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Forecasting daily and monthly exchange rates with machine learning techniques

Plakandaras, Vasilios; Papadimitriou, Theophilos; … - In: Journal of forecasting 34 (2015) 7, pp. 560-573

Persistent link: https://www.econbiz.de/10011390457

Forecasting the US real house price index

Plakandaras, Vasilios; Gupta, Rangan; Gkonkas, Periklēs; … - In: Economic modelling 45 (2015), pp. 259-267

Persistent link: https://www.econbiz.de/10011334082

Testing exchange rate models in a small open economy : an SVR approach

Papadimitriou, Theophilos; Gkonkas, Periklēs; … - In: Bulletin of applied economics 3 (2016) 2, pp. 9-29

Persistent link: https://www.econbiz.de/10011668838

The informational content of the term spread in forecasting the US inflation rate : a nonlinear approach

Plakandaras, Vasilios; Gkonkas, Periklēs; … - In: Journal of forecasting 36 (2017) 2, pp. 109-121

Persistent link: https://www.econbiz.de/10011729092

Gold against the machine

Plakandaras, Vasilios; Gkonkas, Periklēs; … - In: Computational economics 57 (2021) 1, pp. 5-28

Persistent link: https://www.econbiz.de/10012486908

Forecasting bank credit ratings

Gkonkas, Periklēs; Papadimitriou, Theophilos; … - In: Journal of risk finance : the convergence of financial … 15 (2014) 2, pp. 195-209

Persistent link: https://www.econbiz.de/10010337459

Forecasting energy markets using support vector machines

Papadimitriou, Theophilos; Gkonkas, Periklēs; … - In: Energy economics 44 (2014), pp. 135-142

Persistent link: https://www.econbiz.de/10010457232

Forecasting S&P 500 spikes : an SVM approach

Papadimitriou, Theophilos; Gkonkas, Periklēs; … - In: Digital finance : smart data analytics, investment … 2 (2020) 3/4, pp. 241-258

Persistent link: https://www.econbiz.de/10012416301

Mind the gap : forecasting euro-area output gaps with machine learning

Sofianos, Emmanouil; Gkonkas, Periklēs; Papadimitriou, … - In: Applied economics letters 29 (2022) 19, pp. 1824-1828

Persistent link: https://www.econbiz.de/10013412312

The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate : A Nonlinear Approach

Gogas, Periklis - 2017

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...

Persistent link: https://www.econbiz.de/10012953784