Estimating the volatility occupation time via regularized Laplace inversion

• Main Authors: LI, Jia, TODOROV, Viktor, TAUCHEN
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: semimartingale; long memory; stochastic volatility; semiparametric efficiency; local asymptotic mixed normality; irregular sampling.; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/2581; https://ink.library.smu.edu.sg/context/soe_research/article/3580/viewcontent/EstimatingVolatilityOccupTime_sv_2015.pdf
• Institution: Singapore Management University

We propose a consistent functional estimator for the occupation time of the spot variance of an asset price observed at discrete times on a finite interval with the mesh of the observation grid shrinking to zero. The asset price is modeled nonparametrically as a continuous-time Itô semimartingale with nonvanishing diffusion coefficient. The estimation procedure contains two steps. In the first step we estimate the Laplace transform of the volatility occupation time and, in the second step, we conduct a regularized Laplace inversion. Monte Carlo evidence suggests that the proposed estimator has good small-sample performance and in particular it is far better at estimating lower volatility quantiles and the volatility median than a direct estimator formed from the empirical cumulative distribution function of local spot volatility estimates. An empirical application shows the use of the developed techniques for nonparametric analysis of variation of volatility.