英文标题：
《Turbocharging Monte Carlo pricing for the rough Bergomi model》
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作者：
Ryan McCrickerd, Mikko S. Pakkanen
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最新提交年份：
2018
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英文摘要：
  The rough Bergomi model, introduced by Bayer, Friz and Gatheral [Quant. Finance 16(6), 887-904, 2016], is one of the recent rough volatility models that are consistent with the stylised fact of implied volatility surfaces being essentially time-invariant, and are able to capture the term structure of skew observed in equity markets. In the absence of analytical European option pricing methods for the model, we focus on reducing the runtime-adjusted variance of Monte Carlo implied volatilities, thereby contributing to the model\'s calibration by simulation. We employ a novel composition of variance reduction methods, immediately applicable to any conditionally log-normal stochastic volatility model. Assuming one targets implied volatility estimates with a given degree of confidence, thus calibration RMSE, the results we demonstrate equate to significant runtime reductions - roughly 20 times on average, across different correlation regimes.
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中文摘要：
拜耳、弗里兹和Gatheral引入的粗糙Bergomi模型【Quant.Finance 16（6），887-9042016】是最新的粗糙波动率模型之一，该模型与隐含波动率曲面基本上是时不变的风格化事实相一致，并且能够捕捉股市中观察到的偏差的期限结构。在缺乏模型的分析性欧式期权定价方法的情况下，我们专注于减少蒙特卡罗隐含波动率的运行时调整方差，从而有助于通过模拟对模型进行校准。我们采用了一种新的组合方差缩减方法，可立即应用于任何条件对数正态随机波动率模型。假设其中一个目标是具有给定置信度的隐含波动率估计，从而校准RMSE，我们证明的结果相当于显著的运行时减少-在不同的相关制度中，平均约为20倍。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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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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