Modelling, forecasting and trading with a new sliding window approach: the crack spread example

DOI10.1080/14697688.2016.1211796zbMath1400.91550OpenAlexW2520186191MaRDI QIDQ4554253

Christian Dunis, Andreas Karathanasopoulos, Samer Khalil

Publication date: 13 November 2018

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/14697688.2016.1211796

zbMATH Keywords

GARCHARMAspread tradingHONNMLP neural networkPSO RBF neural networksliding window trainingthreshold confirmation filters

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Portfolio theory (91G10)

Cites Work

• Unnamed Item
• Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations
• A threshold cointegration analysis of asymmetric price transmission from crude oil to gasoline prices
• Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization
• Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms

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