Agric. Econ. - Czech, 2023, 69(8):332-342 | DOI: 10.17221/125/2023-AGRICECON
Multi-frequency downside risk interconnectedness between soft agricultural commoditiesOriginal Paper
- 1 Novi Sad School of Business, University of Novi Sad, Novi Sad, Serbia
- 2 Institute of Agricultural Economics, Belgrade, Serbia
In this article, we explore multiscale extreme risk interdependence between four soft agricultural markets – coffee, cocoa, cotton and orange juice. Wavelet correlation and cross-correlation are used to investigate this interlink, and dynamic conditional Value at Risk is used to measure extreme risk. Wavelet correlation results suggest a very weak connection between the markets in the short-term and midterm horizons, which means that investors who operate in the short term or midterm do not have to apply hedging measures against extreme risk. However, the situation is different in the long term, where relatively high correlations are found on the highest wavelet scale in all pairs, except coffee–cocoa. Complementary cross-correlation analysis indicates a lead–lag relationship between the markets. The results are mostly in line with expectations, as bigger markets lead smaller markets. Only in the cases of cocoa–cotton and cocoa–orange juice does the opposite happen.
Keywords: conditional Value at Risk; extreme risk interdependence; wavelet correlation; wavelet cross-correlation
Received: April 11, 2023; Revised: July 19, 2023; Accepted: July 19, 2023; Prepublished online: July 19, 2023; Published: August 11, 2023 Show citation
References
- Akyildirim E., Cepni O., Pham L., Uddin G.S. (2022): How connected is the agricultural commodity market to the news-based investor sentiment? Energy Economics, 113: 106174.
Go to original source...
- Almaskati N. (2022): Oil and GCC foreign exchange forward markets: A wavelet analysis. Borsa Istanbul Review, 22: 1039-1044.
Go to original source...
- Árendáš P., Kotlebová J. (2023): Agricultural commodity markets and the Turn of the month effect. Agricultural Economics - Czech, 69: 101-108.
Go to original source...
- Babar M., Ahmad H., Yousaf I. (2023): Returns and volatility spillover between agricultural commodities and emerging stock markets: new evidence from COVID-19 and Russian-Ukrainian war. International Journal of Emerging Markets (ahead of print).
Go to original source...
- Boscá J.E., Doménech R., Ferri J., García J.R., Ulloa C. (2021): The stabilizing effects of economic policies in Spain in times of COVID-19. Applied Economic Analysis, 29: 4-20.
Go to original source...
- Bonato M. (2019): Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed? Journal of International Financial Markets, Institutions and Money, 62: 184-202.
Go to original source...
- Fernández-Avilés G., Montero J.M., Sanchis-Marco L. (2020): Extreme downside risk co-movement in commodity markets during distress periods: A multidimensional scaling approach. The European Journal of Finance, 26: 1207-1237.
Go to original source...
- Gardebroek C., Hernandez M.A., Robles M. (2016): Market interdependence and volatility transmission among major crops. Agricultural Economics, 47: 141-155.
Go to original source...
- Hamadi H., Bassil C., Nehme T. (2017): News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil. Research in International Business and Finance, 41: 148-157.
Go to original source...
- Investing (2023): Investing. [Dataset]. Available at https://www.investing.com/commodities/softs (accessed Mar 24, 2023).
- Palanska T. (2020): Measurement of volatility spillovers and asymmetric connectedness on commodity and equity markets. Finance a úvěr - Czech Journal of Economics and Finance, 70: 42-69.
- Rockafellar R.T., Uryasev S. (2002): Conditional value-at-risk for general loss distributions. Journal of Banking and Finance, 26: 1443-1471.
Go to original source...
- Stooq (2023): Stooq. [Dataset]. Available at stooq.com (accessed Mar 24, 2023)
- Tiwari A.K., Abakah E.J.A., Dogan B., Ghosh S. (2023): Sustainable debt and gas markets: A new look using the time-varying wavelet-windowed cross-correlation approach. Energy Economics, 120: 106606.
Go to original source...
- Umar Z., Olson D. (2022): Strategic asset al.ocation and the demand for real estate: International evidence. Empirical Economics, 62: 2461-2513.
Go to original source...
- Yu X., Zhang W.G., Liu Y.J. (2018): Crude oil options hedging based on a new extreme risk measure. Economic Computation and Economic Cybernetics Studies and Research, 52: 275-290.
Go to original source...
- Živkov D., Durašković J., Gajić-Glamočlija M. (2022): Multiscale downside risk interdependence between the major agricultural commodities. Agribusiness, 38: 990-1011.
Go to original source...
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.