Journal
JOURNAL OF TIME SERIES ANALYSIS
Volume 43, Issue 3, Pages 345-370Publisher
WILEY
DOI: 10.1111/jtsa.12616
Keywords
Volatility asymmetry; low-frequency historical data; high-frequency historical data; quasi-maximum likelihood estimators; volatility prediction power
Funding
- National Natural Science Foundation of China [92046005, 71671106]
- Shanghai Institute of International Finance and Economics
- Innovative Research Team of Shanghai University of Finance and Economics [2020110930]
- State Key Program of National Natural Science Foundation of China [71931004]
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This study introduces a new econometric model for volatility asymmetry and establishes its asymptotic properties. Through simulation studies, it is verified that the proposed model has better volatility forecasting performance than the GARCH-Ito model in the literature. A real data example further demonstrates the superior volatility prediction power of the new model.
Volatility asymmetry is a hot topic in high-frequency financial market. This article proposes a new econometric model, which could describe volatility asymmetry based on high-frequency data and low-frequency data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed model and method. And a real data example is demonstrated that the new model has more substantial volatility prediction power than GARCH-Ito model in the literature.
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