GARCH-FX: A Modular Framework for Stochastic and Regime-Aware GARCH Forecasting

Tony Paul, Nitin (2025): GARCH-FX: A Modular Framework for Stochastic and Regime-Aware GARCH Forecasting.

Abstract

Traditional GARCH models, while robust, are deterministic and their long-horizon forecasts converge to a static mean, failing to capture the dynamic nature of real markets. Conversely, classical stochastic volatility models often introduce significant implementation and calibration complexity. This paper introduces GARCH-FX (GARCH Forecasting eXtension), a novel and accessible framework that augments the classic GARCH model to generate realistic, stochastic volatility paths without this prohibitive complexity. GARCH-FX is built upon the core strength of GARCH—its ability to estimate long-run variance—but replaces the deterministic multi-step forecast with a stochastic simulation engine. It injects controlled randomness through a Gamma-distributed process, ensuring the forecast path is non-smooth and jagged. Furthermore, it incorporates a modular regime-switching multiplier, providing a flexible interface to inject external views or systematic signals into the forecast’s mean level. The result is a powerful and intuitive framework for generating dynamic long-term volatility scenarios. By separating the drivers of mean-level shifts from local stochastic behavior, GARCHFX aims to provide a practical tool for applications requiring realistic market simulations, such as stress-testing, risk analysis, and synthetic data generation.

• Item Type: MPRA Paper
• Language: English
• Keywords: Stochastic Volatility Forecasting, GARCH Extensions, Regime-Switching Volatility, Gamma-Distributed, Volatility, Volatility Forecast Uncertainty, Nonlinear GARCH Models, Stochastic Vol Forecast, Financial Time Series, Heteroskedasticity Dynamics, Gamma Noise in Volatility
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling

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