Volatility forecast with artificial neural networks as univariate time series, with examples from stock market indexes
Article Sidebar
Main Article Content
Abstract
The tools that are offered to investors in financial markets are fluctuating. As this fluctuation causes losses as well as earnings, it is characterised as a risk for the investor. Especially, fluctuations that may occur in globally important markets and financial instruments have great significance, not just for investor but also for the global economy. Volatility, as a measure of fluctuations taking place in markets, is often used particularly by investors and all economic actors. Therefore, in recent years, future volatility predictions have gained importance. The aim of this research is forecasting future volatility values using the historical data of S&P 500, FTSE 100 and NIKKEI 225 stock market indexes. The progress of historical volatility values in years is presented and generated univariate time series is modelled with artificial neural networks. Future forecasts are done with the obtained model and results are interpreted.
Keywords: Artificial neural networks, volatility, time series analysis, stock market indexes.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The Global Journal of Business Economics and Management: Current Issues is an open-access journal. The copyright holder is the author or authors. Licensee: Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi, North Nicosia, Cyprus. All articles can be downloaded free of charge. Articles published in the Journal are Open-Access articles distributed under the CC-BY license [Attribution 4.0 International (CC BY 4.0)].