Volatility analysis with realized GARCH-Itô models
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DOI: 10.1016/j.jeconom.2020.07.007
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- Li, Li & Kang, Yanfei & Li, Feng, 2023.
"Bayesian forecast combination using time-varying features,"
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- Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.
- Kim, Donggyu & Song, Xinyu & Wang, Yazhen, 2022.
"Unified discrete-time factor stochastic volatility and continuous-time Itô models for combining inference based on low-frequency and high-frequency,"
Journal of Multivariate Analysis, Elsevier, vol. 192(C).
- Donggyu Kim & Xinyu Song & Yazhen Wang, 2020. "Unified Discrete-Time Factor Stochastic Volatility and Continuous-Time Ito Models for Combining Inference Based on Low-Frequency and High-Frequency," Papers 2006.12039, arXiv.org.
- Minseog Oh & Donggyu Kim, 2024.
"Effect of the U.S.–China Trade War on Stock Markets: A Financial Contagion Perspective,"
Journal of Financial Econometrics, Oxford University Press, vol. 22(4), pages 954-1005.
- Minseog Oh & Donggyu Kim, 2021. "Effect of the U.S.--China Trade War on Stock Markets: A Financial Contagion Perspective," Papers 2111.09655, arXiv.org.
- Jianqing Fan & Donggyu Kim & Minseok Shin & Yazhen Wang, 2024. "Factor and Idiosyncratic VAR-Ito Volatility Models for Heavy-Tailed High-Frequency Financial Data," Working Papers 202415, University of California at Riverside, Department of Economics.
- Huiling Yuan & Guodong Li & Junhui Wang, 2022. "High-Frequency-Based Volatility Model with Network Structure," Papers 2204.12933, arXiv.org.
- Dohyun Chun & Donggyu Kim, 2022.
"State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data,"
Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 105-124, January.
- Dohyun Chun & Donggyu Kim, 2021. "State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data," Papers 2102.13404, arXiv.org.
- Jin Yu Fu & Jin Guan Lin & Guangying Liu & Hong Xia Hao, 2024. "Statistical inference for GQARCH‐Itô‐jumps model based on the realized range volatility," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(4), pages 613-638, July.
- Donggyu Kim & Minseok Shin, 2023. "Volatility models for stylized facts of high‐frequency financial data," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 262-279, May.
- Fu, Jin-Yu & Lin, Jin-Guan & Hao, Hong-Xia, 2023. "Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1698-1712.
- Donggyu Kim & Minseog Oh & Yazhen Wang, 2022. "Conditional quantile analysis for realized GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 640-665, July.
- Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
- Donggyu Kim & Minseok Shin, 2024. "Robust High-Dimensional Time-Varying Coefficient Estimation," Working Papers 202417, University of California at Riverside, Department of Economics.
- Donggyu Kim, 2021. "Exponential GARCH-Ito Volatility Models," Papers 2111.04267, arXiv.org.
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More about this item
Keywords
High-frequency financial data; Option data; Quasi-maximum likelihood estimation; Stochastic differential equation; Volatility estimation and prediction;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
Statistics
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