Statistical and RBF NN models: Providing forecasts and risk assessment

Marcek, Milan

doi:10.7327/cerei.2009.12.02

Cited by 2 publications

(4 citation statements)

References 13 publications

(8 reference statements)

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“…We looked to determine the maximum lag for which the PACF coefficient was statistically significant and the lag given the minimum AIC. According to these criterions the specification and estimation was performed by means of the R2.6.0 software (available at http://cran.r-project.org) and resulted into best ARIMA(1,1,1)+PGARCH (1,1) model in the form of mean and variance equations respectively as follows (1,1,1) model. To test the adequacy of the developed model we applied the standardized residuals (3.4) which are calculated from the last two equations on the data set Α , to the BDS test.…”

Section: Some Arch-garch Models For Financial Datamentioning

confidence: 99%

“…The same data used for estimating the linear ARIMA(1,1,1)+PGARCH (1,1) model was also used to train neural network. The variables forming the right hand of the model given by Eq.…”

Section: Neural Approachmentioning

confidence: 99%

“…Though promising results have been achieved with both approaches, for the chaotic financial time markets a purely linear (statistical) approach for modeling relationships does not reflect the reality. For example, if investors do not react to a small change in exchange rate at the first instance, but after crossing a certain interval or threshold react, then a non-linear relationship between (1). The neural approach based on granular RBF network not only detected the functionality between the underlying variables and the EUR/USD exchange rates as well as the short-run dynamics.…”

Section: Empirical Comparisonmentioning

confidence: 99%

“…One of the approaches to understanding uncertainty in forecasting models is understanding it as the standard deviation σ of the forecasted quantity used e.g. in , [1], [2] and so on. The standard deviation as a degree of uncertainty or risk of forecasted quantity values estimates is proportional to the statistical degree of accuracy of the forecast defined as Root Mean Square Error (RMSE) or MAPE (Mean Absolute Percentage Error).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Managerial Forecasting System Based on RBF Neural Network for Financial Data

Marček¹

2013

Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET-2013)

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Forecasting systems are applied which are based on the latest statistical theory and artificial neural networks. The impact of these methods to risk reduction is judged in managerial decision-making. The fundamental question arises whether non-linear methods like neural networks can help modeling any non-linearities being inherent within the estimated statistical model. The proposed novel modeling approach is applied to high frequency time series of EUR/USD exchange rates (EUR currency against the US dollar. Our results show that the proposed neural approach achieves better forecast accuracy on the validation dataset than most available statistical techniques.

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Section: Some Arch-garch Models For Financial Datamentioning

confidence: 99%

“…The same data used for estimating the linear ARIMA(1,1,1)+PGARCH (1,1) model was also used to train neural network. The variables forming the right hand of the model given by Eq.…”

Section: Neural Approachmentioning

confidence: 99%

Section: Empirical Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managerial Forecasting System Based on RBF Neural Network for Financial Data

Marček¹

2013

Proceedings of the 2013 International Conference on Information, Business and Education Technology (ICIBET-2013)

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Risk Scenes of Managerial Decision-Making with Incomplete Information: An Assessment in Forecasting Models Based on Statistical and Neural Networks Approach

Marček

2013

JRACR

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The paper is concerned with measuring of risks in managerial decision-making. It builds upon the uncertainty of economic information, which is converted into the concept of risk expressed in terms of probability and using confidence intervals and standard deviations of the predicted quantities. The paper explains the relation of a degree of risk expressed by the classical information measure, bit, by the concept of confidence intervals, or possibly by the standard deviation. Forecasting models are applied which are based on a statistical theory and a neural approach. The aim is also to examine whether potentially highly non-linear neural network models outperforms the advanced statistical methods and better reduce risk in managerial decision-making, or they yield competitive results. A method for finding the forecasting horizon within which the risk is minimal is also presented.

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Statistical and RBF NN models: Providing forecasts and risk assessment

Abstract: Abstract

Cited by 2 publications

References 13 publications

Managerial Forecasting System Based on RBF Neural Network for Financial Data

Managerial Forecasting System Based on RBF Neural Network for Financial Data

Risk Scenes of Managerial Decision-Making with Incomplete Information: An Assessment in Forecasting Models Based on Statistical and Neural Networks Approach

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