Dergiler / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2026 / Cilt: 5 Sayı: Özel Sayı Özel Sayı

Covid-19 Pandemisinin Tarım Fiyatları Üzerindeki Etkisi: Sürekli Dalgacık Dönüşümü Bazlı Granger Nedensellik Testi

Impacts of the Covid-19 Pandemic on the Agricultural Prices: New Insights from CWT Granger Causality Test

Remzi Gök(DİCLE ÜNİVERSİTESİ)0000-0002-9216-5210
Erkan Kara(NECMETTİN ERBAKAN ÜNİVERSİTESİ, UYGULAMALI BİLİMLER FAKÜLTESİ)0000-0001-7228-0396
Sayfa
76–96
Yayın Tarihi
2020-12-26
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Elde edilen test sonuçlarına göre ölüm oranı ile mısır, yulaf, kolza ve soya fasulyesi fiyatları arasında uzun dönemli eşbütünleşme ilişkisinin varlığı tespit edilmiştir.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışmada, korona virüsü pandemisinin spot tarım fiyatları üzerindeki etkisi, hem standart metod hem de dalgacık bazlı korelasyon ve Granger nedensellik testler kullanılarak, incelenmiştir. 22 Ocak – 18 Eylül 2020 dönemine ait günlük ölüm oranı ile mısır, yulaf, kolza, pirinç, soya fasulyesi ve buğday fiyatları ele alınmıştır.
📊
Temel Bulgular & Veri:Elde edilen test sonuçlarına göre ölüm oranı ile mısır, yulaf, kolza ve soya fasulyesi fiyatları arasında uzun dönemli eşbütünleşme ilişkisinin varlığı tespit edilmiştir.
💡
Sonuç & Katkı:Elde edilen bulgular, politika yapıcılar için önemli sonuçlar doğurmaktadır.
🔬Tür:Araştırma Makalesi
📍Kapsam:2020 Verileri
⚙️Değişkenler:Dalgacıklar · Nedensellik · COVID 19

Özet

Bu çalışmada, korona virüsü pandemisinin spot tarım fiyatları üzerindeki etkisi, hem standart metod hem de dalgacık bazlı korelasyon ve Granger nedensellik testler kullanılarak, incelenmiştir. 22 Ocak – 18 Eylül 2020 dönemine ait günlük ölüm oranı ile mısır, yulaf, kolza, pirinç, soya fasulyesi ve buğday fiyatları ele alınmıştır. Elde edilen test sonuçlarına göre ölüm oranı ile mısır, yulaf, kolza ve soya fasulyesi fiyatları arasında uzun dönemli eşbütünleşme ilişkisinin varlığı tespit edilmiştir. Ayrıca, ölüm oranının mısır ve kolza fiyatlarının uzun dönemde, yulaf fiyatlarının ise hem kısa hem de uzun dönemde Granger nedeni olduğu bulgusuna rastlanmıştır. Diğer taraftan, dalgacık bazlı korelasyon analizi sonuçlarına göre değişkenler arasındaki ilişki zamana göre değişmekte, diğer bir ifadeyle heterojen özellikler sergilemektedir. Dalgacık bazlı nedensellik test bulgularına göre ise, ölüm oranındaki negatif gelişmelerin çoğu tarım fiyatlarındaki negatif gelişmeleri üzerinde istatistiksel olarak anlamlı nedensellik ilişkisine sebep olduğu ortaya çıkmıştır. Elde edilen bulgular, politika yapıcılar için önemli sonuçlar doğurmaktadır.

Abstract

In this paper, the impacts of the Covid-19 mortality rates on the agricultural spot prices were investigated by using both standard techniques and wavelet-based cohesion and Granger causality tests. Our dataset consisted of daily observations of the mortality rates as well as corn, oats, rapeseed, rice, soybeans, and wheat prices during the period January 22 to September 18, 2020. The findings of the paper revealed that the mortality rate was cointegrated with the prices of corn, oats, rapeseed, and soybeans. Further, the VECM results showed that the mortality rate unidirectionally Granger-caused the corn and rapeseed prices in the long-run, and the oat prices in the short- and long-run. On the other hand, the wavelet cohesion results revealed that the dynamics of the interdependence of the underlying variables were time-varying and heterogeneous over time horizons. The wavelet-based Granger-causality test, however, indicated that the mortality rates negatively caused most of the agricultural prices. These findings yield some important implications for policymakers.

🔖

Bu Makaleye Atıf Yap

Gök, R., Kara, E. (2020). Covid-19 Pandemisinin Tarım Fiyatları Üzerindeki Etkisi: Sürekli Dalgacık Dönüşümü Bazlı Granger Nedensellik Testi. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 5 Sayı: Özel Sayı Özel Sayı, 76–96. https://doi.org/10.30784/epfad.810558

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (42)

  1. Akhtaruzzaman, M., Boubaker, S. and Sensoy, A. (2020). Financial contagion during Covid–19 crisis. Finance Research Letters, 38, 1–20. https://doi.org/10.1016/j.frl.2020.101604
  2. Alam, M. S., Shahzad, S. J. H. and Ferrer, R. (2019). Causal flows between oil and forex markets using high-frequency data: Asymmetries from good and bad volatility. Energy Economics, 84, 1–21. https://doi.org/10.1016/j.eneco.2019.104513
  3. Aslam, F., Aziz, S., Nguyen, D. K., Mughal, K. S. and Khan, M. (2020). On the efficiency of foreign exchange markets in times of the COVID-19 pandemic. Technological Forecasting and Social Change, 161, 1–12. https://doi.org/10.1016/j.techfore.2020.120261
  4. Atkeson, A. (2020). What will be the economic impact of Covid-19 in the US? Rough estimates of disease scenarios (NBER Working Paper No 26867). Retrieved from http://acdc2007.free.fr/nber26867.pdf
  5. Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C. and Viratyosin, T. (2020). The unprecedented stock market impact of COVID-19 (NBER Working Paper No 26945). Retrieved from https://www.nber.org/papers/w26945
  6. Brewin, D. G. (2020). The impact of COVID‐19 on the grains and oilseeds sector. Canadian Journal of Agricultural Economics, 68(2), 185–188. https://doi.org/10.1111/cjag.12239
  7. Conlon, T. and McGee, R. (2020). Safe haven or risky hazard? Bitcoin during the Covid-19 bear market. Finance Research Letters, 35, 1–5. https://doi.org/10.1016/j.frl.2020.101607
  8. Dutta, A., Das, D., Jana, R. K. and Vo, X. V. (2020). COVID-19 and oil market crash: Revisiting the safe haven property of gold and Bitcoin. Resources Policy, 69, 1–6. https://doi.org/10.1016/j.resourpol.2020.101816
  9. Fernandes, N. (2020). Economic effects of coronavirus outbreak (COVID-19) on the world economy (IESE Business School Working Paper No. WP-1240-E). Retrieved from https://foroparalapazenelmediterraneo.es/wp-content/uploads/2020/03/SSRN-id3557504.pdf.pdf
  10. Gençay, R., Selçuk, F. and Whitcher, B. J. (2001). An introduction to wavelets and other filtering methods in finance and economics. San Diego: Academic Press (Elsevier).
  11. Ghazanfari, A. (2020). The impact of the Covid-19 pandemic and crude oil price crisis on the price of automobile fuels in European countries. Diverse Journal of Multidisciplinary Research, 2(6), 10–19. Retrieved from https://diverseresearchjournals.com/
  12. Gherghina, Ș. C., Armeanu, D. Ș. and Joldeș, C. C. (2020). Stock market reactions to Covid-19 pandemic outbreak: quantitative evidence from ARDL bounds tests and Granger causality analysis. International Journal of Environmental Research and Public Health, 17(18), 1–35. https://doi.org/10.3390/ijerph17186729
  13. Goodell, J. W. and Goutte, S. (2020). Co-movement of Covid-19 and Bitcoin: Evidence from wavelet coherence analysis. Finance Research Letters, 38, 1–6. https://doi.org/10.1016/j.frl.2020.101625
  14. Grinsted, A., Moore, J. C. and Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. Retrieved from https://hal.archives-ouvertes.fr
  15. Gupta, M., Abdelmaksoud, A., Jafferany, M., Lotti, T., Sadoughifar, R. and Goldust, M. (2020). Covid‐19 and economy [Special issue]. Dermatologic Therapy, 33(5). http://dx.doi.org/10.1111/dth.13329
  16. Hatemi-J, A. (2008). Tests for cointegration with two unknown regime shifts with an application to financial market integration. Empirical Economics, 35(3), 497–505. https://doi.org/10.1007/s00181-007-0175-9
  17. Ji, Q., Zhang, D. and Zhao, Y. (2020). Searching for safe-haven assets during the Covid-19 pandemic. International Review of Financial Analysis, 71(2020), 1–10. https://doi.org/10.1016/j.irfa.2020.101526
  18. Jun, W., Mahmood, H. and Zakaria, M. (2020). Impact of trade openness on environment in China. Journal of Business Economics and Management, 21(4), 1185–1202. https://doi.org/10.3846/jbem.2020.12050
  19. Kang, S. H., Tiwari, A. K., Albulescu, C. T. and Yoon, S. M. (2019). Time-frequency co-movements between the largest nonferrous metal futures markets. Resources Policy, 61, 393–398. https://doi.org/10.1016/j.resourpol.2017.12.010
  20. Kara, E. and Diken, A. (2020). Climatic change: The effect of rainfall on economic growth. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 11(28), 665–679. https://doi.org/10.21076/vizyoner.693363
  21. Kerr, A. K. (2020). The Covid‐19 pandemic and agriculture: Short‐ and long‐run implications for international trade relations. Canadian Journal of Agricultural Economics, 68(2), 225–229. https://doi.org/10.1111/cjag.12230
  22. Lahmiri, S. and Bekiros, S. (2020). The impact of Covid-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets. Chaos, Solitons & Fractals, 138(2020), 1–6. https://doi.org/10.1016/j.chaos.2020.109936
  23. Lawley, C. (2020). Potential impacts of COVID‐19 on Canadian farmland markets. Canadian Journal of Agricultural Economics, 68(2), 245–250. https://doi.org/10.1111/cjag.12242
  24. Lee, J. and 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
  25. Mensi, W., Sensoy, A., Vo, X. V. and Kang, S. H. (2020). Impact of Covid-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69(2020), 1–11. https://doi.org/10.1016/j.resourpol.2020.101829
  26. Narayan, P. K., Devpura, N. and Hua, W. (2020). Japanese currency and stock market—What happened during the Covid-19 pandemic? Economic Analysis and Policy, 68, 191–198. https://doi.org/10.1016/j.eap.2020.09.014
  27. Olayeni, O. R. (2016). Causality in continuous wavelet transform without spectral matrix factorization: theory and application. Computational Economics, 47(3), 321–340. https://doi.org/10.1007/s10614-015-9489-4
  28. Öztürk, Ö., Şişman, M. Y., Uslu, H. and Çıtak, F. (2020). Effect of Covid-19 outbreak on Turkish stock market: A sectoral-level analysis. Hitit University Journal of Social Sciences Institute, 13(1), 56–68. https://doi.org/10.17218/hititsosbil.728146
  29. Percival, D. B. and Walden, A. T. (2000). Wavelet methods for time series analysis (Vol. 4). Cambridge: Cambridge University Press.
  30. Pu, M. and Zhong, Y. (2020). Rising concerns over agricultural production as Covid-19 spreads: Lessons from China. Global Food Security, 26(2020), 1–7. https://doi.org/10.1016/j.gfs.2020.100409
  31. Rawal, V., Kumar, M., Verma, A. and Pais, J. (2020). Covid-19 Lockdown: Impact on agriculture and the rural economy (Society for Social and Economic Research Working Paper No.S3/209). Retrieved from http://archive.indianstatistics.org/sserwp/sserwp2003.pdf
  32. Rua, A. (2010). Measuring comovement in the time-frequency space. Journal of Macroeconomics, 32(2), 685–691. https://doi.org/10.1016/j.jmacro.2009.12.005
  33. Salisu, A. A., Ebuh, G. U. and Usman, N. (2020). Revisiting oil-stock nexus during Covid-19 pandemic: Some preliminary results. International Review of Economics & Finance, 69, 280–294. https://doi.org/10.1016/j.iref.2020.06.023
  34. Sari, S. S. and Kartal, T. (2020). Covid-19 salgınının altın fiyatları, petrol fiyatları ve VIX endeksi ile arasındaki ilişki [The relationship of Covid-19 pandemic with gold prices, oil prices and VIX index]. Erzincan Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 13(1), 93–109. https://doi.org/10.46790/erzisosbil.748181
  35. Şenol, Z. and Zeren, F. (2020). Coronavirus (Covid-19) and stock markets: The effects of the pandemic on the global economy. Eurasian Journal of Researches in Social and Economics 7(4), 1–16. Siche, R. (2020). What is the impact of Covid-19 disease on agriculture?. ScientiaAgropecuaria, 11(1), 3–6. http://dx.doi.org/10.17268/sci.agropecu.2020.01.00
  36. Tiwari, A. K., Olayeni, R. O., Olofin, S. A. and Chang, T. (2019). The Indian inflation–growth relationship revisited: robust evidence from time–frequency analysis. Applied Economics, 51(51), 5559–5576. https://doi.org/10.1080/00036846.2019.1616065
  37. Topcu, M. and Gulal, O. S. (2020). The impact of Covid-19 on emerging stock markets. Finance Research Letters, 36(2020), 1–4. https://doi.org/10.1016/j.frl.2020.101691
  38. Torrence, C. and Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American meteorological society, 79(1), 61–78. https://doi.org/10.1175/1520-0477(1998)079 2.0.CO;2
  39. Torun, E. and Demireli, E. (2019). Sürekli dalgacık dönüşümlü Granger nedensellik analizi: Türkiye örneği [Nonparametrıc continuous wavelet transform (CWT) Granger causality analysis: the case of Turkey]. Çanakkale Onsekiz Mart Üniversitesi Yönetim Bilimleri Dergisi, 17(34), 389–487. doi:10.35408/comuybd.502454
  40. Wang, J., Shao, W. and Kim, J. (2020). Analysis of the impact of Covid-19 on the correlations between crude oil and agricultural futures. Chaos, Solitons & Fractals, 136(2020), 1–7. https://doi.org/10.1016/j.chaos.2020.109896
  41. Yang, Y., Zhang, H. and Chen, X. (2020). Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak. Annals of Tourism Research, 83, 1–6. http://dx.doi.org/10.1016/j.annals.2020.102913
  42. Şenol, Z. and Zeren, F. (2020). Coronavirus (Covid-19) and stock markets: The effects of the pandemic on the global economy. Eurasian Journal of Researches in Social and Economics 7(4), 1–16.