Empirical Asset Pricing with Functional Factors
提出在实证资产定价模型中使用函数因子的方法，可处理利率期限结构或隐含波动率微笑等高维函数曲线，并估计因子载荷和风险溢价参数。实证表明标普500隐含方差微笑的凸性对动量组合有定价能力。
Abstract We propose a methodology to use functional factors in empirical asset pricing models. We establish conditions under which it is possible to recover linear beta pricing. The proposed estimation approach allows us to use high-dimensional functional curves, such as the term structure of interest rates or the implied volatility smile, as factors. This framework enables the estimation of functional factor loadings as well as risk premium parameters of factor models. We derive estimation algorithms and establish the asymptotic consistency and normality of the parameter estimates. In an empirical application, we show that the implied variance smile of the S&P500 is a potential pricing factor for momentum-sorted portfolios. In particular, a positive risk premium is earned by the convexity of the implied variance curve.