The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee

Hoai, N.; Tuyen, D.; Nhien, N.; Hong, N.

Growing Science » Uncertain Supply Chain Management » The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee

Journals

USCM Volumes

Volume 1 (22)
- Issue 1 (4)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 2 (32)
- Issue 1 (7)
- Issue 2 (5)
- Issue 3 (10)
- Issue 4 (10)

Volume 3 (39)
- Issue 1 (9)
- Issue 2 (13)
- Issue 3 (10)
- Issue 4 (7)

Volume 4 (31)
- Issue 1 (10)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (9)

Volume 5 (26)
- Issue 1 (6)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 6 (25)
- Issue 1 (7)
- Issue 2 (6)
- Issue 3 (6)
- Issue 4 (6)

Volume 7 (57)
- Issue 1 (8)
- Issue 2 (19)
- Issue 3 (14)
- Issue 4 (16)

Volume 8 (82)
- Issue 1 (20)
- Issue 2 (15)
- Issue 3 (17)
- Issue 4 (30)

Volume 9 (117)
- Issue 1 (25)
- Issue 2 (26)
- Issue 3 (32)
- Issue 4 (34)

Volume 10 (150)
- Issue 1 (28)
- Issue 2 (32)
- Issue 3 (44)
- Issue 4 (46)

Volume 11 (190)
- Issue 1 (42)
- Issue 2 (45)
- Issue 3 (50)
- Issue 4 (53)

Volume 12 (244)
- Issue 1 (55)
- Issue 2 (59)
- Issue 3 (63)
- Issue 4 (67)

Volume 13 (61)
- Issue 1 (15)
- Issue 2 (15)
- Issue 3 (15)
- Issue 4 (16)

Countries

Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print) Quarterly Publication Volume 11 Issue 4 pp. 1735-1744 , 2023

The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee Pages 1735-1744 Download PDF

Authors: Nguyen Thi Thu Hoai, Dang Trung Tuyen, Nguyen Duy Nhien, Nguyen Thi Hong

DOI: 10.5267/j.uscm.2023.6.017

Keywords: Coffee price, DCC-GRACH model, Price volatility, VaR model, Risk management

Abstract: This paper uses the DCC-GARCH and Value at Risk (VaR) model to analyze the fluctuation, linkage, and price volatility risk among coffee price series in the period of 2004 - 2020. In terms of the fluctuation, the study points out, the volatility of Vietnamese coffee price and the price of Robusta coffee in two markets were affected by two ARCH terms and GARCH terms at 1 percent level. Meanwhile, the coffee price of Brazil and Colombia is only impacted by the ARCH term. The linkage between Brazil and Colombia is the biggest. The average coefficient linkage among Vietnam with two main competitors is relatively small. In terms of price volatility risk, the price volatility risk of Vietnamese coffee is the smallest and the biggest risk is belonging to Brazilian coffee price. The results obtained would be a valuable reference for stakeholders, policymakers, coffee processing and exporting enterprises, and coffee farmers to clearly understand the fluctuation and linkage among coffee export price series, and thereby have appropriate and effective solutions and strategies in price volatility risk management to sustainable development.

How to cite this paper

 Hoai, N., Tuyen, D., Nhien, N & Hong, N. (2023). The fluctuation linkages and price volatility risk on agricultural commodity market: Evidence from Vietnamese coffee.Uncertain Supply Chain Management, 11(4), 1735-1744.

Refrences

Acosta, A., Ihle, R., & Robles, M. (2014). Spatial price transmission of soaring milk prices from global to domestic markets. Agribusiness, 30(1), 64-73.
Canh, N. P., Wongchoti, U., Thanh, S. D., & Thong, N. T. (2019). Systematic risk in cryptocurrency market: Evidence from DCC-MGARCH model. Finance Research Letters, 29, 90-100.
Ceballos, F., Hernandez, M. A., Minot, N., & Robles, M. (2017). Grain price and volatility transmission from international to domestic markets in developing countries. World development, 94, 305-320.
Dang, T., Caihong, Z., Nguyen, T., Nguyen, N. and Tran, C. (2021), "The volatility characteristics of Vietnamese coffee export price and transmission mechanism of influencing factors: a Markov switching approach", Journal of Asia Business Studies, Vol. 15 No. 5, pp. 784-803. https://doi.org/10.1108/JABS-04-2019-0134
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20: 339-350.
Morgan, J. P. (1994). Introduction to riskmetrics. New York: JP Morgan.
OEC, M. s. O. o. E. C. (2017). from https://oec.world/en/profile/hs92/0901/
Lee, J., & Valera, H. G. A. (2016). Price transmission and volatility spillovers in Asian rice markets: Evidence from MGARCH and panel GARCH models. The International Trade Journal, 30(1), 14-32.
Rahayu, M. F., Chang, W.-I., & Anindita, R. (2015). Volatility Analysis and Volatility Spillover Analysis of Indonesia's Coffee Price Using Arch/Garch, and Egarch Model. Journal of Agricultural Studies, 3(2), 37-48.
Schwert, G. W. (2002). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 20(1), 5-17.
Song, M., Fang, K., Zhang, J., & Wu, J. (2019). The co-movement between Chinese oil market and other main international oil markets: a DCC-MGARCH approach. Computational Economics, 54(4), 1303-1318.
Tuyen, D. T., Caihong, Z., Hong, N. T., Akhtar, R., & Elamin, K. M. (2020). Assessing the effect of factors on agricultural commodity export price volatility: evidence from Vietnamese coffee. Fresenius environmental bulletin, 29(12 A), 11151-11164.
Tuyến, Đ. T., Caihong, Z., & Hồng, N. T. (2020). ASSESSING THE RELATIONSHIP BETWEEN INTERNATIONAL MARKET AND AGRICULTURAL COMMODITY EXPORT PRICES: EVIDENCE FROM VIETNAMESE COFFEE. Dalat University Journal of Science, 57-73.
Worako, T., Jordaan, H., & Van Schalkwyk, H. (2011). Investigating volatility in coffee prices along the Ethiopian coffee value chain. Agrekon, 50(3), 90-108.