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Applications in computational finance with a focus on approximation of financial time series by neurocomputing

Spreckelsen, Christian von - 2014

Neural network, financial time series, approximation, forecasting. - Neuronale Netze, Finanzzeitreihen, Prognose …

Persistent link: https://www.econbiz.de/10010511665

Some nonstandard stochastic volatility models and their estimation using structured hidden Markov models

Langrock, Roland; MacDonald, Iain L.; Zucchini, Walter - In: Journal of empirical finance 19 (2012) 1, pp. 147-161

Persistent link: https://www.econbiz.de/10009615752

Dilated convolutional neural networks for time series forecasting

Borovykh, Anastasia; Bohte, Sander; Oosterlee, Cornelis … - In: The journal of computational finance 22 (2018/2019) 4, pp. 73-101

Persistent link: https://www.econbiz.de/10012042219

Forecasts for international financial series with VMD algorithms

Guo, Wei; Liu, Qingfu; Luo, Zhidan; Tse, Yiuman - In: Journal of Asian economics 80 (2022), pp. 1-14

Persistent link: https://www.econbiz.de/10013431356

Modelling of speculative bubbles and forecasting of market crashes

Gerlach, Jan-Christian - 2021

Persistent link: https://www.econbiz.de/10012510251

Temporal-spatial dependencies enhanced deep learning model for time series forecast

Yang, Hu; Chen, Yu; Chen, Kedong; Wang, Haijun - In: International review of financial analysis 94 (2024), pp. 1-14

Persistent link: https://www.econbiz.de/10014543936

Fathoming the theta method for a unit root process

Thomakos, Dimitrios D.; Nikolopoulos, Konstantinos - In: IMA journal of management mathematics 25 (2014) 1, pp. 105-124

Persistent link: https://www.econbiz.de/10010242798

Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors

Spiliotis, Evangelos; Assimakopoulos, V.; Nikolopoulos, … - In: International journal of production economics 209 (2019), pp. 92-102

Persistent link: https://www.econbiz.de/10012013066

Phase transition in the S&P stock market

Raddant, Matthias; Wagner, Friedrich - 2013

We analyze the stock prices of the S&P market from 1987 to 2012 with the covariance matrix of the firm returns determined in time windows of several years. The eigenvector belonging to the leading eigenvalue (market) exhibits in its long term time dependence a phase transition with an order...

Persistent link: https://www.econbiz.de/10009762492

Stochastic volatility and GARCH : do squared end-of-day returns provide similar information?

Allen, David E. - In: Journal of risk and financial management : JRFM 13 (2020) 9/202, pp. 1-26

volatility forecasting capabilities of more sophisticated models. …

Persistent link: https://www.econbiz.de/10012384599

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