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雷达卡
英文标题：
《A Quantization Approach to the Counterparty Credit Exposure Estimation》
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作者：
M. Bonollo, L. Di Persio, I. Oliva, A. Semmoloni
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最新提交年份：
2015
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英文摘要：
During recent years the counterparty risk subject has received a growing attention because of the so called Basel Accord. In particular the Basel III Accord asks the banks to fulfill finer conditions concerning counterparty credit exposures arising from banks\' derivatives, securities financing transactions, default and downgrade risks characterizing the Over The Counter (OTC) derivatives market, etc. Consequently the development of effective and more accurate measures of risk have been pushed, particularly focusing on the estimate of the future fair value of derivatives with respect to prescribed time horizon and fixed grid of time buckets. Standard methods used to treat the latter scenario are mainly based on ad hoc implementations of the classic Monte Carlo (MC) approach, which is characterized by a high computational time, strongly dependent on the number of considered assets. This is why many financial players moved to more enhanced Technologies, e.g., grid computing and Graphics Processing Units (GPUs) capabilities. In this paper we show how to implement the quantization technique, in order to accurately estimate both pricing and volatility values. Our approach is tested to produce effective results for the counterparty risk evaluation, with a big improvement concerning required time to run when compared to MC approach.
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中文摘要：
近年来，由于所谓的巴塞尔协议，交易对手风险问题受到了越来越多的关注。特别是《巴塞尔协议III》要求银行在银行衍生产品、证券融资交易、场外衍生品市场的违约和降级风险等方面满足更精细的交易对手信用风险敞口条件。因此，推动了有效和更准确的风险度量的发展，特别关注在规定的时间范围和固定的时间段网格下，衍生工具的未来公允价值的估计。用于处理后一种情况的标准方法主要基于经典蒙特卡罗（MC）方法的特殊实现，其特点是计算时间长，强烈依赖于考虑的资产数量。这就是为什么许多金融玩家转向了更强大的技术，例如网格计算和图形处理单元（GPU）功能。在本文中，我们展示了如何实现量化技术，以便准确估计定价和波动率值。我们的方法经过测试，能够产生有效的交易对手风险评估结果，与MC方法相比，在运行所需时间方面有了很大的改进。
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Pricing of Securities 证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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