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Gamma stochastic volatility models
Abraham, Bovas; Balakrishna, N.; Sivakumar, Ranjini - In: Journal of forecasting 25 (2006) 3, pp. 153-171
Persistent link: https://www.econbiz.de/10003318072
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Daily FX volatility forecasts : can the GARCH (1,1) model be beaten using high-frequency data?
McMillan, David G.; Speight, Alan E. H. - In: Journal of forecasting 31 (2012) 4, pp. 330-343
Persistent link: https://www.econbiz.de/10009576375
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Does information help intra-day volatility forecasts?
McMillan, David G.; Quiroga García, Raquel - In: Journal of forecasting 32 (2013) 1, pp. 1-9
Persistent link: https://www.econbiz.de/10009758739
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Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes
Chen, Bei; Gel, Yulia R.; Balakrishna, N.; Abraham, Bovas - In: Journal of forecasting 30 (2011) 1, pp. 51-71
Persistent link: https://www.econbiz.de/10009233916
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Daily volatility forecasts : reassessing the performance of GARCH models
McMillan, David G.; Speight, Alan E. H. - In: Journal of forecasting 23 (2004) 6, pp. 449-460
Persistent link: https://www.econbiz.de/10002233160
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Forecasting oil futures realized range-based volatility with jumps, leverage effect, and regime switching : new evidence from MIDAS models
Lu, Xinjie; Ma, Feng; Wang, Jiqian; Liu, Jing - In: Journal of forecasting 41 (2022) 4, pp. 853-868
Persistent link: https://www.econbiz.de/10013287870
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Forecasting international equity market volatility : a new approach
Liang, Chao; Li, Yan; Ma, Feng; Zhang, Yaojie - In: Journal of forecasting 41 (2022) 7, pp. 1433-1457
Persistent link: https://www.econbiz.de/10013465704
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A tug of war of forecasting the US stock market volatility : oil futures overnight versus intraday information
Ma, Feng; Wahab, M. I. M.; Chevallier, Julien; Li, Ziyang - In: Journal of forecasting 42 (2023) 1, pp. 60-75
Persistent link: https://www.econbiz.de/10013465762
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Which factors drive Bitcoin volatility : macroeconomic, technical, or both?
Wang, Jiqian; Ma, Feng; Bouri, Elie; Guo, Yangli - In: Journal of forecasting 42 (2023) 4, pp. 970-988
Persistent link: https://www.econbiz.de/10014292892