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首页> 外文期刊>International journal of theoretical and applied finance >ALGORITHMIC COUNTERPARTY CREDIT EXPOSURE FOR MULTI-ASSET BERMUDAN OPTIONS
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ALGORITHMIC COUNTERPARTY CREDIT EXPOSURE FOR MULTI-ASSET BERMUDAN OPTIONS

机译：多资产百慕大期权的算法对等信贷额度

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摘要

For an efficient computation of the counterparty credit exposure profiles of the multiasset options, a simulation-based method, named the Stochastic Grid Bundling Method (SGBM), is applied. The method is based on a 'regression later' technique used for the conditional expectation approximation and a bundling (or 'binning') technique used for state space partitioning. In the case of high-dimensional underlying asset processes, by using the bundling technique, the accuracy of exposure profiles is improved significantly, and the computation speed is reasonably fast. A detailed analysis for the bundling technique and regression approximation technique used in SGBM is given via various numerical examples. We provide an efficiency comparison of SGBM, the Standard Regression Method (SRM), and the Standard Regression Bundling Method (SRBM). We also show that for discontinuous payoffs, such as digital options, by using the bundling technique appropriately, SGBM can get accurate and stable results of option prices and exposure profiles. Compared with the benchmark results of one-dimensional European and Bermudan options, the SGBM has high accuracy in the computation of exposure profiles. The efficient calculation of the expected exposure (EE) by using SGBM forms the basis of the credit value adjustment (CVA) for multi-asset portfolios.
机译：为了有效地计算多资产选项的对手方信用风险承担状况，使用了一种基于模拟的方法，即随机网格捆绑法（SGBM）。该方法基于用于条件期望近似的“稍后回归”技术和用于状态空间划分的捆绑（或“合并”）技术。在使用高维基础资产流程的情况下，通过使用捆绑技术，可以大大提高风险承担配置文件的准确性，并且计算速度相当快。通过各种数值示例，对SGBM中使用的捆绑技术和回归逼近技术进行了详细分析。我们提供了SGBM，标准回归方法（SRM）和标准回归捆绑方法（SRBM）的效率比较。我们还表明，对于不连续的回报（例如数字期权），通过适当地使用捆绑技术，SGBM可以获得期权价格和风险敞口的准确和稳定的结果。与一维欧洲和百慕大期权的基准结果相比，SGMBM在计算曝光量时具有很高的准确性。通过使用SGBM来有效计算预期风险（EE）构成了多资产投资组合信贷价值调整（CVA）的基础。

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