Double Smoothed Volatility Estimation of Potentially Non‐stationary Jump‐diffusion Model of Shibor

The occurrence‐50 of economic policies and other sudden and large shocks often bring out jumps in financial data, which can be characterized through continuous‐time jump‐diffusion model. In this article, we present the double smoothed non‐parametric approach for infinitesimal conditional volatility...

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Bibliographic Details
Published inJournal of time series analysis Vol. 43; no. 1; pp. 53 - 82
Main Authors Song, Yuping, Hou, Weijie, Lin, Zhengyan
Format Journal Article
LanguageEnglish
Published Oxford, UK John Wiley & Sons, Ltd 01.01.2022
Blackwell Publishing Ltd
Subjects
Asymptotic methods
Asymptotic properties
bias and variance reduction
consistency and asymptotic normality
Diffusion
Diffusion models with jumps
Economic models
Economic policy
infinitesimal conditional moment
Monte Carlo simulation
non‐stationary high frequency financial data
Normality
Recurrent
Simulation
Volatility
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Summary:The occurrence‐50 of economic policies and other sudden and large shocks often bring out jumps in financial data, which can be characterized through continuous‐time jump‐diffusion model. In this article, we present the double smoothed non‐parametric approach for infinitesimal conditional volatility of jump‐diffusion model based on high frequency data. Under certain minimal conditions, we obtain the strong consistency and asymptotic normality for the estimator as the time span T → ∞ and the sample interval Δn→0. The procedure and asymptotic behavior can be applied for both Harris recurrent and positive Harris recurrent processes. The finite sample properties of the underlying double smoothed volatility estimator are verified through Monte Carlo simulation and Shanghai Interbank Offered Rate in China for application.
ISSN:0143-9782
1467-9892
DOI:10.1111/jtsa.12592