A Real-Time GARCH-MIDAS model
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DOI: 10.1016/j.frl.2023.104103
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References listed on IDEAS
- Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
- Maheu John, 2005. "Can GARCH Models Capture Long-Range Dependence?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-43, December.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Yu, Jize & Zhang, Li & Peng, Lijuan & Wu, Rui, 2023. "Which component of air quality index drives stock price volatility in China: a decomposition-based forecasting method," Finance Research Letters, Elsevier, vol. 51(C).
- Curto, José Dias & Serrasqueiro, Pedro, 2022. "The impact of COVID-19 on S&P500 sector indices and FATANG stocks volatility: An expanded APARCH model," Finance Research Letters, Elsevier, vol. 46(PA).
- Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
- Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
- Yu, Jun, 2005.
"On leverage in a stochastic volatility model,"
Journal of Econometrics, Elsevier, vol. 127(2), pages 165-178, August.
- Jun Yu, 2004. "On Leverage in a Stochastic Volatility Model," Working Papers 13-2004, Singapore Management University, School of Economics.
- Jun Yu, 2004. "On Leverage in a Stochastic Volatility Model," Econometric Society 2004 Far Eastern Meetings 506, Econometric Society.
- Jun Yu, 2004. "On leverage in a stochastic volatility model," Econometric Society 2004 Far Eastern Meetings 497, Econometric Society.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, "undated". "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
- Bakry, Walid & Kavalmthara, Peter John & Saverimuttu, Vivienne & Liu, Yiyang & Cyril, Sajan, 2022. "Response of stock market volatility to COVID-19 announcements and stringency measures: A comparison of developed and emerging markets," Finance Research Letters, Elsevier, vol. 46(PA).
- Salisu, Afees A. & Gupta, Rangan, 2021.
"Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach,"
Global Finance Journal, Elsevier, vol. 48(C).
- Afees A. Salisu & Rangan Gupta, 2019. "Oil Shocks and Stock Market Volatility of the BRICS: A GARCH-MIDAS Approach," Working Papers 201976, University of Pretoria, Department of Economics.
- Patton, Andrew J., 2011.
"Volatility forecast comparison using imperfect volatility proxies,"
Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
- Andrew Patton, 2006. "Volatility Forecast Comparison using Imperfect Volatility Proxies," Research Paper Series 175, Quantitative Finance Research Centre, University of Technology, Sydney.
- Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
- Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022. "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- Christensen, Kim & Podolskij, Mark, 2007. "Realized range-based estimation of integrated variance," Journal of Econometrics, Elsevier, vol. 141(2), pages 323-349, December.
- Wang, Fangfang & Ghysels, Eric, 2015. "Econometric Analysis Of Volatility Component Models," Econometric Theory, Cambridge University Press, vol. 31(2), pages 362-393, April.
- Li, Yingying & Liu, Guangying & Zhang, Zhiyuan, 2022. "Volatility of volatility: Estimation and tests based on noisy high frequency data with jumps," Journal of Econometrics, Elsevier, vol. 229(2), pages 422-451.
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Cited by:
- Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024. "Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model," Finance Research Letters, Elsevier, vol. 67(PA).
- Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
- Wu, Xinyu & Zhao, An & Wang, Yuyao & Han, Yang, 2024. "Forecasting Chinese stock market volatility with high-frequency intraday and current return information," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
- Chen, Zhenlong & Liu, Junjie & Hao, Xiaozhen, 2024. "Can asymmetry, long memory, and current return information improve crude oil volatility prediction? ——Evidence from ASHARV-MIDAS model," Finance Research Letters, Elsevier, vol. 64(C).
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More about this item
Keywords
Real-Time GARCH-MIDAS; Persistence; Current return information; Volatility of volatility; Volatility forecasting;All these keywords.
JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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