Estimation of Tempered Stable Lévy Models of Infinite Variation

José E. Figueroa-López (), Ruoting Gong () and Yuchen Han ()
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José E. Figueroa-López: Washington University in St. Louis
Ruoting Gong: Illinois Institute of Technology
Yuchen Han: Washington University in St. Louis

Methodology and Computing in Applied Probability, 2022, vol. 24, issue 2, 713-747

Abstract: Abstract Truncated realized quadratic variations (TRQV) are among the most widely used high-frequency-based nonparametric methods to estimate the volatility of a process in the presence of jumps. Nevertheless, the truncation level is known to critically affect its performance, especially in the presence of infinite variation jumps. In this paper, we study the optimal truncation level, in the mean-square error sense, for a semiparametric tempered stable Lévy model. We obtain a novel closed-form 2nd-order approximation of the optimal threshold in a high-frequency setting. As an application, we propose a new estimation method, which combines iteratively an approximate semiparametric method of moment estimator and TRQVs with the newly found small-time approximation for the optimal threshold. The method is tested via simulations to estimate the volatility and the Blumenthal-Getoor index of a generalized CGMY model and, via a localization technique, to estimate the integrated volatility of a Heston type model with CGMY jumps. Our method is found to outperform other alternatives proposed in the literature when working with a Lévy process (i.e., the volatility is constant), or when the index of jump intensity Y is larger than 3/2 in the presence of stochastic volatility.

Keywords: Threshold estimator; High-frequency estimation; Lévy models; Method of moment estimators; Optimal parameter tuning; 60G51; 62M09 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11009-022-09940-7

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