The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks

Emilia Fraszka-Sobczyk () and Aleksandra Zakrzewska ()
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Emilia Fraszka-Sobczyk: University of Lodz
Aleksandra Zakrzewska: University of Lodz

Computational Economics, 2025, vol. 65, issue 5, No 12, 2774 pages

Abstract: Abstract The paper investigates the issue of volatility of stock index returns on the Warsaw Stock Exchange (WIG20 index returns volatility). The purpose of this review is to compare how other stock market indexes as HANG SENG, NIKKEI 225, FTSE 250, DAX, S&P 500 and NASDAQ 100 influance the volatility of WIG20 index returns. The innovation of this work is the usage of a new neural network with three different activation functions to predict future volatility of WIG20 index returns. The input for this network is the last 3 values of WIG20 index returns volatility and the last 3 values of one of the considered foreign index returns volatility. As measurements for the best forecasting performance of neural networks are taken common used forecast errors: ME (mean error), MPE (mean percentage error), MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root mean square error). The study shows that the Polish stock market is mainly influenced by the European and US markets.

Keywords: Stock index returns; Volatility forecasting; Stock index prediction; Neural network; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10662-w

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