On the estimation of non linear functions in stochastic volatility models. Issue 2 (17th January 2021)

• Record Type: Journal Article
• Authors: Albano, Giuseppina; Giordano, Francesco; Perna, Cira
• Is Part Of: Communications in statistics. Volume 50:Issue 2(2021)
• Page Start: 387
• Page End: 399
• Publication Date: 2021-01-17
• Subjects: Volatility -- ergodicity -- smooth function of the sample mean -- bootstrap; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1635700
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: This paper focuses on the inference of suitable generally non linear functions in stochastic volatility models. In this context, in order to estimate the variance of the proposed estimators, a moving block bootstrap (MBB) approach is suggested and discussed. Under mild assumptions, we show that the MBB procedure is weakly consistent. Moreover, a methodology to choose the optimal length block in the MBB is proposed. Some examples and simulations on the model are also made to show the performance of the proposed procedure.