Discussing the article: "Building Volatility Models in MQL5 (Part II): Implementing GJR-GARCH and TARCH in MQL5"
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Check out the new article: Building Volatility Models in MQL5 (Part II): Implementing GJR-GARCH and TARCH in MQL5.
The article implements GJR-GARCH and TARCH in an MQL5 volatility library and explains why asymmetry improves on standard ARCH/GARCH. It covers model formulation, parameterization, and usage through derived classes and scripts. Readers get code examples for calibration and one-step-ahead forecasting on real data to support risk and diagnostics.
In the first part of this series, we implemented an MQL5 volatility library. We built conditional mean processes such as AR and HAR and paired them with standard ARCH/GARCH volatility frameworks. While effective for clustering, these traditional models are held back by their symmetric nature. They treat market rallies and market crashes as having an identical impact on future risk.
In this installment, we address this limitation by augmenting our library with support for asymmetric volatility processes, specifically the GJR-GARCH and TARCH models. In addition to defining these models, we explain why asymmetry is necessary from both mathematical and behavioral perspectives and how these frameworks improve on ARCH/GARCH. Finally, the article will demonstrate how to implement these asymmetric processes within full volatility models in MQL5 using real-world data. Readers will obtain ready-to-use code that captures the panic factor in financial markets. This provides a more robust toolset for risk management and directional analysis.
Author: Francis Dube