Trading and Financial Engineering Algorithms on Stock Market Volatility: A Log-Return Model and GARCH (1, 1)-M

Resvina Situmorang; Marianus Hendrilensio Sanga

doi:10.17977/jabe.v10i2.62646

Trading and Financial Engineering Algorithms on Stock Market Volatility: A Log-Return Model and GARCH (1, 1)-M

Resvina Situmorang, Marianus Hendrilensio Sanga

Abstract

This study examines the effects of algorithmic trading and financial engineering strategies on stock market volatility in Indonesia, as well as how their interaction influences price stability. A quantitative approach is employed using volatility estimation through the GARCH (1, 1)-M model and dynamic panel regression based on the Generalized Method of Moments (GMM). The data were obtained from 40 LQ45 stocks with daily frequency throughout 2024, using indicators of algorithmic trading intensity (order-to-trade ratio) and financial engineering (derivatives contract volume). The results show that both algorithmic trading and financial engineering have a positive and significant impact on market volatility. Furthermore, their interaction amplifies volatility, indicating that the integration of trading technologies and financial innovations creates additional pressure on price stability. Theoretically, these findings reinforce the concept of feedback volatility and the endogeneity of risk in the dynamics of modern markets. From a practical perspective, the results highlight the need to strengthen microstructural risk monitoring systems and to design adaptive regulatory policies to address the complexity arising from the interaction between technology and derivatives in capital markets. This study opens avenues for further research on behavioral dimensions, algorithmic regulation, and systemic risks in an increasingly digital and globally integrated market ecosystem.

Keywords

Algorithmic Trading;Financial Engineering;GARCH (1, 1)-M;Stock Market Volatility

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DOI: http://dx.doi.org/10.17977/jabe.v10i2.62646