英文标题：
《A sparse grid approach to balance sheet risk measurement》
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作者：
Cyril B\\\'en\\\'ezet, J\\\'er\\\'emie Bonnefoy, Jean-Fran\\c{c}ois
Chassagneux, Shuoqing Deng, Camilo Garcia Trillos, Lionel Len\\^otre
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最新提交年份：
2018
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英文摘要：
In this work, we present a numerical method based on a sparse grid approximation to compute the loss distribution of the balance sheet of a financial or an insurance company. We first describe, in a stylised way, the assets and liabilities dynamics that are used for the numerical estimation of the balance sheet distribution. For the pricing and hedging model, we chose a classical Black & Scholes model with a stochastic interest rate following a Hull & White model. The risk management model describing the evolution of the parameters of the pricing and hedging model is a Gaussian model. The new numerical method is compared with the traditional nested simulation approach. We review the convergence of both methods to estimate the risk indicators under consideration. Finally, we provide numerical results showing that the sparse grid approach is extremely competitive for models with moderate dimension.
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中文摘要：
在这项工作中，我们提出了一种基于稀疏网格近似的数值方法来计算金融或保险公司资产负债表的损失分布。我们首先以一种风格化的方式描述用于资产负债表分布数值估计的资产和负债动态。对于定价和套期保值模型，我们选择了一个经典的Black&Scholes模型，随机利率遵循Hull&White模型。描述定价和对冲模型参数演变的风险管理模型是高斯模型。将新的数值方法与传统的嵌套模拟方法进行了比较。我们回顾了两种方法的收敛性，以估计所考虑的风险指标。最后，我们提供的数值结果表明，稀疏网格方法对于中等维数的模型非常有竞争力。
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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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