EWMA combination of both GARCH and neural networks for the prediction of exchange rate

Title:

EWMA combination of both GARCH and neural networks for the prediction of exchange rate

Creator:

Sohn, So Young and Shin , Won H.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:bad0c3b8-84a1-437f-b740-9a38d22140c9
uuid:bad0c3b8-84a1-437f-b740-9a38d22140c9

Subject:

Forecasting, GARCH, neural networks, EWMA, and combining

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Exchange rate forecasting is an important and challenging task for both academic researchers and business practitioners. Several statistical or artificial intelligence approaches have been applied to forecasting exchange rate. The recent trend to improve the prediction accuracy is to combine individual forecasts in the form of the simple average or weighted average where the weight reflects the inverse of the prediction error. This kind of combination, however, does not reflect the current prediction error more than the relatively old performance. In this paper, we propose a new approach where the forecasting results of GARCH and neural networks are combined based on the weight reflecting the inverse of EWMA of the mean absolute percentage error (MAPE) of each individual prediction model. Empirical study results indicate that the proposed combining method has better accuracy than GARCH, neural networks, and traditional combining methods that utilize the MAPE for the weight.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:4

Harvested from:

CDK

Metadata only:

false