Adaptive Volatility Analysis: An Integrated ATR Model for Dynamic Market Conditions

This paper introduces an enhanced Integrated Average True Range (ATR) model for volatility analysis, addressing the limitations of the traditional ATR in responding to dynamic market conditions. Using EUR/USD Forex market data from 2022 to 2024, the proposed model integrates traditional ATR with Rate of Change (ROC) and a volume responsiveness factor to create a more adaptive and real-time indicator. The methodology combines statistical, optimization-based, and correlation-driven approaches to derive coefficients that balance the contributions of these components, supported by a covariance ratio analysis. The results demonstrate the Integrated ATR's superior performance in capturing short-term volatility compared to the Traditional ATR, with improved sensitivity to market fluctuations. This study highlights the model's novelty in synthesizing multiple dimensions of market behavior while acknowledging its complexity and dependence on parameter calibration. The findings offer actionable insights for traders and analysts, with future research suggested to incorporate external factors and adaptive techniques for broader applicability.