Discussing the article: "Building Volatility models in MQL5 (Part I): The Initial Implementation"
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Check out the new article: Building Volatility models in MQL5 (Part I): The Initial Implementation.
In this article, we present an MQL5 library for modeling volatility, designed to function similarly to Python's arch package. The library currently supports the specification of common conditional mean (HAR, AR, Constant Mean, Zero Mean) and conditional volatility (Constant Variance, ARCH, GARCH) models.
The library presented here is inspired by Python’s arch package, a specialized toolkit for financial econometrics focusing on Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized ARCH (GARCH) models. While the primary function of the arch package is to implement various volatility models, it also provides diverse options for modeling the mean equation—such as constant mean, zero mean, or autoregressive (AR) models. Furthermore, users can specify different distributions for standardized residuals, including Normal, Student's t, and Skewed Student's t-distributions. Our objective is to natively reproduce this functionality within MQL5.
The architecture of this native implementation is modular, decoupling the mean process from the volatility process and the error distribution. Consequently, a model is a composition of these three distinct components. The mean process serves as the primary component to which the others are attached; notably, the joint estimation of all parameters is managed exclusively through this central component. Each element is implemented as a base class, with subclasses representing specific variations.
Author: Francis Dube