The volatility characteristics of Vietnamese coffee export price and transmission mechanism of influencing factors: a Markov switching approach
| DOI | https://doi.org/10.1108/JABS-04-2019-0134 |
| Published date | 26 July 2021 |
| Date | 26 July 2021 |
| Pages | 784-803 |
| Author | TrungTuyen Dang,Zhang Caihong,ThiHong Nguyen,NgocTrung Nguyen,Cuong Tran |
The volatility characteristics of
Vietnamese coffee export price and
transmission mechanism of influencing
factors: a Markov switching approach
TrungTuyen Dang, Zhang Caihong, ThiHong Nguyen, NgocTrung Nguyen and Cuong Tran
Abstract
Purpose –This study aims to examine the transmission mechanism of factors on the characteristic
fluctuationof Vietnamese coffee bean exportprice (PVN).
Design/methodology/approach –ApplyingMarkov switching–vector autoregressivemodel.
Findings –Significantly,the empirical results showed that the transmissionof independent variables on
PVN is non-linear, and the fluctuationof PVN is affected by many factors, especially PVN in the previous
period. In addition,the effect of Robusta coffee price was the greatestwith coefficient is 0.28785, and the
correlation between PVN and it was also the highest in both regimes with coefficients are 0.5317 and
0.3959,respectively.
Originality/value –These obtained results are in accordance with reality, as Vietnam is the largest
exporterof Robusta coffee in the world.
Keywords Transmission mechanism, Price volatility, Vietnamese coffee price, MS-VAR model,
Volatility characteristic
Paper type Research paper
1. Introduction
Coffee is one of the most valuable traded commodities of many developing countries over
the world, accounting for a relativelylarge proportion of world trade turnover. Each year, the
world exports more than US$20bn of coffee, of which the overwhelming majority (70%) is
produced by smallholder farmers in remote tropical regions (LDC, 2018). Commercial
coffee presents two main varieties: Arabica coffee and Robusta coffee. Vietnam is the
largest exporter of Robusta coffee in the world, with an export value of US$3.21bn (GSO,
2017). Significantly, Vietnamese coffee price is affected by not only the market price
volatility of internal industry factors such as the world coffee futures market price, but also
the fluctuation of non-agricultural products prices including the world oil price, exchange
rate and so on (Phuc and Hong, 2014;To,2015,2016).
During the study period, the fluctuation of Vietnamese coffee export price was almost
parallel with that of the world’s Robustacoffee price. After a long period of steadily climbing
from 40 US cents/lb in 2004 to 111 cents/lb in 2008, Vietnamesecoffee export prices had a
period of adjustment in the three years from 2008 to early 2011, and the transaction price
fell to only 70 US cents/lb. The price peaked at 110.16 US cents/lb in May 2011 before
hitting the lowest point of 74.71 US cents/lb in April 2016. Currently, Vietnamese coffee
export price is being traded at approximately 100 US cents/lb (FOB Ho Chi Minh price)
TrungTuyen Dang,
Zhang Caihong and
ThiHong Nguyen are all
based at Beijing Forestry
University, Beijing, China.
NgocTrung Nguyen is
based at Electric Power
University, Ha Noi, Vietnam.
Cuong Tran is based at the
Thai Nguyen University of
Agriculture and Forestry,
Thai Nguyen, Vietnam.
Received 30 April 2019
Revised 13 April 2020
24 September 2020
18 December 2020
Accepted 21 December 2020
PAGE 784 jJOURNAL OF ASIA BUSINESS STUDIES jVOL. 15 NO. 5 2021, pp. 784-803, ©EmeraldPublishing Limited, ISSN 1558-7894 DOI 10.1108/JABS-04-2019-0134
(Figure 1). Despite the fact that the fluctuation of Vietnam coffee export price is more stable
than that of Brazilian coffee price, it is still difficult to predict, as Vietnamese coffee export
price fluctuates without any rules. As a result, it is in significance to analyze volatility
characteristics and investigate which factors put the effect on Vietnamese coffee export
price in each various state.
In fact, the price volatility of agricultural commodities and the influence of the
microeconomic and macroeconomic factors on it have been studied by many authors. For
instance, the relationship between oil and an agricultural price has been reported by Serra
et al. (2011),Zafeiriou et al. (2018),Fasanya et al. (2019),Su et al. (2019),Taghizadeh-
Hesary et al. (2019) and so on; whereas the relationship among exchange rate and
agricultural prices has been examined by Nazlioglu and Soytas (2012),Devadoss et al.
(2014),Thuy and Thuy (2019),Dang et al. (2020)and so on. But the majority of these works
have investigated the relationship between two-time series variables, few studies have
researched the effect of independent variables on one dependent variable. Furthermore,
the main subjects of these studies are the main agricultural commodities such as corn,
soybean and wheat. The authors mainly used several popular models and methods as
cointegration test, Granger causality test, multiple linear regression model, vector
autoregressive (VAR) model and thresholdVAR. Recently, there are some researchers used
Markov switching –VAR (MS-VAR) modelto study the price volatility, such as Roubaud and
Arouri (2018),Sun and Wang (2018),Yıldırım et al. (2018),Chen et al. (2019) and so on.
However, the research subjects of these studies were not commodities price, especially
coffee price. The coffee price volatility has rarely been addressed. There are only some
researches of Vietnamese scholars regarding coffee price’s volatility in general and
Vietnamese coffee price’s volatility in particular, they mainly used the multiple linear
regression model and cointegration test (Nguyen and Tran, 2015;To,2015, 2016).
However, the biggest drawback of multiple linear regression model when estimating time
series data is spurious regression if the variables are not stationary at level (Granger and
Newbold, 1974;Hamilton, 1994;Ferson et al.,2003;McCallum, 2010). Meanwhile, it is
crucial to note that in VAR model or VECMmodels or their modifications, the parameters are
assumed to be constant over the sample period, in other words, the relationship among
variables or the influence of factors on them is stable. But this is not true in all the cases, as
the world has experienced many significant crises and events including the energy crises,
recessions, Southeast Asia crises and recent credit crises during the past decades. All of
these events could affect the relationship among economic variables. In time series
framework, therefore, these changes must be taken into account. If not, the estimated
parameters would be incorrect and misleading. One way to overcome this problem is to
Figure 1 The fluctuationof coffee prices
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
2004M1
2004M9
2005M5
2006M1
2006M9
2007M5
2008M1
2008M9
2009M5
2010M1
2010M9
2011M5
2012M1
2012M9
2013M5
2014M1
2014M9
2015M5
2016M1
2016M9
2017M5
Vietnam coffee bean export
price
Colombia coffee price
Brazilian coffee price
Robustas coffee price
ICO indicator price
Source: ICO, GSO of Vietnam and author’s calculation (Unit: US cent/lb)
VOL. 15 NO. 5 2021 jJOURNALOF ASIA BUSINESS STUDIES jPAGE 785
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