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Nonlinear prediction of exchange rates with monetary fundamentals
Qi, Min; Wu, Yangru - In: Journal of empirical finance 10 (2003) 5, pp. 623-640
Persistent link: https://www.econbiz.de/10001806976
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An artificial neural network-GARCH model for international stock return volatility
Donaldson, R. Glen - In: Journal of empirical finance 4 (1997) 1, pp. 17-46
Persistent link: https://www.econbiz.de/10001224775
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Forecasting realized volatility with machine learning : panel data perspective
Zhu, Haibin; Bai, Lu; He, Lidan; Liu, Zhi - In: Journal of empirical finance 73 (2023), pp. 251-271
Persistent link: https://www.econbiz.de/10014477028
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The commodity risk premium and neural networks
Rad, Hossein; Low, Rand Kwong Yew; Miffre, Joëlle; … - In: Journal of empirical finance 74 (2023), pp. 1-20
Persistent link: https://www.econbiz.de/10014477111
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Order price clustering, size clustering, and stock price movements: evidence from the Taiwan Stock Exchange
Lien, Da-hsiang Donald; Hung, Pi-Hsia; Hung, I-Chung - In: Journal of empirical finance 52 (2019), pp. 149-177
Persistent link: https://www.econbiz.de/10012170684
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The demand effect of yield-chasing retail investors: evidence from the Chinese enterprise bond market
Liu, Clark; Wang, Shujing; Wei, K. C. John; Zhong, Ninghua - In: Journal of empirical finance 50 (2019), pp. 57-77
Persistent link: https://www.econbiz.de/10012169920
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Instability of return prediction models
Paye, Bradley S.; Timmermann, Allan - In: Journal of empirical finance 13 (2006) 3, pp. 274-315
Persistent link: https://www.econbiz.de/10003334583
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Forecasting financial crises and contagion in Asia using dynamic factor analysis
Cipollini, Andrea; Kapetanios, George - In: Journal of empirical finance 16 (2009) 2, pp. 188-200
Persistent link: https://www.econbiz.de/10003839250
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Model averaging in risk management with an application to futures markets
Pesaran, M. Hashem; Schleicher, Christoph; Zaffaroni, Paolo - In: Journal of empirical finance 16 (2009) 2, pp. 280-305
Persistent link: https://www.econbiz.de/10003839329
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Which power variation predicts volatility well?
Ghysels, Eric; Sohn, Bumjean - In: Journal of empirical finance 16 (2009) 4, pp. 686-700
We estimate MIDAS regressions with various (bi)power variations to predict future volatility measured via increments in quadratic variation. Instead of pre-determining the (bi)power variation we parameterize it and estimate the intra-daily return power transformation that optimally predicts...
Persistent link: https://www.econbiz.de/10003900365