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Scenarios of the Romanian GDP Evolution with Neural Models
Saman, Corina - 2014
This paper aims to explore the nonlinear relation between investments and GDP. The method of neural network is used to … construct two nonlinear models of GDP in relation to domestic investments, foreign direct investments and real interest rate … accuracy may be capturing more fundamental non-linearities between investment and financial variables and the real output for a …
Persistent link: https://www.econbiz.de/10013059876
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Out-of-Sample Forecasting Performance of a Robust Neural Exchange Rate Model of RON/USD
Saman, Corina - 2016
This paper aims to explore the forecasting accuracy of RON/USD exchange rate structural models with monetary fundamentals. I used robust regression approach for constructing robust neural models less sensitive to contamination with outliers and I studied its predictability on 1 to 6-month...
Persistent link: https://www.econbiz.de/10013001999
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OUT-OF-SAMPLE FORECASTING PERFORMANCE OF A ROBUST NEURAL EXCHANGE RATE MODEL OF RON/USD
SAMAN, Corina - In: Journal for Economic Forecasting (2015) 1, pp. 93-106
This paper aims to explore the forecasting accuracy of RON/USD exchange rate structural models with monetary fundamentals. I used robust regression approach for constructing robust neural models less sensitive to contamination with outliers and I studied its predictability on 1 to 6-month...
Persistent link: https://www.econbiz.de/10011265554
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FuNVol : A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs
Choudhary, Vedant; Jaimungal, Sebastian; Bergeron, Maxime - 2023
Here, we introduce a new approach for generating sequences of implied volatility (IV) surfaces across multiple assets that is faithful to historical prices. We do so using a combination of functional data analysis and neural stochastic differential equations (SDEs) combined with a probability...
Persistent link: https://www.econbiz.de/10014254286
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The benefits of the Velvet Revolution in Armenia : estimation of the short-term economic gains using deep neural networks
Nahapetyan, Yervand - In: Central European economic journal 6 (2019) 53, pp. 286-303
models using an ensembling mechanism. As a result, CNN overperformed the rest of the models. The CNN simulation on post …-revolutionary data indicates that during the period 2018-Q2-2019-Q1, Armenia gained approximately 850 million EUR in terms of GDP, thanks …
Persistent link: https://www.econbiz.de/10012303300
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Application of Feed-Forward Neural Networks Autoregressive Models with Genetic Algorithm in Gross Domestic Product Prediction
Giovanis, Eleftherios - 2010
In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric...
Persistent link: https://www.econbiz.de/10013137781
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Application of Feed-Forward Neural Networks Autoregressive Models in Gross Domestic Product Prediction
Giovanis, Eleftherios - 2010
algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically …
Persistent link: https://www.econbiz.de/10013137783
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Applications of Neural Network Radial Basis Function in Economics and Financial Time Series
Giovanis, Eleftherios - 2010
In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that...
Persistent link: https://www.econbiz.de/10013138753
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Feed-Forward Neural Networks Regressions with Genetic Algorithms : Applications in Econometrics and Finance
Giovanis, Eleftherios - 2010
In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates...
Persistent link: https://www.econbiz.de/10013138757
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Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns
Bildirici, Melike - 2013
The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...
Persistent link: https://www.econbiz.de/10013090501