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Data size requirement for forecasting daily crude oil price with neural networks
Aras, Serkan; Hamdi, Manel - In: Scientific Annals of Economics and Business 66 (2019) 3, pp. 363-388
Persistent link: https://www.econbiz.de/10012405854
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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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Forecasting exchange rates with neural networks : time variation, nonstationarity, and causal models
Reikard, Gordon - In: International economic journal 37 (2023) 2, pp. 202-219
Persistent link: https://www.econbiz.de/10014295003
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Current and prospective estimate of counterparty risk through dynamic neural networks
Agnese, Alessio; Giribone, Pier Giuseppe; Querci, Francesca - In: Risk management magazine 17 (2022) 2, pp. 42-61
networks. The forecasting aspect in the calculation of such risk measure is becoming more and more important over time as …
Persistent link: https://www.econbiz.de/10013501084
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Effects of multiple financial news shocks on tourism demand volatility modelling and forecasting
Zhang, Yuruixian; Choo, Wei Chong; Yuhanis Abdul Aziz; … - In: Journal of risk and financial management : JRFM 15 (2022) 7, pp. 1-47
GJRGARCH-FFNSs model is the best model for Malaysian tourism demand volatility forecasting accuracy. Furthermore, KLCI and Gold … economics and financial markets, the effects of combining multiple news shocks on the volatility of tourism demand have not yet …), conditional heteroscedastic volatility models, and multiple news shocks are suitable for forecasting the volatility of the …
Persistent link: https://www.econbiz.de/10013369139
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Investigate the effect of exchange rate volatility on the demand for life insurance in Iran
Hosseinzadeh, Maryam; Daei-Karimzadeh, Saeed - In: International journal of economics and financial issues … 7 (2017) 2, pp. 166-174
Persistent link: https://www.econbiz.de/10011786561
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Macroeconomic forecasting and evaluation with supervised and neural network reinforced factor models
Umbach, Simon Lineu - 2021
Persistent link: https://www.econbiz.de/10013166034
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Estimating nonlinear heterogeneous agents models with neural networks
Kase, Hanno; Melosi, Leonardo; Rottner, Matthias - 2022
Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...
Persistent link: https://www.econbiz.de/10013257224
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Estimating nonlinear heterogeneous agents models with neural networks
Kase, Hanno; Melosi, Leonardo; Rottner, Matthias - 2022
Persistent link: https://www.econbiz.de/10013263361
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Learning forecast-efficient yield curve factor decompositions with neural networks
Kauffmann, Piero C.; Takada, Hellinton H.; Terada, Ana T.; … - In: Econometrics : open access journal 10 (2022) 2, pp. 1-15
Most factor-based forecasting models for the term structure of interest rates depend on a fixed number of factor … forecasting model that learns new factor decompositions directly from data for an arbitrary number of factors, combining a …
Persistent link: https://www.econbiz.de/10013355189