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The predictive power of Nelson-Siegel factor loadings for the real economy
Han, Yang; Jiao, Anqi; Ma, Jun - In: Journal of empirical finance 64 (2021), pp. 95-127
Persistent link: https://www.econbiz.de/10013259403
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Forecasting crude oil prices with a large set of predictors : Can LASSO select powerful predictors?
Zhang, Yaojie; Ma, Feng; Wang, Yudong - In: Journal of empirical finance 54 (2019), pp. 97-117
Persistent link: https://www.econbiz.de/10012174816
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Inflation uncertainty, disagreement and monetary policy : evidence from the ECB survey of professional forecasters
Glas, Alexander; Hartmann, Matthias - In: Journal of empirical finance 39 (2016), pp. 215-228
Persistent link: https://www.econbiz.de/10011663852
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Forecasting aggregate stock market volatility using financial and macroeconomic predictors : Which models forecast best, when and why?
Nonejad, Nima - In: Journal of empirical finance 42 (2017), pp. 131-154
Persistent link: https://www.econbiz.de/10011808557
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Aggregate investor preferences and beliefs in stock market : a stochastic dominance analysis
Fang, Yi - In: Journal of empirical finance 19 (2012) 4, pp. 528-547
Persistent link: https://www.econbiz.de/10009615664
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Aggregate investor preferences and beliefs : a comment
Post, Thierry; Kopa, Miloš - In: Journal of empirical finance 23 (2013), pp. 187-190
Persistent link: https://www.econbiz.de/10010221754
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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