Central limit theorems for the non-parametric estimation of time-changed Lévy models

Let {Z_t}_t0 be a Lévy process with Lévy measure ν and let be a random clock, where g is a non-negative function and is an ergodic diffusion independent of Z. Time-changed Lévy models of the form are known to incorporate several important stylized features of asset prices, such as leptokurtic distributions and volatility clustering. In this article, we prove central limit theorems for a type of estimators of the integral parameter β(φ{symbol}):=∫φ{symbol}(x)ν(dx), valid when both the sampling frequency and the observation time-horizon of the process get larger. Our results combine the long-run ergodic properties of the diffusion process with the short-term ergodic properties of the Lévy process Z via central limit theorems for martingale differences. The performance of the estimators are illustrated numerically for Normal Inverse Gaussian process Z and a Cox-Ingersoll-Ross process.