Statistical Inference for Unified Garch-Itô Models with High-Frequency Financial Data

DOI10.1111/jtsa.12171zbMath1359.62373OpenAlexW2172331772MaRDI QIDQ2815047

Donggyu Kim

Publication date: 27 June 2016

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/jtsa.12171

zbMATH Keywords

GARCHquasi-maximum likelihood estimatorrealized volatilityItô processhigh-frequency financial datalow-frequency financial data

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) Statistical methods; risk measures (91G70) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (5)

Conditional quantile analysis for realized GARCH models ⋮ Volatility models for stylized facts of high‐frequency financial data ⋮ Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction ⋮ Forecasting price of financial market crash via a new nonlinear potential GARCH model ⋮ State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data

Cites Work

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• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
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• FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA
• Modeling and Forecasting Realized Volatility
• A Tale of Two Time Scales

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