A Realized Stochastic Volatility Model With Box–Cox Transformation
提出一类新的实现随机波动率模型，用Box-Cox变换替代传统的对数变换，通过两步极大似然估计法进行估计，模拟和实证表明该模型能更准确捕捉波动率特征。
This article presents a new class of realized stochastic volatility model based on realized volatilities and returns jointly. We generalize the traditionally used logarithm transformation of realized volatility to the Box-Cox transformation, a more flexible parametric family of transformations. A two-step maximum likelihood estimation procedure is introduced to estimate this model on the basis of Koopman and Scharth (2013). Simulation results show that the two-step estimator performs well, and the misspecified log transformation may lead to inaccurate parameter estimation and certain excessive skewness and kurtosis. Finally, an empirical investigation on realized volatility measures and daily returns is carried out for several stock indices.