AI 中文总结
该研究构建带随机不连续性的结构性信用风险模型，推导相关滤波方程，分析披露与内生违约对信用利差等的影响，通过数值实验揭示公告前信用利差动态。
AI 中文摘要
我们在不完全信息下构建了一个结构性信用风险模型，其中投资者仅通过带噪声的市场信号和定期企业披露间接观测公司价值。虽然披露日期是预先已知的，但其信息内容是随机的，导致观测过程出现随机不连续性。我们通过应用带可预测跳跃的非线性滤波框架，推导了具有内生违约的结构性信用风险模型的Kushner-Stratonovich方程。随后，我们研究了部分信息下违约敏感证券的估值和局部风险最小化对冲策略。可预测披露事件与内生违约之间的相互作用会在条件违约补偿中产生离散调整，导致信用利差和对冲比率出现公告驱动的扭曲，这是经典扩散模型和不可访问跳跃模型中不存在且无法获得的。数值实验说明了定期披露如何影响过滤后的违约概率、信用违约互换（CDS）利差以及对冲策略，进而产生信用利差中特有的公告前动态。
英文摘要
We develop a structural credit-risk model under incomplete information in which investors observe firm value only indirectly through noisy market signals and scheduled corporate disclosures. While disclosure dates are known in advance, their informational content is random, leading to stochastic discontinuities in the observation process. We derive the Kushner-Stratonovich equation for structural credit-risk models with endogenous default by applying the nonlinear filtering framework with predictable jumps. We then study the valuation and local risk-minimization hedging for default-sensitive securities under partial information. The interaction between predictable disclosure events and endogenous default produces discrete adjustments in the conditional default compensator, leading to announcement-driven distortions in credit spreads and hedge ratios that are absent from classical diffusion-based and inaccessible-jump models. Numerical experiments illustrate how scheduled disclosures affect filtered default probabilities, Credit Default Swaps (CDS) spreads, and hedging strategies, generating characteristic pre-announcement dynamics in credit spreads.
