Asymmetric Effects of Global Financial, Oil, and Currency Shocks on the Petrochemical Sector: A NARDL Approach with Turkish Evidence

Abstract

This study analyzes the asymmetric effects of global financial stress, exchange rate volatility, and energy market uncertainty on the Turkish stock market during the period 2010-2025. Specifically, the Chicago Board Options Exchange (CBOE) Volatility Index (VIX) is used as an indicator of financial stress, the CBOE Crude Oil Volatility Index (OVX) as an indicator of oil market uncertainty, and the U.S. Dollar Index (DXY) as an indicator of exchange rate pressure. The study distinguishes itself from the existing literature by focusing on the BIST Chemical, Petroleum, and Plastic Index (XKMYA) through a Nonlinear Autoregressive Distributed Lag (NARDL) framework. To account for structural changes, dummy variables representing significant political and economic events have been included in the model. The findings reveal that negative shocks in the DXY index create a significant and asymmetric long-term impact on the XKMYA. Furthermore, while the lagged negative values of the OVX index show significance, the VIX index and the COVID-19 dummy variable do not yield statistically significant results. This information provides important insights for policymakers and investors in developing sector-specific risk management strategies.

Keywords

• Financial Stress
• Asymmetric Impact
• Oil Volatility
• Global Uncertainty
• BIST
• Nonlinear Delayed Distributed Autoregressive Model (NARDL)

Küresel Finansal, Petrol ve Döviz Şoklarının Petrokimya Sektörüne Asimetrik Etkileri: Türkiye Verileriyle NARDL Yaklaşımı

Öz

Bu çalışma, 2010-2025 dönemini kapsayan küresel finansal stres, döviz kuru oynaklığı ve enerji piyasası belirsizliğinin Türk hisse senedi piyasası üzerindeki asimetrik etkilerini analiz etmektedir. Özellikle, Chicago Board Options Exchange (CBOE) Volatilite Endeksi (VIX) finansal stresin, CBOE Ham Petrol Volatilite Endeksi (OVX) petrol piyasası belirsizliğinin ve ABD Doları Endeksi (DXY) döviz kuru baskısının bir göstergesi olarak kullanılmaktadır. Çalışma, Doğrusal Olmayan Otoregresif Dağıtılmış Gecikme (NARDL) çerçevesi aracılığıyla BIST Kimya, Petrol ve Plastik Endeksi'ne (XKMYA) odaklanarak mevcut literatürden ayrılmaktadır. Yapısal değişimleri hesaba katmak için, önemli siyasi ve ekonomik olayları temsil eden kukla değişkenler modele dahil edilmiştir. Bulgular, DXY endeksindeki olumsuz şokların XKMYA üzerinde önemli ve asimetrik bir uzun vadeli etki yarattığını ortaya koymaktadır. Ayrıca, OVX endeksinin gecikmeli negatif değerleri anlamlılık gösterirken, VIX endeksi ve COVID-19 kukla değişkeni istatistiksel olarak anlamlı sonuçlar vermemektedir. Bu bilgiler, politika yapıcılar ve yatırımcılar için sektör özelinde risk yönetimi stratejileri geliştirme konusunda önemli çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Finansal Stres
• Asimetrik Etki
• Petrol Volatilitesi
• Küresel Belirsizlik
• BİST
• Doğrusal Olmayan Gecikmesi Dağıtılmış Otoregresif Model (NARDL)

Kaynakça

1. Abdullah, G. I., & Abbas, R. S. (2022). Financial Development and Economic Growth in Iraq: The Role of Financial Institutions using NARDL. World Economics and Finance Bulletin, 15, 104-121.
2. Adrangi, B., & Chatrath, A. (2022). Dynamic Responses of Major Pacific Rim Emerging Equity Markets to the US Crude Oil Fear Index (OVX). Bulletin of Applied Economics, 9(1), 51-84. https://doi.org/10.47260/bae/915
3. Aimer, N., & Lusta, A. (2021). The Asymmetric Impact of Oil Price Shocks on Economic Uncertainty: Evidence from the Asymmetric NARDL Model. OPEC Energy Review, 45(4), 393-413. https://doi.org/10.1111/opec.12214
4. Aliu, F., Asllani, A. and Hašková, S. (2024), "The Impact of Bitcoin on Gold, The Volatility Index (VIX), and Dollar Index (USDX): Analysis Based on VAR, SVAR, And Wavelet Coherence". Studies in Economics and Finance, 41(1), 64-87. https://doi.org/10.1108/SEF-04-2023-0187
5. Allen, D. E., & McAleer, M. (2021). A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes. Risks, 9(11), 195. https://doi.org/10.3390/risks9110195
6. Bannerjee, S. S., Pillai, R., Tabash, M. I., & Al-Absy, M. S. M. (2025). Unveiling Inter-Market Reactions to Different Asset Classes/Commodities Pre- and Post-COVID-19: An Exploratory Qualitative Study. Economies, 13(3), 66. https://doi.org/10.3390/economies13030066
7. Bouteska, A., Hassan, M. K., Rashid, M., & Bilgin, M. H. (2024). The Dynamics of Bonds, Commodities and Bitcoin Based on NARDL Approach. The Quarterly Review of Economics and Finance, 94, 58-70. https://doi.org/10.1016/j.qref.2023.12.013
8. Ceyhan, İ. F. (2025). Küresel ve Türkiye'ye Özgü Jeopolitik Risklerin BİST Endeksleri Üzerindeki Etkisinin Araştırılması. Business & Management Studies: An International Journal, 13(3), 1376.

9. Chittineni, J. (2022). The Nonlinear Asymmetric Relationship Among Implied Volatility Indices Of Indian Stock Market, Gold, And Oil: Evidences From NARDL Model. Indian Journal of Finance and Banking, 9(1), 1-9. https://doi.org/10.46281/ijfb.v9i1.1540
10. Cont, R. (2001). Empirical Properties of Asset Returns: Stylized Facts and Statistical Issues. Quantitative Finance, 1(2), 223–236. https://doi.org/10.1080/713665670
11. Das, N., & Gangopadhyay, P. (2023). Did Weekly Economic Index and Volatility Index Impact US Food Sales During The First Year Of The Pandemic?. Finance Innov, 9, 57. https://doi.org/10.1186/s40854-023-00460-y
12. Demir, E., Simonyan, S., García-Gómez, C. D., & Lau, C. K. M. (2021). The Asymmetric Effect of Bitcoin on Altcoins: Evidence From The Nonlinear Autoregressive Distributed Lag (NARDL) Model. Finance Research Letters, 40, 101754. https://doi.org/10.1016/j.frl.2020.101754
13. Demirer, R., Ferrer, R., & Shahzad, S. J. H. (2020). Oil Price Shocks, Global Financial Markets and Their Connectedness. Energy Economics, 88, 104771. https://doi.org/10.1016/j.eneco.2020.104771
14. Dickey, D. A., & Fuller, W. A. (1979). Distribution of The Estimators for Autoregressive Time Series with A Unit Root. Journal of the American Statistical Association, 74(366a), 427-431. https://doi.org/10.1080/01621459.1979.10482531
15. Eidi, M., Hojabr Kiani, K., Rajaei, Y., & Rahimzadeh, A. (2019). Estimation of Money Demand Function in Iran Including Households Religious Costs: A NARDL Approach. International Journal of Finance & Managerial Accounting, 4(15), 127-132.
16. Erdoğan, L., Ceylan, R., & Abdul-Rahman, M. (2022). The Impact of Domestic and Global Risk Factors On Turkish Stock Market: Evidence from the NARDL Approach. Emerging Markets Finance and Trade, 58(7), 1961-1974. https://doi.org/10.1080/1540496X.2021.1949282
17. Göksu, S., & Balkı, A. (2023). ARDL ve NARDL Eşbütünleşme Analizleri: Adım adım EViews Uygulaması. Bursa: Dora Yayıncılık.
18. Hakkio, C. S., & Keeton, W. R. (2009). Financial Stress: What Is It, How Can It Be Measured, and Why Does, It Matter. Economic Review, 94(2), 5-50.
19. Ilgın, K. S., & Güney, S. (2024) The Effects of Price and Volatility Indices of Strategic Commodities on Stock Markets: A Study on BRICS Economies. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(3), 23-47. https://doi.org/10.53443/anadoluibfd.1389265
20. Jareño, F., de la O González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and Gold Price Returns: A Quantile Regression and NARDL Analysis. Resources Policy, 67, 101666. https://doi.org/10.1016/j.resourpol.2020.101666
21. Jobling, A., & Jamasb, T. (2017). Price Volatility and Demand For Oil: A Comparative Analysis of Developed and Developing Countries. Economic Analysis and Policy, 53, 96-113. https://doi.org/10.1016/j.eap.2017.01.001
22. Kanwal, A., Khalid, S., & Alam, M. Z. (2023). Analyzing the Asymmetric Effects of Green Finance, Financial Development and FDI on Environment Sustainability: New Insights from Pakistan Based Non-Linear ARDL Approach. iRASD Journal of Economics, 5(3), 625-644. https://doi.org/10.52131/joe.2023.0503.0151
23. Khanalizadeh, B., Rahimzadeh, A., Dalmanpour, M., & Afsharirad, M. (2024). Investigating The Impact of Financial, Economic, and Political Risks and Economic Complexity on Sukuk Market Development (NARDL Approach). Iranian Journal of Finance, 8(2), 105-130. https://doi.org/10.61186/ijf.2024.429303.1447
24. Kilian, L., & Park, C. (2009), The Impact of Oil Price Shocks on The U.S. Stock Market. International Economic Review, 50(4), 1267-1287. https://doi.org/10.1111/j.1468- 2354.2009.00568.x
25. Lee, J., & Strazicich, M. C. (2003). Minimum Lagrange Multiplier Unit Root Test With Two Structural Breaks. Review of Economics and Statistics, 85(4), 1082-1089. https://doi.org/10.1162/003465303772815961
26. Liang, C. C., Troy, C., & Rouyer, E. (2020). US Uncertainty and Asian Stock Prices: Evidence from the Asymmetric NARDL Model. The North American Journal of Economics and Finance, 51, 101046. https://doi.org/10.1016/j.najef.2019.101046
27. Liu, Y. (2025). Whether Fintech, Natural Resources, Green Finance and Environmental Tax Affect CO2 Emissions in China? A Step Towards Green Initiatives. Energy, 320, 135181. https://doi.org/10.1016/j.energy.2025.135181
28. Long, S., Pei, H., Tian, H., & Lang, K. (2021). Can Both Bitcoin and Gold Serve as Safe-Haven Assets?-A Comparative Analysis Based on the NARDL Model. International Review of Financial Analysis, 78, 101914. https://doi.org/10.1016/j.irfa.2021.101914
29. Mert, M., & Çağlar, A. E. (2023). Eviews ve Gauss Uygulamalı Zaman Serileri Analizi, (2. bs). Ankara: Detay Yayıncılık.
30. Mili, M., Al Amoodi, A. Y., & Bawazir, H. (2024). The Asymmetric Effect of COVID-19 on Investor Sentiment: Evidence from NARDL Model. Review of Behavioral Finance, 16(1), 60-84. https://doi.org/10.1108/RBF-02-2022-0068
31. Nakhli, M. S., & Gaies, B. (2021). Political Risk and Financial Development in Nigeria: Can Credit Buy Social Peace?: Evidence From NARDL Approach. The Quarterly Review of Economics and Finance, 82, 55-62. https://doi.org/10.1016/j.qref.2021.07.004
32. Narayan, P. K. (2005). The Saving and Investment Nexus for China: Evidence from Cointegration Tests. Applied Economics, 37(17), 1979-1990. https://doi.org/10.1080/00036840500278103
33. Narayan, P. K., & Narayan, S. (2007). Modelling Oil Price Volatility. Energy Policy, 35(12), 6549-6553. https://doi.org/10.1016/j.enpol.2007.07.020
34. Neog, Y., & Yadava, A. K. (2020). Nexus Among CO2 Emissions, Remittances, and Financial Development: A NARDL Approach For India. Environmental Science and Pollution Research, 27(35), 44470-44481. https://doi.org/10.1007/s11356-020-10198-0
35. Nittayakamolphun, P., Bejrananda, T., & Pholkerd, P. (2024). Asymmetric Effects of Uncertainty and Commodity Markets on Sustainable Stock in Seven Emerging Markets. Journal of Risk and Financial Management, 17(4), 155. https://doi.org/10.3390/jrfm17040155
36. Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds Testing Approaches to the Analysis of Level Relationships. Journal of Applied Econometrics, 16(3), 289-326. https://doi.org/10.1002/jae.616
37. Phillips, P. C., & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335
38. Rahman, Z. U., & Ahmad, M. (2019). Modeling the Relationship Between Gross Capital Formation and CO 2 (A) Symmetrically in the Case of Pakistan: An Empirical Analysis Through NARDL Approach. Environmental Science and Pollution Research, 26, 8111- 8124. https://doi.org/10.1007/s11356-019-04254-7
39. Rehman, M. U., Nautiyal, N., Zeitun, R., & Vo, X. V. (2025). The Temporal Variability in the Returns of Socially Responsible Funds to Structural Oil Shocks. The North American Journal of Economics and Finance 102366. https://doi.org/10.1016/j.najef.2025.102366
40. Sam, C. Y., McNown, R., & Goh, S. K. (2019). An Augmented Autoregressive Distributed Lag Bounds Test For Cointegration. Economic Modelling, 80, 130-141. https://doi.org/10.1016/j.econmod.2018.11.001
41. Sama, J. M. S., Nna, T. P. N., Sapnken, F. E., & Tamba, J. G. (2025). The Role of Political Stability on Crude Oil Production in Cameroon: Evidence from the ARDL and NARDL Models. Resources Policy, 103, 105563. https://doi.org/10.1016/j.resourpol.2025.105563
42. Shin, Y., Yu, B., & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In R. C. Sickles & W. C. Horrace (Eds.), Festschrift in honor of Peter Schmidt: Econometric Methods and Applications,281–314. New York, NY: Springer. doi:10.1007/978-1-4899-8008-3_9
43. Simonyan, S. (2020). Asymmetric Exchange Rate Pass-Through to Import and Export Prices for Turkey: A Nonlinear Autoregressive Distributed Lag (NARDL) Approach. Asian Academy of Management Journal of Accounting and Finance, 16(1), 35-44. https://doi.org/10.21315/aamjaf2020.16.1.2
44. Sun, Q., & Zhang, Z. (2023). Can Art Hedge Against Economic Policy Uncertainty?: New Insights through the NARDL Model. Finance Research Letters, 54, 103706. https://doi.org/10.1016/j.frl.2023.103706
45. Şencan, İ. (2024). Volatilite Endekslerinin Borsa Endeksleri Üzerindeki Etkileri: Kırılgan Beşli Ülkelerin Karşılaştırmalı Analizi. Balkan Sosyal Bilimler Dergisi, 13(25), 1-6. https://doi.org/10.55589/bsbd.1465625
46. Tobin, J. (2000). Financial Globalization. World Development, 28(6), 1101-1104. https://doi.org/10.1016/S0305-750X(00)00008-5
47. Tunçel, M. B., & Gürsoy, S. (2020). Korku Endeksi (VIX), Bitcoin Fiyatlari ve Bist100 Endeksi Arasindaki Nedensellik İlişkisi Üzerine Ampirik Bir Uygulama. Elektronik Sosyal Bilimler Dergisi, 19(76), 1999-2011. https://doi.org/10.17755/esosder.712702
48. Wang, Y. C., & Su, H. L. (2025). Unraveling Exogenous Shocks, Financial Stress and US Economic Performance. Studies in Economics and Finance, 42(2), 259-273. https://doi.org/10.1108/SEF-04-2024-0255 Whaley, R. E. (2009). Understanding the VIX. Journal of Portfolio Management, 35(3), 98- 105.
49. Yousaf, I., Patel, R., & Yarovaya, L. (2022). The Reaction of G20+ Stock Markets to the Russia– Ukraine Conflict “Black-Swan” Event: Evidence from Event Study Approach. Journal of Behavioral and Experimental Finance, 35, 100723. https://doi.org/10.1016/j.jbef.2022.100723
50. Zhang, D., Hu, M., & Ji, Q. (2020). Financial Markets under the Global Pandemic of COVID-19. Finance Research Letters, 36, 101528. https://doi.org/10.1016/j.frl.2020.101528
51. Zhao, J., Rahman, S. U., Afshan, S., Ali, M. S. E., Ashfaq, H., & Idrees, S. (2023). Green Investment, Institutional Quality, and Environmental Performance: Evidence from G-7 Countries Using Panel NARDL Approach. Environmental Science and Pollution Research, 30(45), 100845-100860. https://doi.org/10.1007/s11356-023-29332