英文标题:
《Deep Learning Volatility》
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作者:
Blanka Horvath, Aitor Muguruza, and Mehdi Tomas
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最新提交年份:
2019
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英文摘要:
We present a neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models -including the rough volatility family- and a range of derivative contracts. The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling: The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several numerical pricers and model families (such as rough volatility models) within the scope of applicability in industry practice. The form in which information from available data is extracted and stored influences network performance: This approach is inspired by representing the implied volatility and option prices as a collection of pixels. In a number of applications we demonstrate the prowess of this modelling approach regarding accuracy, speed, robustness and generality and also its potentials towards model recognition.
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中文摘要:
我们提出了一种基于神经网络的标定方法,该方法可以在几毫秒内完成整个隐含波动率曲面的标定任务。该框架始终适用于一系列波动率模型(包括粗略波动率系列)和一系列衍生品合约。
神经网络在这项工作中的目的是离线逼近复杂的定价函数,这些函数很难用其他方法表示或评估很耗时。我们强调了这一观点如何为定量建模打开了新的视野:解除了衍生品合约定价缓慢造成的校准瓶颈。这将几个数值定价者和模型系列(如粗糙波动率模型)纳入了行业实践的适用范围。从可用数据中提取和存储信息的形式会影响网络性能:这种方法的灵感来自于将隐含波动率和期权价格表示为像素集合。在许多应用中,我们展示了这种建模方法在准确性、速度、鲁棒性和通用性方面的强大能力,以及它在模型识别方面的潜力。
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分类信息:
一级分类:Quantitative Finance 数量金融学
二级分类:Mathematical Finance 数学金融学
分类描述:Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法,包括随机、概率和泛函分析、代数、几何和其他方法
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