Vol. 5 No. 1 (2025): Issue 5
Articles

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

PDF
  • Zuofei Fu,
  • Yiyun Zheng,
  • Jindi Lin
Jindi Lin
University of Illinois Urbana-Champaign
DOI: https://doi.org/10.71070/oaml.v5i1.25

Published 2025-01-26

Keywords

  • Volatility Analysis,
  • Integrated ATR,
  • Forex Market,
  • Rate of Change,
  • Market Dynamics

How to Cite

Fu, Z., Zheng, Y., & Lin, J. (2025). Adaptive Volatility Analysis: An Integrated ATR Model for Dynamic Market Conditions. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.25
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

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.

PDF

References

  1. Andersen, T. G., & Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. International Economic Review, 39(4), 885–905.
  2. Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001). The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 61(1), 43–76.
  3. Bollinger, J. (2001). Bollinger on Bollinger Bands. McGraw-Hill Education.
  4. Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance, 1(2), 223–236.
  5. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007.
  6. Engle, R. F., & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Econometric Theory, 11(1), 122–150.
  7. Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3–56.
  8. Garman, M. B., & Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. The Journal of Business, 53(1), 67–78.
  9. Hansen, P. R., & Lunde, A. (2005). A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889.
  10. Hull, J. (2018). Options, Futures, and Other Derivatives. Pearson.
  11. Liang, Y., Chen, C., Tian, T., & Shu, K. (2023). Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach. Frontiers in Big Data, 5:129.
  12. Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394–419.
  13. Murphy, J. J. (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance.
  14. Patton, A. J. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2), 527–556.
  15. Schwert, G. W. (1989). Why Does Stock Market Volatility Change Over Time? The Journal of Finance, 44(5), 1115–1153.
  16. Taylor, S. J. (1986). Modelling Financial Time Series. Wiley.
  17. Tian, T. (2024). Integrating Deep Learning and Innovative Feature Selection for Improved Short-Term Price Prediction in Futures Markets. Doctoral Dissertation, Illinois Institute of Technology.
  18. Tian, T., Fang, S., Huang, Z., & Wan, X. (2024). TriFusion Ensemble Model: A Physical Systems Approach to Enhancing E-Commerce Predictive Analytics with an Interpretable Hybrid Ensemble Using SHAP Explainable AI. Economic Management & Global Business Studies, 3(1), 15.
  19. Tian, T., Sihan Jia, Jindi Lin, Zichen Huang, Kei O Wang, & Yubing Tang. (2024). Enhancing Industrial Management through AI Integration: A Comprehensive Review of Risk Assessment, Machine Learning Applications, and Data-Driven Strategies. Economics & Management Information, 1–18.
  20. Tian, T., Deng, J., Zheng, B., Wan, X., & Lin, J. (2024). AI-Driven Transformation: Revolutionizing Production Management with Machine Learning and Data Visualization. Journal of Computational Methods in Engineering Applications, 1–18.
  21. Tian, T., Chen, X., Liu, Z., Huang, Z., & Tang, Y. (2024). Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development. Innovations in Applied Engineering and Technology, 3(1), 1–15.
  22. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research.
  23. Tsay, R. S. (2005). Analysis of Financial Time Series. Wiley.
  24. Z. Luo, H. Yan, and X. Pan, ‘Optimizing Transformer Models for Resource-Constrained Environments: A Study on Model Compression Techniques’, Journal of Computational Methods in Engineering Applications, pp. 1–12, Nov. 2023, doi: 10.62836/jcmea.v3i1.030107.
  25. H. Yan and D. Shao, ‘Enhancing Transformer Training Efficiency with Dynamic Dropout’, Nov. 05, 2024, arXiv: arXiv:2411.03236. doi: 10.48550/arXiv.2411.03236.
  26. Y. Liu and J. Wang, ‘AI-Driven Health Advice: Evaluating the Potential of Large Language Models as Health Assistants’, Journal of Computational Methods in Engineering Applications, pp. 1–7, Nov. 2023, doi: 10.62836/jcmea.v3i1.030106.
  27. Y. Gan and D. Zhu, ‘The Research on Intelligent News Advertisement Recommendation Algorithm Based on Prompt Learning in End-to-End Large Language Model Architecture’, Innovations in Applied Engineering and Technology, pp. 1–19, 2024.
  28. D. Zhu, Y. Gan, and X. Chen, ‘Domain Adaptation-Based Machine Learning Framework for Customer Churn Prediction Across Varing Distributions’, Journal of Computational Methods in Engineering Applications, pp. 1–14, 2021.
  29. H. Zhang, D. Zhu, Y. Gan, and S. Xiong, ‘End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection’, Journal of Information, Technology and Policy, pp. 1–17, 2024.
  30. D. Zhu, X. Chen, and Y. Gan, ‘A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction’, Journal of Electronic & Information Systems, vol. 4, no. 1.