A Heisenberg Uncertainty Principle-Based Volatility Approach for WTI Price Dynamics

Abstract

This study is designed to fill the gap between classical financial models and modern market requirements by applying quantum mechanics principles to the WTI (West Texas Intermediate) crude oil price dynamics. Specifically, the Heisenberg Uncertainty Principle is explored with both a conceptual framework and an applicative real-time scenario to measure and model the volatility of WTI (West Texas Intermediate) crude oil prices. In this context, a novel volatility indicator, which is derived from a quantum mechanics-inspired methodology, centered on the Heisenberg formulation, is proposed. Therefore, the relationship between price position (Δx) and momentum (Δp) is analyzed to demonstrate how this principle, which is formulated as Δ𝑥⋅Δ𝑝≥ℎ/4𝜋 can be adapted to capture the uncertainty and fluctuations of WTI price actions. The recommended volatility indicator offers a unique approach for inquiring price behavior, incorporating both quantum mechanics principles and financial market dynamics. This approach not only enhances predictive accuracy but also provides a deeper insight into market patterns by addressing the chaotic and interconnected nature of modern financial systems. Furthermore, the findings pave the way for developing more advanced trading and risk management strategies tailored to volatile energy markets like WTI.

Keywords

• Quantum Mechanics
• Heisenberg Uncertainty Principle
• Financial Markets Volatility Indicator
• WTI Price Dynamics
• Price Action Analysis

WTI Fiyat Hareketleri için Heisenberg Tabanlı Bir Volatilite Göstergesi

Abstract

Bu çalışma, klasik finansal modeller ile modern piyasa gereklilikleri arasındaki boşluğu doldurmayı amaçlamakta ve bunun için kuantum mekaniği prensiplerini WTI (West Texas Intermediate) ham petrol fiyat dinamiklerine uygulamaktadır. Özellikle, Heisenberg Belirsizlik İlkesi hem kavramsal bir çerçeve hem de gerçek zamanlı bir uygulama senaryosu aracılığıyla ele alınarak WTI fiyat oynaklığı ölçülmekte ve modellenmektedir. Bu bağlamda, Heisenberg formülasyonuna (Δ𝑥⋅Δ𝑝 ≥ ℎ/4𝜋) dayanan kuantum mekaniği esaslı yeni bir volatilite göstergesi önerilmektedir. Bu kapsamda, fiyat pozisyonu (Δx) ile momentum (Δp) arasındaki ilişki incelenerek bu ilkenin WTI fiyat hareketlerindeki belirsizlik ve dalgalanmaları nasıl yakalayabileceği gösterilmektedir. Önerilen volatilite göstergesi, fiyat davranışını incelemek için kuantum mekaniği prensipleri ile finansal piyasa dinamiklerini bir araya getiren özgün bir yaklaşım sunmaktadır. Bu yaklaşım, yalnızca tahmin doğruluğunu artırmakla kalmayıp, modern finansal sistemlerin kaotik ve birbirine bağlı yapısını da dikkate alarak piyasa örüntülerine daha derin bir bakış açısı sağlamaktadır. Ayrıca, elde edilen bulgular WTI gibi yüksek oynaklığa sahip enerji piyasalarına yönelik daha gelişmiş alım-satım ve risk yönetimi stratejilerinin geliştirilmesine de zemin hazırlamaktadır.

Keywords

• Kuantum Mekaniği
• Heisenberg Belirsizlik İlkesi
• Finansal Piyasalar Volatilite Göstergesi
• WTI Fiyat Dinamikleri
• Fiyat Hareketi Analizi

References

1. Arioli, G., & Valente, G. (2021). What is really quantum in quantum econophysics. Philosophy of Science, 88(4), 665-685. https://ddc449fe97e72df15faf96b8d7d4f5facfaa9c92.vetisonline.com/c/smi7rf/viewer/pdf/kydnjdzf3v adresinden alındı
2. Atadoga, A., Ike, C. U., Asuzu, O. F., Ayinla, B. S., Ndubuisi, N. L., & Adeleye, R. A. (2024). The intersection of AI and quantum computing in financial markets: A critical review. Computer Science & IT Research Journal, 5(2), 461-472. doi:10.51594/csitrj.v5i2.816
3. Baaquie, B. (2013). Financial modeling and quantum mathematics. Computers & Mathematics with Applications, 65(10), 1665-1673. doi:10.1016/j.camwa.2013.01.025
4. Caginalp, G., & Laurent, H. (1998). The predictive power of price patterns. Applied Mathematical Finance, (5), 181-205.
5. Choustova, O. (2009). Quantum probability and financial market. Information Sciences, 179(5), 478-484. doi:10.1016/j.ins.2008.07.001
6. Choustova, O. A. (2007). Quantum Bohmian model for financial market. Physica A: Statistical Mechanics and its Applications, 374(1), 304-314. doi:10.1016/j.physa.2006.07.029
7. FRED. (2025). Crude Oil Prices: West Texas Intermediate (WTI) - Cushing, Oklahoma (DCOILWTICO). Retrieved from https://fred.stlouisfed.org/series/DCOILWTICO
8. Griffin, P., & Sampat, R. (2021). Quantum computing for supply chain finance. 2021 IEEE International Conference on Services Computing (SCC) (456-459). Chicago, IL, USA. https://c85689232ea394a8dc08a512c1f46793a2397178.vetisonline.com/document/9592468 adresinden alındı

9. Helliwell, T., & Sahakian, V. (2021). Modern classical mechanics. California: Cambridge University Press.
10. IG. (2025). Oil Us crude price chart. Retrieved from https://www.ig.com/en/commodities/markets-commodities/us-light-crude
11. Ingber, L. (2021). Hybrid classical-quantum computing: Applications to statistical mechanics of financial markets. International Interdisciplinary Scientific Conference “Digitalisation and Sustainability for Development Management: Economic, Social, and Ecological Aspects”. 307. E3S Web of Conferences. doi:10.1051/e3sconf/202130704001
12. Iovane, G., Briscione, A., & Benedetto, E. (2021). Financion: A quantum approach to financial market modelling. Journal of Statistics and Management Systems, 24(5), 1127-1149. doi:10.1080/09720510.2021.1930665
13. Kırer, H., & Eren, E. (2015). Historical perspective on the relationship between economics and physics. Ekonomi-Tek, 4(2), 25-60. https://dergipark.org.tr/en/download/article-file/1729923
14. Kuzu, E., & Tanrıöven, C. (2017). Application of the Heisenberg uncertainty. Gazi Üniversitesi Sosyal Bilimler Dergisi, 4(11), 370-393. https://dergipark.org.tr/en/download/article-file/397088
15. Kuzu, E., Süsay, A., & Tanrıöven, C. (2021). A model study for calculation of the temperatures of major stock markets in the world with the quantum simulation and determination of the crisis periods. Physica A: Statistical Mechanics and its Applications, 585. https://doi.org/10.1016/j.physa.2021.126417
16. Messiah, A. (2014). Quantum mechanics (Vol. 2). New York: Dover Publications.
17. Nastasiuk, V. (2014). Emergent quantum mechanics of finances. Physica A: Statistical Mechanics and its Applications, 403, 148-154. doi:10.1016/j.physa.2014.02.037
18. O Farfar, D. (2002). Louis Bachelier. Scotland. https://mathshistory.st-andrews.ac.uk/Biographies/Bachelier/
19. Sarkissian, J. (2016). Quantum theory of securities price formation in financial markets. doi:10.2139/ssrn.2765298
20. Vasileiou, E. (2021). Are markets efficient? A quantum mechanics view. Journal of Behavioral Finance, 22(2), 214-220. doi:10.1080/15427560.2020.1772260
21. WorldQuant Perspectives. (2017, 6 30). Wall Street on a Lattice: Finance Meets Physics. Old Greenwich, United Kingdom. https://www.weareworldquant.com/media/1455/063017_wq-perspectives_wall-st-finance-meets-physics-v2.pdf
22. Zhang, C., & Huang, L. (2010, December 15). A quantum model for the stock market. Physica A: Statistical Mechanics and its Applications, 389(24), 5769-5775. doi:10.1016/j.physa.2010.09.008