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Do artificial neural networks provide improved volatility forecasts : evidence from Asian markets
Sahiner, Mehmet; McMillan, David G.; Kambouroudis, Dimos - In: Journal of economics and finance : JEF 47 (2023) 3, pp. 723-762
Persistent link: https://www.econbiz.de/10014380680
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Jump forecasting in foreign exchange markets : a high-frequency analysis
Uzun, Sevcan; Sensoy, Ahmet; Nguyen, Duc Khuong - In: Journal of forecasting 42 (2023) 3, pp. 578-624
Persistent link: https://www.econbiz.de/10014292217
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Implied volatility directional forecasting : a machine learning approach
Vrontos, Spyridon D.; Galakis, John; Vrontos, Ioannis D. - In: Quantitative finance 21 (2021) 10, pp. 1687-1706
Persistent link: https://www.econbiz.de/10012653707
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Applications of machine learning for corporate bond yield spread forecasting
Kim, Jong-Min; Kim, Dong H.; Jung, Hojin - In: The North American journal of economics and finance : a … 58 (2021), pp. 1-11
Persistent link: https://www.econbiz.de/10013188180
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Machine-learning stock market volatility : predictability, drivers, and economic value
Díaz, Juan; Hansen, Erwin; Cabrera, Gabriel - In: International review of financial analysis 94 (2024), pp. 1-23
Persistent link: https://www.econbiz.de/10014544073
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A comparison of machine learning methods for predicting the direction of the US stock market on the basis of volatility indices
Campisi, Giovanni; Muzzioli, Silvia; De Baets, Bernard - In: International journal of forecasting 40 (2024) 3, pp. 869-880
Persistent link: https://www.econbiz.de/10014547222
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Investor confidence and forecastability of US stock market realized volatility : evidence from machine learning
Gupta, Rangan; Nel, Jacobus; Pierdzioch, Christian - In: The journal of behavioral finance : a publication of … 24 (2023) 1, pp. 111-122
Persistent link: https://www.econbiz.de/10013547864
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Forecasting the realized variance of oil-price returns using machine learning : is there a role for U.S. state-level uncertainty?
Çepni, Oğuzhan; Gupta, Rangan; Pienaar, Daniel; … - In: Energy economics 114 (2022), pp. 1-14
Persistent link: https://www.econbiz.de/10013477404
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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 returns volatility of cryptocurrency by applying various deep learning algorithms
Khan, Farman Ullah; Khan, Faridoon; Shaikh, Parvez Ahmed - In: Future Business Journal 9 (2023), pp. 1-11
The study aims at forecasting the return volatility of the cryptocurrencies using several machine learning algorithms … forecasting cryptocurrency volatility accordingly. … observations. We predict the volatility of four cryptocurrencies, namely Bitcoin, Ethereum, XRP, and Tether, and compare their …
Persistent link: https://www.econbiz.de/10014381146