论文标题
隐含波动表面的PCA
PCA for Implied Volatility Surfaces
论文作者
论文摘要
当试图从历史资产返回中构建因子模型时,主成分分析(PCA)是一个有用的工具。对于美国股票的隐含波动,有一个基于PCA的模型,其主要特征港的返回时间序列与总体市场因素接近。作者表明,这个市场因素是由暗示允许回报的加权平均值的每日复合产生的指数,其权重基于期权的开放兴趣(OI)和VEGA。作者还分析了S&P500成分隐含波动率的张量结构的奇异向量,并找到证据表明某种类型的OI和VEGA加权指数应该是该市场中至少两个重要因素之一。
Principal component analysis (PCA) is a useful tool when trying to construct factor models from historical asset returns. For the implied volatilities of U.S. equities there is a PCA-based model with a principal eigenportfolio whose return time series lies close to that of an overarching market factor. The authors show that this market factor is the index resulting from the daily compounding of a weighted average of implied-volatility returns, with weights based on the options' open interest (OI) and Vega. The authors also analyze the singular vectors derived from the tensor structure of the implied volatilities of S&P500 constituents, and find evidence indicating that some type of OI and Vega-weighted index should be one of at least two significant factors in this market.