Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates

Jittima Singvejsakul, Yaovarate Chaovanapoonphol and Budsara Limnirankul
Additional contact information
Jittima Singvejsakul: Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand
Yaovarate Chaovanapoonphol: Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand
Budsara Limnirankul: Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand

Economies, 2021, vol. 9, issue 3, 1-10

Abstract: Thailand is a significant global exporter of cassava, of which cassava chips are the main export products. Moreover, China was the most important export market for Thailand from 2000 to 2020. However, during that period, Thailand confronted fluctuations in the cassava product price, and cassava chips were a product with significant price volatility, adapting to changes in export volumes. This study aims to analyze the volatility of the price of cassava chips in Thailand from 2010 to 2020. The data were collected monthly from 2010 to 2020, including the price of cassava chips in Thailand (Y), the volume of cassava China imported from Thailand (X1), the price of the cassava chips that China imported from Thailand (X2), the price of the cassava starch that China imported from Thailand (X3), the substitute crop price for maize (X4), the substitute crop price for wheat (X5), and Thailand’s cassava product export volume (X6). The volatility and the factors affecting the volatility in the price of cassava chips were calculated using Bayesian GARCH-X. The results indicate that the increase in X1, X2, X3, X4, and X6 led to an increase in the rate of change in cassava chip price volatility. On the other hand, if the substitute crop price for wheat (X5) increases, then the rate of change in the volatility of the cassava chip price decreases. Therefore, the government’s formulation of an appropriate cassava policy should take volatility and the factors affecting price volatility into account. Additionally, the government’s formulation of agricultural policy needs to consider Thailand’s macro-environmental factors and its key trading partners, especially when these environmental factors signal changes in the price volatility of cassava.

Keywords: cassava price; volatility; Bayesian; GARCH-X; Thailand (search for similar items in EconPapers)
JEL-codes: E F I J O Q (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7099/9/3/132/pdf (application/pdf)
https://www.mdpi.com/2227-7099/9/3/132/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecomi:v:9:y:2021:i:3:p:132-:d:637559

Access Statistics for this article

Economies is currently edited by Ms. Hongyan Zhang

More articles in Economies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().