于资本持有量。对于GAAP资本,债券是按市值计价的,而负债不是,因此收益率曲线的变化会对资本产生重大影响。收益率曲线情景生成器是量化这种风险的一种方法。它们产生了许多未来收益率曲线的模拟演变,可以用来量化债券价值变化的概率,这些变化将由各种期限组合策略引起。其中一些生成器以黑盒模型的形式提供,用户只能得到预测的场景。本文的一个重点是通过比较收益率曲线的已知分布特性,提供测试这些模型生成情景的方法。财险保险公司持有债券直至到期,并通过匹配资产和负债流动来管理现金流风险。在相关的时间框架内,衍生品定价和随机波动率几乎不受关注。这需要不同于广泛金融市场中常见的模型和模型测试。更复杂的是,过去十年的利率并没有遵循二战后60年建立的模式。我们现在正在走出极低利率的时期,但仍未回到此前被认为是正常的水平。随着新模式的出现,建模和模型测试处于不断发展的状态。我们的分析首先回顾了利率模型测试的文献,重点关注损益损失,并对当前市场行为的测试进行了更新。然后我们讨论模型,并用它们来说明拟合和测试方法。测试讨论不需要模型构建部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building and Testing Yield Curve Generators for P&C Insurance
Interest-rate risk is a key factor for property-casualty insurer capital. P&C companies tend to be highly leveraged, with bond holdings much greater than capital. For GAAP capital, bonds are marked to market but liabilities are not, so shifts in the yield curve can have a significant impact on capital. Yield-curve scenario generators are one approach to quantifying this risk. They produce many future simulated evolutions of the yield curve, which can be used to quantify the probabilities of bond-value changes that would result from various maturity-mix strategies. Some of these generators are provided as black-box models where the user gets only the projected scenarios. One focus of this paper is to provide methods for testing generated scenarios from such models by comparing to known distributional properties of yield curves. P&C insurers hold bonds to maturity and manage cash-flow risk by matching asset and liability flows. Derivative pricing and stochastic volatility are of little concern over the relevant time frames. This requires different models and model testing than what is common in the broader financial markets. To complicate things further, interest rates for the last decade have not been following the patterns established in the sixty years following WWII. We are now coming out of the period of very low rates, yet are still not returning to what had been thought of as normal before that. Modeling and model testing are in an evolving state while new patterns emerge. Our analysis starts with a review of the literature on interest-rate model testing, with a P&C focus, and an update of the tests for current market behavior. We then discuss models, and use them to illustrate the fitting and testing methods. The testing discussion does not require the model-building section.
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