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Forecasting stock market realized volatility using random forest and artificial neural network in South Africa
Diane, Lamine; Brijlal, Pradeep - In: International journal of economics and financial issues … 14 (2024) 2, pp. 5-14
Persistent link: https://www.econbiz.de/10014581568
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Prognosemodelle und Handelsansätze für Implizite Volatilitäten
Sachtler, Michael - 2004
Persistent link: https://www.econbiz.de/10002392690
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Modelling market volatilities : the neural network perspective
González Miranda, Fernando - In: The European journal of finance 3 (1997) 2, pp. 137-157
Persistent link: https://www.econbiz.de/10001224328
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Combining Dimensionality Reduction with Neural Networks for Realized Volatility Forecasting
He, Lidan; Bucci, Andrea; Liu, Zhi - 2021
Factorization (NMF) and Least Absolute Shrinkage and Selection Operator (LASSO) with hybrid artificial neutral networks to forecast …
Persistent link: https://www.econbiz.de/10013233916
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HARNet : a convolutional neural network for realized volatility forecasting
Reisenhofer, Rafael; Bayer, Xandro; Hautsch, Nikolaus - 2022
's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly …
Persistent link: https://www.econbiz.de/10013199338
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Forecasting and trading on the VIX futures market : a neural network approach based on open to close returns and coincident indicators
Ballestra, Luca Vincenzo; Guizzardi, Andrea; Palladini, … - In: International journal of forecasting 35 (2019) 4, pp. 1250-1262
Persistent link: https://www.econbiz.de/10012305272
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Topological tail dependence : evidence from forecasting realized volatility
Souto, Hugo Gobato - In: The Journal of finance and data science : JFDS 9 (2023), pp. 1-26
(RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation … indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a …
Persistent link: https://www.econbiz.de/10014514075
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Electricity price forecasting using hybrid deep learned networks
Prakash N., Krishna; Singh, Jai Govind - In: Journal of forecasting 42 (2023) 7, pp. 1750-1771
Persistent link: https://www.econbiz.de/10014432756
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Prediction of stock index of two-scale long short-term memory model based on multiscale nonlinear integration
Tang, Decai; Pan, Zhiwei; Bethel, Brandon J. - In: Studies in nonlinear dynamics and econometrics : SNDE ; … 26 (2022) 5, pp. 723-735
Persistent link: https://www.econbiz.de/10013554949
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Graph Neural Networks for Forecasting Realized Volatility with Nonlinear Spillover Effects
Zhang, Chao; Pu, Xingyue (Stacy); Cucuringu, Mihai; … - 2023
We propose a novel methodology for modeling and forecasting multivariate realized volatilities using graph neural networks. This approach extends the work of Zhang et al. [2022] (Graph-based methods for forecasting realized covariances) and explicitly incorporates the spillover effects from...
Persistent link: https://www.econbiz.de/10014265206