The Potential of Hybrid GARCH Models Implemented in R for High-Frequency Trading Risk Management_Vol. 2 No. 5 (JSE 2025)_Journal of Statistics and Economics (ISSN: 3005-5733)

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The Potential of Hybrid GARCH Models Implemented in R for High-Frequency Trading Risk Management

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DOI: https://doi.org/10.62517/jse.202511518

Author(s)

Ruichao Luo

Affiliation(s)

University College Dublin, Singapore Campus, Singapore *Corresponding Author

Abstract

High-frequency trading (HFT) tackles financial market volatility and risk management with high-level algorithms and trading velocity of milliseconds. This paper provides an overview of the potential application of hybrid GARCH models in managing the risk of HFT, systematically exploring the roots of the fundamental GARCH model and its core application in modeling volatility. With the addition of machine learning techniques, nonlinear dynamics and long-range dependence prevail in hybrid GARCH models overwhelmingly, extending the accuracy of risk measures. The R statistical language, with deep ecosystems of statistical packages, provides model implementation and forecasting with the computational efficiency of high-frequency worlds necessary. Hybrid GARCH models prevail with clear advantages in processing microstructure noise and asymmetric shocks, with efficient tools for dynamic decisions in trading. This paper provides an integrated picture of their core position in theoretical and applied research, with an all-inclusive framework of managing the risk of HFT.

Keywords

High-Frequency Trading; Hybrid GARCH; Risk Measurement; R Language; Volatility Modeling

References

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