A new volatility model: GQARCH‐ItÔ model
DOI10.1111/jtsa.12616OpenAlexW3194701565MaRDI QIDQ5095287
Huiling Yuan, Yong Zhou, Xiangyu Cui, Lu Xu, Yulei Sun
Publication date: 8 August 2022
Published in: Journal of Time Series Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2101.05644
quasi-maximum likelihood estimatorsvolatility asymmetryhigh-frequency historical datalow-frequency historical datavolatility prediction power
Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Inference from stochastic processes (62Mxx)
Cites Work
- Unified discrete-time and continuous-time models and statistical inferences for merged low-frequency and high-frequency financial data
- A discrete-time model for daily S\&P500 returns and realized variations: jumps and leverage effects
- Data-based ranking of realised volatility estimators
- Quasi-maximum likelihood estimation of volatility with high frequency data
- Volatility forecast comparison using imperfect volatility proxies
- Estimation of the discontinuous leverage effect: evidence from the NASDAQ order book
- Generalized autoregressive conditional heteroscedasticity
- Asymptotic nonequivalence of GARCH models and diffusions
- Estimation of the stochastic leverage effect using the Fourier transform method
- Microstructure noise in the continuous case: the pre-averaging approach
- Efficient estimation of stochastic volatility using noisy observations: a multi-scale approach
- Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise
- Evaluating Volatility and Correlation Forecasts
- Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
- Quadratic ARCH Models
- The Estimation of Leverage Effect With High-Frequency Data
- A Tale of Two Time Scales
This page was built for publication: A new volatility model: GQARCH‐ItÔ model
