This reference project is based on an MQL5 article describing a native Sequential Least Squares Programming (SLSQP) optimizer for constrained nonlinear optimization and its integration into an existing conditional volatility library intended to mirror Python's `arch` module behavior.
This project documents an MQL5 implementation approach for asymmetric volatility modeling, extending a prior ARCH/GARCH library with support for:
- GJR-GARCH
- TARCH
The article explains why asymmetric volatility matters in financial time series, especially under the leverage effect, and shows how these models can be integrated into an MQL5 volatility framework for fitting, forecasting, and comparative analysis across asset classes.
This project documents and organizes the concepts presented in an MQL5 article about a native library for univariate volatility modeling and forecasting. The article describes a modular framework inspired by Python's `arch` package.