Volatility analysis with realized GARCH-Itô models

DOI10.1016/j.jeconom.2020.07.007zbMath1471.62498arXiv1907.01175OpenAlexW3048259990MaRDI QIDQ134810

Zhiping Lu, Xinyu Song, Xiangyu Cui, Yong Zhou, Yazhen Wang, Donggyu Kim, Huiling Yuan, Yanyan Li

Publication date: May 2021

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1907.01175

zbMATH Keywords

stochastic differential equationquasi-maximum likelihood estimationhigh-frequency financial dataoption datavolatility estimation and prediction

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items

Editorial for the special issue on financial econometrics in the age of the digital economy ⋮ Conditional quantile analysis for realized GARCH models ⋮ Review of statistical approaches for modeling high-frequency trading data ⋮ MCMC interweaving strategy for estimating stochastic volatility model and its application ⋮ Volatility models for stylized facts of high‐frequency financial data ⋮ On a class of Ito stochastic differential equations ⋮ Overnight GARCH-Itô Volatility Models ⋮ GARCHIto ⋮ Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency ⋮ State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data

Uses Software

• itsmr
• ITSM2000

Cites Work

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