Modelling financial time series with threshold nonlinearity

Samuel Tabot Enow
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Samuel Tabot Enow: Research Associate, The IIE VEGA School

International Journal of Research in Business and Social Science (2147-4478), 2025, vol. 14, issue 8, 152-156

Abstract: Financial markets are a system of complex price dynamics that are often influenced by various nonlinear factors. Traditional linear models such as Autoregressive Moving Average also known as ARMA and the GARCH model often fail to capture the inherent nonlinearities observed in financial market returns and trading volumes which poses significant problems in modelling financial time series. The aim of this study was to investigates the role of threshold nonlinearity in modelling financial market returns and trading volume. Daily share prices and trading volumes from the S&P 500 and the Nasdaq indices spanning from January 2000 to December 2020 were used as the sample financial markets. The findings revealed that the threshold model improves predictive performance over traditional linear models, highlighting the importance of incorporating nonlinearity for understanding the dynamics of financial markets. A threshold autoregressive model was proposed to account for sudden shifts in market conditions and nonlinear dependencies between returns and trading volumes. Key Words: Threshold Nonlinearity, Returns, Trading Volume, Nonlinear Models, TAR Model

Date: 2025
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