Rusya-Ukrayna Savaşının Gıda Fiyatları ile Finansal Piyasalar Arasındaki Bağlantılılık Üzerine Etkisi

Öz

Bu çalışmada, Rusya-Ukrayna savaşının gıda fiyatları ile çeşitli finansal varlıklar arasındaki dinamik volatilite bağlantılılığı üzerine etkisi araştırılmaktadır. 01.01.2015 ile 31.05.2023 tarihleri arası buğday, mısır ve pirinç fiyatları ile hisse senedi (MSCI ACWI), tahvil (MOVE), emtia (S&P GSCI) ve tarımsal emtia (S&P GSCI Agriculture) piyasa endekslerinin günlük kapanış değerlerinin kullanıldığı çalışmada dinamik bağlantılılık ilişkisi Zamanla Değişen Parametreli Otoregresif (TVP-VAR) model ile incelenmiştir. Ortalama dinamik bağlantılılık sonuçlarına göre tarımsal emtia piyasaları, mısır ve hisse senedi piyasaları net volatilite yayıcısı iken, diğer piyasaların net volatilite alıcısı olduğu; savaş nedeniyle ortaya çıkan jeopolitik risklerin finansal varlıkların volatiliteleri arasındaki toplam dinamik bağlantılılığı artırdığı sonucuna varılmıştır. İncelenen dönemde değişkenlerin volatilite alıcısı ve yayıcısı olarak sürekli değişiklik gösterdiği belirlenmiştir. Savaşın ardından buğday ve hisse senedi piyasaları sert bir şekilde net volatilite yayıcısı, pirinç ve tahvil piyasaları net volatilite alıcısı haline gelmiştir. Ayrıca, tarımsal kökenli emtia piyasalarından hisse senedi piyasaları hariç diğer piyasalara; tahvil ve emtia piyasası dışındaki diğer piyasalardan da pirinç fiyatına doğru volatilite yayılımı olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Volatilite Bağlantılılığı
• Rusya-Ukrayna Savaşı
• Tarımsal Emtialar
• TVP-VAR

The Impact of the Russia-Ukraine War on the Connectedness Between Food Prices and Financial Markets

Öz

This study investigates the impact of the Russian-Ukrainian war on the dynamic volatility connectedness between food prices and various financial assets. Between 01.01.2015 and 31.05.2023, daily closing values of wheat, corn and rice prices and stock (MSCI ACWI), bond (MOVE), commodity (S&P GSCI) and agricultural commodity (S&P GSCI Agriculture) market indices are used to analyze the dynamic connectedness relationship with the Time-Varying Parameter Autoregressive (TVP-VAR) model. According to the average dynamic interconnectedness results, agricultural commodity markets, corn and stock markets are net volatility transmitters, while other markets are net volatility receivers; geopolitical risks due to war increase the overall dynamic interconnectedness between the volatilities of financial assets. During the analyzed period, the variables were found to change continuously as volatility receivers and transmitters. After the war, wheat and equity markets sharply became net volatility transmitter, while rice and bond markets became net volatility receivers. Furthermore, it has been observed that there is volatility spillover from agricultural commodity markets to all markets except the stock market, and from other markets outside the bond and commodity markets to rice prices.

Anahtar Kelimeler

• Volatility Connectedness
• Russia-Ukraine War
• Agricultural Commodities
• TVP-VAR

Kaynakça

1. Adeleke, M. A., Awodumi, O. B., & Adewuyi, A. O. (2022). Return and volatility connectedness among commodity markets during major crises periods: Static and dynamic analyses with asymmetries. Resources Policy, 79, 102963. https://doi.org/10.1016/J.RESOURPOL.2022.102963
2. Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4). https://doi.org/10.3390/jrfm13040084
3. Bahloul, S., & Khemakhem, I. (2021). Dynamic return and volatility connectedness between commodities and Islamic stock market indices. Resources Policy, 71, 101993. https://doi.org/10.1016/J.RESOURPOL.2021.101993
4. Będowska-Sójka, B., & Kliber, A. (2021). Information content of liquidity and volatility measures. Physica A: Statistical Mechanics and its Applications, 563, 125436. https://doi.org/10.1016/J.PHYSA.2020.125436
5. Billah, M., Balli, F., & Hoxha, I. (2023). Extreme connectedness of agri-commodities with stock markets and its determinants. Global Finance Journal, 56, 100824. https://doi.org/10.1016/J.GFJ.2023.100824
6. Cagli, E. C., Mandaci, P. E., & Taskin, D. (2023). The volatility connectedness between agricultural commodity and agri businesses: Evidence from time-varying extended joint approach. Finance Research Letters, 52, 103555. https://doi.org/10.1016/J.FRL.2022.103555
7. Capelle-Blancard, G., & Coulibaly, D. (2011). Index trading and agricultural commodity prices: A Panel Granger Causality Analysis. International Economics, 126-127, 51-71. https://doi.org/10.1016/S2110-7017(13)60036-0
8. Creti, A., Joëts, M., & Mignon, V. (2013). On the links between stock and commodity markets’ volatility. Energy Economics, 37, 16-28. https://doi.org/10.1016/J.ENECO.2013.01.005

9. Diebold, F. X., & Yılmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. Source: The Economic Journal, 119(534), 158-171.
10. Diebold, F. X., & Yılmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006
11. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134. https://doi.org/10.1016/J.JECONOM.2014.04.012
12. Erdoğdu, H., & Baykut, E. (2016). BIST Banka Endeksi’nin (XBANK) VIX ve MOVE endeksleri ile ilişkisi. Bankacılar Dergisi, 27(98), 57-72.
13. Farid, S., Naeem, M. A., Paltrinieri, A., & Nepal, R. (2022). Impact of COVID-19 on the quantile connectedness between energy, metals and agriculture commodities. Energy Economics, 109, 105962. https://doi.org/10.1016/J.ENECO.2022.105962
14. Furuoka, F., Yaya, O. O. S., Ling, P. K., Saleh Al-Faryan, M. A., & Islam, M. N. (2023). Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management. Resources Policy, 81, 103339. https://doi.org/10.1016/J.RESOURPOL.2023.103339
15. 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. http://www.jstor.org/stable/2352358
16. Girardi, D. (2015). Financialization of food. Modelling the time-varying relation between agricultural prices and stock market dynamics. International Review of Applied Economics, , 29(4), 482-505. https://doi.org/10.1080/02692171.2015.1016406
17. İlarslan, K., & Yıldız, M. (2022). Do international agricultural commodity prices have an effect on the stock market index? A comparative analysis between Poland and Turkey. Sosyoekonomi, 30(52), 87-107. https://doi.org/10.17233/sosyoekonomi.2022.02.06
18. İlter Küçükçolak, N. (2022). Ürün ihtisas borsacılığının gıda fiyat istikrarına katkısı. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 49, 325-339. https://doi.org/10.30794/pausbed.975798
19. İşcan, E. (2022). Metal fiyatlarının Borsa İstanbul sınai endeksi üzerine etkisi: Fourier eşbütünleşme testinden bulgular. Yakın Doğu Üniversitesi Sosyal Bilimler Dergisi, 15(2), 204-238. https://dergi.neu.edu.tr/index.php/sosbilder/article/view/585/249
20. Kang, S. H., & Lee, J. W. (2019). The network connectedness of volatility spillovers across global futures markets. Physica A: Statistical Mechanics and its Applications, 526, 120756. https://doi.org/10.1016/J.PHYSA.2019.03.121
21. Khalfaoui, R., Shahzad, U., Ghaemi Asl, M., & Ben Jabeur, S. (2023). Investigating the spillovers between energy, food, and agricultural commodity markets: New insights from the quantile coherency approach. The Quarterly Review of Economics and Finance, 88, 63-80. https://doi.org/10.1016/J.QREF.2022.12.006
22. Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101-116. https://doi.org/10.1016/J.EUROECOREV.2014.07.002
23. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. https://doi.org/10.1016/0304-4076(95)01753-4
24. López Cabrera, B., & Schulz, F. (2016). Volatility linkages between energy and agricultural commodity prices. Energy Economics, 54, 190-203. https://doi.org/10.1016/J.ENECO.2015.11.018
25. Mensi, W., Beljid, M., Boubaker, A., & Managi, S. (2013). Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold. Economic Modelling, 32(1), 15-22. https://doi.org/10.1016/J.ECONMOD.2013.01.023
26. Mensi, W., Vo, X. V., & Kang, S. H. (2021). Multiscale spillovers, connectedness, and portfolio management among precious and industrial metals, energy, agriculture, and livestock futures. Resources Policy, 74, 102375. https://doi.org/10.1016/J.RESOURPOL.2021.102375
27. Naifar, N., & Hammoudeh, S. (2016). Do global financial distress and uncertainties impact GCC and global sukuk return dynamics? Pacific-Basin Finance Journal, 39, 57-69. https://doi.org/10.1016/J.PACFIN.2016.05.016
28. Owusu Junior, P., Agyei, S. K., Adam, A. M., & Bossman, A. (2022). Time-frequency connectedness between food commodities: New implications for portfolio diversification. Environmental Challenges, 9, 100623. https://doi.org/10.1016/J.ENVC.2022.100623
29. Özer, H., & Yarbaşı, İ. Y. (2023). Tahıl emtia fiyat oynaklığının Markov değişim asimetrik Garch modelleriyle incelenmesi. İşletme Araştırmaları Dergisi, 15(1), 500-513. https://doi.org/https://doi.org/10.20491/isarder.2023.1600
30. Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0
31. Piljak, V. (2013). Bond markets co-movement dynamics and macroeconomic factors: Evidence from emerging and frontier markets. Emerging Markets Review, 17, 29-43. https://doi.org/10.1016/J.EMEMAR.2013.08.001
32. Silvennoinen, A., & Thorp, S. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24(1), 42-65. https://doi.org/10.1016/J.INTFIN.2012.11.007
33. Tiwari, A. K., Nasreen, S., Shahbaz, M., & Hammoudeh, S. (2020). Time-frequency causality and connectedness between international prices of energy, food, industry, agriculture and metals. Energy Economics, 85, 104529. https://doi.org/10.1016/J.ENECO.2019.104529
34. Umar, Z., Jareño, F., & Escribano, A. (2021a). Agricultural commodity markets and oil prices: An analysis of the dynamic return and volatility connectedness. Resources Policy, 73, 102147. https://doi.org/10.1016/J.RESOURPOL.2021.102147
35. Umar, Z., Polat, O., Choi, S. Y., & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, 48, 102976. https://doi.org/10.1016/J.FRL.2022.102976
36. Umar, Z., Riaz, Y., & Zaremba, A. (2021b). Patterns of spillover in energy, agricultural and metal markets: A connectedness analysis for years 1780-2020. Finance Research Letters, 43, 101999. https://doi.org/10.1016/J.FRL.2021.101999
37. Vardar, G., Coşkun, Y., & Yelkenci, T. (2018). Shock transmission and volatility spillover in stock and commodity markets: evidence from advanced and emerging markets. Eurasian Economic Review, 8, 231-288. https://doi.org/10.1007/s40822-018-0095-3
38. Wang, S. (2023). Tail dependence, dynamic linkages, and extreme spillover between the stock and China’s commodity markets. Journal of Commodity Markets, 29, 100312. https://doi.org/10.1016/J.JCOMM.2023.100312
39. Wang, S., Zhou, B., & Gao, T. (2023). Speculation or actual demand? The return spillover effect between stock and commodity markets. Journal of Commodity Markets, 29, 100308. https://doi.org/10.1016/J.JCOMM.2022.100308
40. www.msci.com/documents/10199/a71b65b5-d0ea-4b5c-a709-24b1213bc3c5
41. www.spglobal.com/spdji/en/indices/commodities/sp-gsci/#overview
42. www.spglobal.com/spdji/en/documents/indexnews/announcements/202211101457679/14576 79_spgsci2023cpwindexannouncement.pdf
43. www.spglobal.com/spdji/en/indices/commodities/sp-gsci-agriculture/#overview