Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data

DOI10.1016/j.jeconom.2016.05.003zbMath1443.62357OpenAlexW2398294697MaRDI QIDQ134805

Yazhen Wang, Donggyu Kim, Donggyu Kim, Yazhen Wang

Publication date: October 2016

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2016.05.003

zbMATH Keywords

stochastic differential equationGARCHquasi-maximum likelihood estimatorrealized volatilityItô process

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 (12)

Forecasting realized volatility: a review ⋮ A new volatility model: GQARCH‐ItÔ model ⋮ Conditional quantile analysis for realized GARCH models ⋮ Volatility models for stylized facts of high‐frequency financial data ⋮ Overnight GARCH-Itô Volatility Models ⋮ Factor GARCH-Itô models for high-frequency data with application to large volatility matrix prediction ⋮ Large volatility matrix estimation with factor-based diffusion model for high-frequency financial data ⋮ GARCHIto ⋮ Volatility analysis with realized GARCH-Itô models ⋮ Statistical Inference for Unified Garch-Itô Models with High-Frequency Financial Data ⋮ 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

Cites Work

• Unnamed Item
• A multiple indicators model for volatility using intra-daily data
• Asymptotic theory for large volatility matrix estimation based on high-frequency financial data
• Quasi-maximum likelihood estimation of volatility with high frequency data
• Generalized autoregressive conditional heteroscedasticity
• Asymptotic nonequivalence of GARCH models and diffusions
• Microstructure noise in the continuous case: the pre-averaging approach
• ARCH models as diffusion approximations
• Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
• Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches
• Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
• Multi-Scale Jump and Volatility Analysis for High-Frequency Financial Data
• CONVERGENCE SPEED OF GARCH OPTION PRICE TO DIFFUSION OPTION PRICE
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Option Pricing in ARCH-type Models
• A Tale of Two Time Scales

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