Causality Relationships Between Oil and Foreign Exchange Markets An Application With Wavelet Analysis

Öz

Relationships among financial and commodity markets become dynamic through globalization and increasing market integration. It is undeniably accepted that fluctuations in financial markets drag real economies into crisis and cause socio-economic changes in countries. In this context, examining the temporal variations of causality relations in commodity and financial markets has become crucial for investors and policy makers, as it provides useful insights in terms of understanding the nature of the inter-market information flows and the spillover effect of shocks. Thus, the main purpose of this study is to reveal the time-based and scale based causality information flow between Oil prices and Euro exchange rate, and to reveal the global and local events affecting these information flows through the empirical approach proposing the use of SHP and CWTC tests. Through using the CWTC (Continuous Wavelet Transformation Based Granger Causality Test) and SPH, which allow for the analysis of non-stationary data directly, evidence that the causality between the Euro exchange rate and oil prices varies over time and has dynamics varying based on the time scale is found in this study. The overall result of the aformentioned tests indicates that there exist both unidirectional causalities from EUR to OIL in the period of 2010 - 2015, and bidirectional causality in the period of 2015 – 2020. Moreoever, evidence in favor of short-term bidirectional causality relationship patterns between EUR and OIL and long term unidirectional information flow from EUR to OIL were provided in this study.

Anahtar Kelimeler

• Causality Test
• Nonstationary Data Analysis
• Wavelet Analysis
• LA-VAR

Petrol ve Döviz Piyasaları Arasındaki Nedensellik İlişkileri: Dalgacık (Wavelet) Analizi ile Bir Uygulama

Öz

Küreselleşme ve piyasalar arası artan entegrasyon neticesinde finansal piyasalar ve emtia piyasaları arasındaki ilişki dinamik bir hale gelmiştir. Emtia ve finans piyasalarındaki nedensellik ilişkilerinin zamana bağlı değişiminin incelenmesi, piyasalar arası bilgi akışı ve şokların yayılma etkisinin doğasının anlaşılması açısından yararlı bilgiler sunması nedeniyle yatırımcı ve politika yapıcılar için zorunluluk halini almıştır. Bu çalışmanın ana amacı, SHP ve CWTC testlerinin kullanılmasını öngören ampirik yaklaşım aracılığıyla Petrol fiyatları ve Avro döviz kuru arasındaki zamana dayalı nedensellik etkisinin zamana ve zaman skalasına göre değişiminin ortaya çıkarılması ve söz konusu değişimlerin oluştuğu dönemlerde meydana gelen küresel ve yerel olayların ortaya konulmasıdır. Durağan olmayan verilerin analizine izin veren CWTC (Continuous Wavelet Transformantion Based Granger Casuality Test) ve SHP (Shi – Hurn – Phillips (2020) test) testlerinin uygulanması sonucunda, Avro döviz kuru ve petrol fiyatları arasındaki nedenselliğin zamana bağlı değiştiği ve zaman skalasına göre değişen dinamiklere sahip olduğuna ilişkin kanıtlar bulunmuştur. Söz konusu testlerin ortak sonucu 2010 – 2015 döneminde EUR’den OIL’e tek yönlü nedensellik, 2015 – 2020 döneminde ise çift yönlü nedensellik örüntüsüne dair kanıtlar elde edilmiştir. Ayrıca çalışma sonuçlarına göre EUR ve OIL arasında kısa dönemde kısa süreli meydana gelen çift yönlü nedensellik ilişkisi ve örüntüsünden bahsedilebilir. Uzun dönemde ise EUR’den OIL’e tek yönlü nedensellik ilişkisine dair bulgular sağlanmıştır.

Anahtar Kelimeler

• Nedensellik testi
• Durağan Olmayan Veri Analizi
• Dalgacık Analizi
• LA-VAR

Kaynakça

1. Aguiar-Conraria vd. (2008). Aguiar-Conraria vd. (2008)= Aguiar-Conraria, L., Azevedo, N., Soares, M. J.(2008). Using wavelets to decompose the time–frequency effects of monetary policy, Physica A, 387, 2863–2878
2. Aguiar-Conraria vd. (2018). Aguiar-Conraria vd. (2018) = Aguiar-Conraria, L., Soares, M. J., Sousa, R. (2018). California’s carbon market and energy prices: a wavelet analysis. Phil. Trans. R. Soc. A 376: 20170256
3. Albulescu vd. (2015). Albulescu vd. (2015)= Albulescu, C. T., Goyeau, D., & Tiwari, A. (2015). Contagion and dynamic correlation of the main European stock index futures markets: a time-frequency approach.
4. Albulescu vd. (2017). Albulescu, C. T., Goyeau, D., & Tiwari, A. K. (2017). Co-movements and contagion between international stock index futures markets. Empirical Economics, 52(4), 1529-1568.
5. Almasri and Shukur (2003). Almasri, A., Shukur, G., 2003. An illustration of the causality relationship between government spending and revenue using wavelets analysis on Finnish data. Journal of Applied Statistics 30 (5), 571–584.
6. Altarturi vd. (2018). Altarturi, B., H., M., Alshammuri, A., A., Hussin, T., M., T., I., T., Saiti, B., (2016), International Journal of Energy Economics and Policy, 6,3, 421- 430
7. Andries vd. (2004). Andrieș, A. M., Ihnatov, I., & Tiwari, A. K. (2014). Analyzing time–frequency relationship between interest rate, stock price and exchange rate through continuous wavelet. Economic Modelling, 41, 227-238.
8. Andries vd. (2017). Andrieș, A. M., Căpraru, B., Ihnatov, I., & Tiwari, A. K. (2017). The relationship between exchange rates and interest rates in a small open emerging economy: The case of Romania. Economic Modelling, 67, 261-274.

9. Bekiros ve Marcellino (2013). Bekiros S. D., Diks, C., G., H., 2008, The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality, Energy Economics, 30, 2673–2685
10. Benhmad (2012). Benhmad, F., (2012). Modeling nonlinear Granger causality between the oil price and U.S. dollar: A wavelet based approach, Economic Modelling, 29, 1505–1514
11. Breitung ve Candelon (2006). Jörg Breitung, Bertrand Candelon, Testing for short- and long-run causality: A frequency-domain approach, Journal of Econometrics, Volume 132, Issue 2, 2006, Pages 363-378, ISSN 0304-4076,
12. Chou and Chen (2011). Chou C., C., Chen S.-L., (2011), 'Integrated or segmented? a wavelet transform analysis on relationship between stock and real estate markets, Economics Bulletin, Vol. 31 No. 4 pp. 3030-3040.
13. Christiano ve Ljungqvist (1988). Christiano, L., J., Ljungqvist, L., (1988), Money Does Granger-Cause Output in the Bivariate Money–Output Relation, Journal of Monetary Economics 22: 217–235.
14. Crowley ve Mayes (2009). Crowley, P. M., & Mayes, D. G. (2009). How fused is the euro area core?. OECD Journal: Journal of Business Cycle Measurement and Analysis, 2008(1), 63-95.
15. Dhamala vd. (2018a). Dhamala, M., Rangarajan, G., Ding, M. 2008 Estimating Granger Causality from Fourier and Wavelet Transforms of Time Series Data. Physical Review Letters 100, 018701-1 - 4.
16. Dhamala vd. (2018b). Dhamala, M., Rangarajan, G., Ding, M. (2008b) Analyzing information flow in brain networks with nonparametric Granger causality, NeuroImage, 41 , 354–362
17. Diks ve Panchenko (2006). Diks, C., Panchenko, V., (2006), A new statistic and practical guidelines for nonparametric Granger causality testing, Journal of Economic Dynamics & Control, 30, 1647–1669.
18. Durai and Bhaduri (2009). Durai, S. R. S., & Bhaduri, S. N. (2009). Stock prices, inflation and output: Evidence from wavelet analysis. Economic Modelling, 26(5), 1089-1092.
19. Eichenbaum ve Singleton (1986). Eichenbaum, M., Singleton, K., J., (1986), Do Equilibrium Real Business Cycle Theories Explain Postwar U.S. Business Cycles, NBER Macroeconomics Annual 1986: 91–146.
20. Eichler (2007). Eichler, M., (2007). Granger causality and path diagrams for multivariate time series, Journal of Econometrics, 137(2), 334-353.
21. Geweke (1982). Geweke, J., (1982). Measurement of Linear Dependence and Feedback between Multiple Time Series, Journal of the American Statistical Association, 77,378, 304-313
22. Grinsted vd. (2004). Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series.
23. Hong vd. (2009). Hong, Y., Liu, Y., & Wang, S. (2009). Granger causality in risk and detection of extreme risk spillover between financial markets. Journal of Econometrics, 150(2), 271-287.
24. In and Kim (2006). In, F., Kim, S., (2006), The hedge ratio and the empirical relationship between the stock and futures markets: a new approach using wavelets, The Journal of Business, 79, 799–820.
25. Kim and In (2003). Kim, S., In, F., H., (2003), The Relationship Between Financial Variables and Real Economic Activity: Evidence From Spectral and Wavelet Analyses, In Studies in Nonlinear Dynamics & Econometrics ,7, 4.
26. Lv vd. (2018). Lv, X., Lien, D., Chen, Q., Yu, C,. (2018). Does Exchange Rate Management Affect the Causality Between Exchange Rates and Oil Prices? Evidence from Oil-Exporting Countries
27. Mannson (2012). Månsson, K. (2012). A Wavelet-Based Approach of Testing for Granger Causality in the Presence of GARCH Effects. Communications in Statistics-Theory and Methods, 41(4), 717-728.
28. Mitra (2006). Mitra, S., 2006. A wavelet filtering based analysis of macroeconomic indicators: the Indian evidence. Applied Mathematics and Computation 175, 1055–1079.
29. Olayeni (2016). Olayeni, O. R. (2016). Causality in continuous wavelet transform without spectral matrix factorization: theory and application. Computational Economics, 47(3), 321-340.
30. Polanco-Martinez ve Abadie (2016)= Polanco-Martínez, J. M., & Abadie, L. M. (2016). Analyzing crude oil spot price dynamics versus long term future prices: A wavelet analysis approach. Energies, 9(12), 1089.
31. Rhif vd. (2019). Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: A review. Applied Sciences, 9(7), 1345.
32. Rua A. (2010). Rua, A. (2010) Measuring comovement in the timefrequency space, Journal of Macroeconomics, 32, 685–91.
33. Rua A. (2013). Rua, A. (2013). Worldwide synchronization since the nineteenth century: a wavelet-based view. Applied Economics Letters, 20(8), 773-776.
34. Rua ve Nunes (2012). Rua, A., & Nunes, L. C. (2012). A wavelet-based assessment of market risk: The emerging markets case. The Quarterly Review of Economics and Finance, 52(1), 84-92.
35. Shi vd. (2012). Shi, S., Hurn, S., Phillips, P., B., (2020), Causal Change Detection in Possibly Integrated Systems: Revisiting the Money- Income Relationship, Journal of Financial Econometrics, 18,1,158-180
36. Sims (1987). Sims, C., A,. (1987), Comment. Journal of Business&Economic Statistics 5: 443–449.
37. Stock ve Watson (1989). Stock, J. H., Watson, M., W., (1989), Interpreting the Evidence on Money–Income Causality. Journal of Econometrics, 40,161–181.
38. Tiwari vd. (2013). Tiwari, A. K., Mutascu, M. I., & Albulescu, C. T. (2013). The influence of the international oil prices on the real effective exchange rate in Romania in a wavelet transform framework. Energy Economics, 40, 714-733.
39. Toda ve Yamamoto (1995). Toda, H., Y., Yamamoto, T., (1995), Statistical Inference in Vector Autoregressive with Possibly Integrated Process, Journal of Econometrics,66, 225-250.
40. Torrence ve Compo (1998). Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological society, 79(1), 61-78.
41. Wen vd. (2020). Wen, D., Liu, L., Ma, C., Wang, Y. (2020), Etreme Risk Spillovers between Crude Oil Prices an the U.S. Exchange Rate: Evidence from oil-exporting and oil-importing Countries, Energy, 212, 118740
42. Wilson (1972). Wilson, G., T. 1972 The Factorization of Matricial Spectral Densities. SIAM Journal on Applied Mathematics, 23,4, 420-426.
43. Wilson (1978). Wilson, G., T. 1978 A convergence theorem for spectral factorization. Journal of Multivariate Analysis, 8, 2, 222 - 232.
44. Yang vd. (2017). Yang, L., Cai, X., J., Hamori, S., (2017), Does the Crude Oil Price Influence the Exchange Rates of Oil-Importing and Oil-Exporting Countries Differently? A wavelet Coherence Analysis, International Review of Economics and Finance