英文标题：
《Multi-curve HJM modelling for risk management》
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作者：
Chiara Sabelli, Michele Pioppi, Luca Sitzia and Giacomo Bormetti
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最新提交年份：
2015
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英文摘要：
We present a HJM approach to the projection of multiple yield curves developed to capture the volatility content of historical term structures for risk management purposes. Since we observe the empirical data at daily frequency and only for a finite number of time-to-maturity buckets, we propose a modelling framework which is inherently discrete. In particular, we show how to approximate the HJM continuous time description of the multi-curve dynamics by a Vector Autoregressive process of order one. The resulting dynamics lends itself to a feasible estimation of the model volatility-correlation structure and market risk-premia. Then, resorting to the Principal Component Analysis we further simplify the dynamics reducing the number of covariance components. Applying the constant volatility version of our model on a sample of curves from the Euro area, we demonstrate its forecasting ability through an out-of-sample test.
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中文摘要：
我们提出了一种HJM方法来预测多个收益率曲线，用于捕捉历史期限结构的波动性内容，以便进行风险管理。由于我们以每天的频率观察经验数据，并且只针对有限数量的到期时间桶，因此我们提出了一个固有离散的建模框架。特别地，我们展示了如何通过一阶向量自回归过程逼近多曲线动力学的HJM连续时间描述。由此产生的动态有助于对模型波动率相关性结构和市场风险溢价进行可行的估计。然后，借助主成分分析，我们进一步简化了动力学，减少了协方差分量的数量。在欧元区的曲线样本上应用我们模型的恒定波动率版本，我们通过样本外测试证明了其预测能力。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Risk Management 风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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