CITATION:Bakari, Y., (2023) PRICE VOLATILITY ANALYZE IN EARLY PANDEMIC COVID 19 OUTBREAKS: Case Study in Gorontalo Province Shallot Market, Agricultural Socio-Economics Journal, 23(1), 69-76 DOI:
PRICE VOLATILITY ANALYZE IN EARLY PANDEMIC COVID 19 OUTBREAKS:
Case Study in Gorontalo Province Shallot Market
Yuliana Bakari
Department of Agribusiness, Faculty of Agriculture, Gorontalo State University, Indonesia
*corresponding author: [email protected]
Abstract The Covid-19 pandemic in early 2020 led to unpredictable price fluctuation in agricultural commodities such as Shallots. The purpose of this research was to analyze the price fluctuation behavior of shallots in Gorontalo Province during the Covid-19 Pandemic and the price volatility surge as well as the effect of the pandemic during 2020. This study used data from The National Food Strategy Information Center in the form of weekly price in time-series from January 2018 to December 2020. The data analysis on price volatility was conducted using the Auto-Regressive Conditional Heteroscedastic and Generalized Auto-Regressive Conditional Heteroscedastic (ARCH / GARCH) Models. The results show that shallot price behavior randomly fluctuates every year, is more unpredictable, and has significant fluctuation over 2020. The ARCH / GARCH models prove that Pandemic Covid-19 in 2020 triggered the unpredictable price fluctuation and led to price volatility of shallots commodity in Gorontalo Province.
Keywords: fluctuation, Gorontalo, price volatility, pandemic, shallots.
http://dx.doi.org/10.21776/ub.agrise.2023.023.1.9 Received 9 August 2022 Accepted 20 September 2022 Available online 31 January 2023
INTRODUCTION
The Covid-19 pandemic that originated in Wuhan, China, has spread to 213 countries around the world including Indonesia. The pandemic hit Indonesia in early 2020. The increasing number of cases of the spread of Covid-19 in Indonesia not only has a significant impact on the health sector but also indirectly impacts several important sectors in the country, one of which is the agricultural sector. Manurung (2019) stated that Covid-19 was likely to have an impact on commodity prices, supply chains, as well as the health and safety of farmers, and could trigger other disturbances in the agricultural sector. Fao (2020) emphasized that Covid-19 could be the cause of supply and demand shocks/supply imbalances which in multiplayer could have an impact on the market and cause price fluctuation for agricultural products.
Price fluctuation that shows a significant decrease or increase in prices during the Covid-19 pandemic has hit many countries, including Indonesia. As happened in Syria where the increase in prices of foodstuffs was 40-50% higher than prices in the previous year due to limited local logistics or difficulties in importing, causing hunger and other crises (FAO, 2020). Furthermore, one of the regions known as the largest onion trading market in Asia, namely Maharashtra India, also experienced obstacles in the distribution process of shallots. This happened because most of the drivers and workers in the City Center were worried about the transmission of the Covid-19 virus so that the drivers chose to stop working and return to their hometowns. Another factor that has caused an increase in food prices in India was the freezing of transportation services and the scarcity
of fresh agricultural products during the lockdown period (Kim et al., 2020). As for Indonesia, according to Amanta & Aprilianti (2020) that at certain times such as religious holidays, there is indeed a food commodities scarcity which often causes fluctuations such as garlic, shallots, beef, and sugar.
Staple food commodities have been more volatile that contributes to high inflation, especially during the Covid-19 pandemic, therefore it is very important to pay attention to the dynamics and fluctuation of the prices (Agustian et al., 2020).
The strategic food commodities include rice, red chili, broiler eggs, broiler meat, bulk cooking oil, and shallots. The shallot commodity in Indonesia experienced an increased price of more than 40%
during the Covid-19 pandemic in 2020, especially in May-June period compared to the previous year (Agustina, 2020).
Significant and different patterns of shallot price fluctuation from the previous years also occurred in Gorontalo Province. Data from The National Food Strategy Information Center (2020) show that during 2020, the price of shallots reached Rp.44,000/kg in January which then increased to Rp.51,700/kg in February. In March-April the prices of shallots tended to be stable with the range of Rp. 43,000/kg, but in early May of that year there was a significant increase in prices that had never happened before which the price of shallots reached Rp. 65,000/kg. This price fluctuation with this unpredictable pattern of significant increases then triggered the tendency of shallot price volatility in Gorontalo Province.
Price volatility is defined as a very extreme price uncertainty due to an increase/decrease in prices with a very large variation from the main value which occurs suddenly, unpredictable, and difficult to anticipate, affecting the economic situation and increasing the risks that must be faced by all market players. One example of the impact of volatility on prices was concisely explained by Achsani (2011) that the volatility of international rice prices caused an imbalance in the trade balance and led to a decrease in the level of community welfare in ASEAN countries including Indonesia.
Moreover, Ajibade (2020) explained the impact of price volatility in the perspective of consumers and producers: from the perspective of consumers - the price volatility triggered the occurrence of poverty in the household and food insecurity in the long term; from the perspective of producers - price volatility posed output risk, disrupted resource allocation, cut investment, and especially affected
the condition of farmers because it caused price instability in the local market.
This certainly gives insight about the future that if the price volatility is not predicted properly, then the price behavior will be more difficult to predict which will only increase the risk that must be faced in the future. If future prices cannot be estimated correctly, then the risks faced by all market players will also increase and the losses will be greatly felt by farmers as producers. In addition, consumer welfare will also be affected and can cause inflation as well (Binswanger and Rosenzweig, 1986; Saha and Delgado, 1989 in (Apergis & Rezitis, 2011).
Therefore, a study on price volatility, especially as a result or impact of the Covid-19 pandemic in 2020 is necessary to conduct. Thus, the purpose of this study was to analyze the price behavior and the price volatility of shallots in Gorontalo Province during the COVID-19 pandemic in 2020.
METHODS
Based on the source, this study used secondary data with the type of time series data. The main variable analyzed was the price of shallots in Gorontalo Province using weekly data in time series from January 2018 to December 2020 and other supporting data such as data on demand and supply of shallots. The data sources were obtained from the National Food Strategy Information Center (PIHPS), Indonesian Agricultural Data Center and Information System (PUSDATIN), Statistics Indonesia (BPS), Ministry of Trade Republic of Indonesia, and the Ministry of Agriculture Republic of Indonesia.
The analysis of the data was carried out using time series econometric methods with several stages of analysis. In the early stages, the pattern price fluctuation of shallots was observed to gain information as the basis for determining the next analysis model. The following is a description of the stages of data analysis used in this research:
Stationary Test (unit root test)
Brooks (2008) explained two reasons that underlie the importance of stationary test, one of which was that the stationary nature of the data series would greatly affect the behavior and nature of the data because using or regressing non- stationary data would cause spurious regressions.
The unit root test was originally carried out and introduced by Dickey and Fuller (Fuller, 1976;
Dickey and Fuller, 1979) so that it is known as Dickey-Fuller (DF) tests. Assuming that there was an autocorrelation in the dependent variable (Δyt), which was not previously discussed in the DF model, this test was then developed and refined into an Augmented Dickey-Fuller (ADF) model.
The ADF model used in this study is as follows:
ΔPst = α0 + β1T + δPst-1 + εt ... (1) Explanation:
ΔPst =
Pst-Pst-1 (first difference operator) Prices of shallots in Gorontalo Province,
(Rp/Kg).
α0 = Constanta.
β1,β2 = Intercept.
δ = Coefficient
Pst-1 = Prices of shallots in one previous period (Rp/Kg).
εt = Error term variable.
Using Hypothesis:
If H0 : δ = 0, (Non- Stationary).
If H1 : δ < 0, (Stationary).
Testing Criteria:
If ADF Statistics < DF Critical Value does Not Reject H0, there is a unit root, the data is not stationary.
If ADF Statistics > DF Critical Value Reject H0, there is no unit root, the data is stationary.
Analysis on ARIMA Models
After performing a stationary test on the data, the next step was to formulate the ARIMA (Autoregressive Integrated Moving Average) model. From several ARIMA models that had been formulated, one of the best models that fit the testing criteria was then selected and followed by Heteroscedastic and ARCH Effect tests.
Mathematically, the ARIMA model used in this study is as follows:
Pst= μ + β1Pst-1+...+ βpPst-p+α1Psεt-1 +· · ·+αqPsεt-q+ εt ... (2)
Explanation:
Pst
= Prices of shallots in Gorontalo Province (Rp/Kg).
μ = Constanta.
β = Estimated coefficient of AR.
α = Estimated coefficient of MA.
p = Rate of AR/the amount of lag used in the AR model.
q = Rate of MA/the amount of lag used in the MA model.
The criteria for selecting the best ARIMA Model as follows: the best ARIMA model has random error; the estimated coefficient is significant; it has the smallest AIC and SIC values compared to other models; it shows a relatively small value of Standard Error of Regression and Sum Squared Residual; the value of Adjusted R Squared is relatively large (Asmara et al., 2011).
Furthermore, the Heteroscedastic and ARCH Effect tests on the best ARIMA model are carried out based on the test indicators, namely if the Chi- Square probability value is less than 5% of significance value then the model is heteroscedastic and contains ARCH elements (Firdaus, 2020).
Analysis on Price Volatility
After the series of tests performed in advance, then the price volatility test was carried out using the Auto-Regressive Conditional Heteroscedastic Model and the Generalized Auto-Regressive Conditional Heteroscedastic Model or commonly known as the ARCH/GARCH models.
σ2Pst= ɷ+ αε2Pst-1+βσ2Pst-1+ εt...(3) Explanation:
σ2Pbmt =
Conditional variance of squared residual prices of shallots in the tth period.
ɷ = Constanta.
ε2Pbmt- 1
=
Squared residual prices of shallots in one previous period.
σ2Pbmt- 1
=
Conditional variance prices of shallots in one previous period.
α1, β1 = Estimated Coefficient.
Testing Criteria:
The sum of the estimated coefficients (αi+βi) in the volatility model above shows the occurrence of volatility shocks with the following volatility criteria:
if the value of (αi+βi) < 1 indicates low volatility, if the value of (αi+βi) = 1 indicates high volatility,
if the value of (αi+βi) > 1 indicates extremely high volatility,
where the tendency of the volatility shocks will be greater if the accumulated value of the estimated coefficient (αi+βi) is closer to 1 (Bollerslev T, 1986; Piot-Lepetit, 2011; Ajibade T, 2020).
RESULTS AND DISCUSSION
The behavior of the price of shallots in Gorontalo Province shows a random movement pattern from year to year. In general, during the 2018-2019 period, the pattern of shallot price movements increased slowly at the beginning of the year and then decreased gradually in the following months with fluctuations that tended to be stable as illustrated in Figure 1. During this period, shallot prices increased steadily since the beginning of the year and reached its highest point in April and May, but then slowly began to decline in the following months. The highest price fluctuation of shallots occurred in 2018 in April reaching a price of Rp. 45.00/kg and at the beginning of May in 2019 reaching a price of Rp.
50,000/kg.
This condition coincides with the Holy Month of Ramadan and Eid al-Fitr Day. As the explanation of a farmer and shallot trader quoted from AntaraNews.com e-newspaper by (Sako, 2018), the increase in the prices of shallots during the Holy Month of Ramadan and ahead of Eid al- Fitr Day was caused by the soaring demand of the community for shallots which was not accompanied by the availability of supply of local shallots. The stock of shallots brought into Gorontalo Province was less than in the previous months. Even at that time, to obtain shallots from Makassar, traders had to compete with traders from the Manado area, thus becoming the main trigger for the high prices of shallots in April 2018.
Meanwhile, in 2020, the pattern of price movements becomes unpredictable. The price chart in Figure 1 shows the pattern of shallot prices movements in 2020 is very different from 2018 and 2019. This is indicated by the price surge that occurred at the beginning of 2020 followed by a very significant price surge in May 2020. The significance of the increase in shallot prices in the May-June 2021 period did not only occur in Gorontalo Province but throughout Indonesia.
Agustian (2020) stated that the cause of the surge in shallot prices in Indonesia in the May-June 2020 period, which reached 40% higher than the previous year, was the impact of the
implementation of the 2020 Indonesia Large-Scale Social Restrictions (PSBB) policy which disrupted the distribution of shallot supplies so that supply was hampered and ultimately resulted in increased prices.
Figure 1. Weekly price fluctuation of shallots commodity for the 2018-2020 Period Sources: Information Center for National Food
Prices (2020)
Based on the pattern of price movements, the price fluctuation of shallots tends to be random and unpredictable causing the data to be non-stationary.
Thiswas the basis for conducting a stationary test of data at an early stage using unit root test which specifically used the AugmentedDickey-Fuller (ADF) unit root test. The results of statistical testing are as follows:
Table 1. Stationary Test Results of Prices of Shallots on 2018-2020 Period
Variable
Level First
Differences DF
Statistic DF Critical
Value
Prob DF
Statistic DF Critical
Value Prob Shallot
prices -0.55 -1.94 0.477 -7.95 -1.94 0.00
Explanation:
* Significant at the rate of 5%
There were two stages in the stationary test, namely Level testing and First Differences testing.
The test results at the Level testing with order I(0) indicated that the absolute value of DF Statistics (- 0.55) was smaller than the absolute value of DF Critical Value 5% (-1.94) and the probability value (0.477) was greater than the significance value of 5% (0,05). The DF Statistical Value which was smaller than the DF Critical Value at a certain level of significance indicated that the data was not stationary (Brooks, 2008). Thus, the test results indicated that the data were not stationary at the level testing or order I(0).
Continuing the statistical test using non- stationary data can lead to spurious regression.
Therefore, the stationary test of the data must be continued at the Difference Non-stationary Process
stage at the first level or first differences. The results of the analysis showed that the stationary data at the first level (first difference) with order I(I) had the DF Statistical Value (-7.94) greater than the absolute value of the DF Critical Value (- 1.94) and the probability value (0.00) was smaller than the significance value of 5% (0.05). Thus, the
results of testing the prices of shallots were stationary at the first level or the first difference.
Furthermore, several tentative ARIMA models were determined, which the AR and MA orders had previously been determined using a colleogram based on the ACF and PACF values. The tentative ARIMA model obtained is presented in Table. 2 as follows:
Table 2. Test Results of Tentative ARIMA Models ARIMA
Models
(p, d, q) Coefficient
Criteria of Model Selection
Best Model Sig R2 AIC/SIC Prob
F stat
Sum Squared Residual
S.E Reg
ARIMA (1,1,1)
3313 0.86 0.24
0.00 0.00 0.00
0.81
19.502 19.583
0.00 2.39E+09 4076 1
ARIMA (1,1,2)
3341 0.93 -0.10
0.00 0.00 0.29
0.80 19.536
19.617 0.00 2.47E+09 4145 2
ARIMA (2,1,1)
3328 0.821 0.93
0.00 0.00 0.00
0.80 19.540
19.621 0.00 2.48E+09 4153 3
Explanation:
* Significant at the rate of 5%
Determination of the best tentative ARIMA model in Table. 2 above was done based on several criteria. The criteria for selecting the best ARIMA model are that the model has random errors;
estimated coefficient is significant; the smallest AIC and SIC values compared to other models; The Standard Error of Regression is relatively small;
The Sum Squared Residual is relatively small; and Adjusted R Squared is relatively large. (Asmara et al., 2011). Based on the test criteria, the best model was obtained, namely ARIMA (1,1,1).
Mathematically the ARIMA (1,1) model is written as follows:
Pst = 3313 +0,86 Pst-1 + 0,24Psεt-1+ εt Before carrying out the volatility test by building the ARCH/GARCH model, the Heteroscedastic ARCH-Test was first carried out to determine the effect of ARCH on the ARIMA (1,1,1) model. The heteroscedastic test was used to prove that the data were heteroscedastic and had an ARCH effect so that the ARCH/GARCH model could be created. Based on the test indicators, if the Chi-Square probability value is less than the significance value of 5%, the model is heteroscedastic and contains ARCH elements.
(Firdaus, 2020). The test results showed that the probability value of Chi-Square (0.00) was smaller than the significance value of 5% (0.05) indicating the presence of heteroscedasticity elements in the
ARIMA model (1,1,1) as well as the ARCH element so that it could be continued to the volatility test using the model ARCH/GARCH.
Volatility Test Results of Shallot Prices in Gorontalo Province
Volatility is one of the important issues studied because variations in price movements of a commodity including agricultural commodities cause prices to fluctuate. (Gilbert, 2011) explains that the prices of agricultural commodities vary because the prices of production and consumption are not fixed (variable by distinguishing the ability between the predictable changes (variability) of demand and supply and the unpredictable changes of supply and demand or known as supply and demand shocks).
Volatility began to be studied in 1960 by considering the frequency of observed prices (Huchet-bourdon, 2011). Volatility began to be studied in 1960 by considering the frequency of observed prices, while in the field of agricultural economics, research on volatility began to develop around 1980. In the last 10 years, a study conducted by (Anderson, 2012) concerning the factors that affect the prices of commodities in the future and the volatility in eight main markets for agricultural commodities. Furthermore, research on volatility using the ARCH-GARCH model began
to develop to examine the "backward-looking" of prices or known as historical volatility (O’Connor, 2011; Piot-Lepetit, 2011). One of the most recent studies conducted by (Ajibade et al., 2020) which examined the price volatility that occured in Nigeria and the factors that influenced it by using the GARCH model.
Based on several previous studies, the price volatility test for shallots in Gorontalo Province was also measured using the ARCH/GARCH model approach. Table. 3 shows the volatility test results of shallot prices in Gorontalo Province.
Table 3. Volatility Test Results of Shallot Prices in Gorontalo Province
Variance Equationof Shallot Prices Price Volatility Variables Coefficient Std.
Error Prob.
C 581661.2 171031.3 0.0007 High RESID(-
1)^2 0.099858 0.036044 0.0056 GARCH(-
1) 0.831898 0.041263 0.0000 Explanation:
* Significant at the rate of 5%
Based on the results of statistical analysis, the model of shallots price volatility in Gorontalo Province that was formed is as follows:
σ2Pst = 58,166 + 0,09ε2Pst-1 + 0,83σ2Pst-1 + εt The trend/tendency of price volatility can be measured based on the estimated coefficientof the ARCH/GARCH model. The sum of the estimated coefficients (αi+βi) in the volatility model showed that there was volatility in the prices of shallots in Gorontalo Province.
The tendency of price volatility to be greater if the accumulated value of the estimated coefficient (αi+βi) shows a value that is closer to 1. The accumulated value of the estimated coefficient can also be used as a benchmark in determining the level of volatility. The value of (αi+βi) < 1 indicate low volatility, the value of (αi+βi) = 1 indicate high volatility, and value of (αi+βi) > 1 indicate extremely high volatility (Bollerslev T, 1986; Piot- Lepetit, 2011; Ajibade T, 2020). The accumulated value of the estimated coefficient of the shallots price volatility model in Gorontalo Province was 0.92. It explains that the prices of shallots in this province were included in the high volatility criteria.
The period used in Figure 1 is the period 2018- 2019 before the occurrence ofCovid 19 and in 2020 at the time of the Covid-19 pandemic. Based on the fluctuation pattern, it can be concluded that price
movements in 2018 and 2019 tend to be stable with almost the same fluctuation pattern. Meanwhile, in 2020, price fluctuation occurred significantly, unpredictable, and had never occurred in previous years. The prices of shallots increased very significantly to reach Rp. 65,000/kg in 2020, which was at the beginning of the Covid 19 pandemic.
This explains that the Covid-19 pandemic led to more volatile and unpredictable prices.
Basically, the Covid-19 pandemic at the beginning of 2020 caused a supply and demand shock that created a multiplayer effect and led to high volatility in agricultural commodity prices. If examined from the supply side, one of the contributing factors was the implementation of several health protocol policies in dealing with the Covid-19 pandemic in Indonesia, including Gorontalo Province. Poudel et al., (2020), in his research explained that the Covid-19 Pandemic Protocol such as travel restrictions, border closures, and social distancing affected every stage of the market supply chain with a major impact on food distribution.
The same situation also occurred in the shallot market in Gorontalo Province where the implementation of travel restrictions and border closures since the end of March 2020 restricted the movement of shallot traders who transported shallot supplies from outside the region. This certainly had a significant impact on the amount of shallot supply in Gorontalo Province, especially because 78.11% of the need for shallots in Gorontalo Province were supplied from outside the region and only 21.89% was met by local farmers (Adhiwibowo, 2019). On the other hand, during the pandemic conditions, local traders in Gorontalo Province experienced difficulties in obtaining imported shallot stocks. As a result, shallot traders were only able to fulfill 50% of the shallot stocks than the usual markets thus forcing traders to increase selling prices in order to gain profit.
(Hasanuddin, 2020). During this period there was a supply shock in the shallot market causing prices to become more volatile.
On the other hand, demand shocks also occurred simultaneously in April-May 2020. This was due to the fact that it coincided with the Holy Month of Ramadan and Eid al-Fitr, resulting in an increase in demand for shallots to meet the daily needs of the community. Nevertheless, the price surge caused by religious holidays remains predictable as it shows the same pattern as in previous years. However, during these years, the magnitude of the increase became very significant
because it coincided with the effects of the Covid- 19 pandemic.
Thus, the results of this study prove that the impact of the Covid-19 pandemic has triggered the high volatility of shallot prices in Gorontalo Province. This is in line with the research of Ana Frasipa, (2021) which stated that the Covid-19 pandemic had a positive effect on the price volatility of rice in Indonesia, which occurred due to the implementation of the PSBB policy causing the demand to increase significantly and prices to be more volatile.
Therefore, this high price volatility must be addressed immediately. As written in (Apergis &
Rezitis, 2011) that the high price volatility makes price movements more difficult to predict which can have an impact on all market players and on consumer welfare and even trigger inflation. On the other hand, the findings of (Bakari et al., 2013) showed that high volatility tended to occur in a long time with an increasing value, so that a policy in providing price subsidies is needed from the government as a solution.
In the case of this study, a policy recommendation which can be made by the Gorontalo Provincial government as a solution to the surge in shallot prices in the Covid-19 pandemic era is to increase the shallot production of local farmers to maintain the stability of the shallot supply in Gorontalo Province without depending on supply from outside the area. In addition, there is a need for the implementation of policy regarding the basic prices and market prices of shallots as a short-term solution in anticipation of a sudden price increase. These policy recommendations are given as an effort to maintain price stability, especially during the Covid-19 pandemic so that the price volatility can also be suppressed.
CONCLUSION AND RECOMMENDATION CONCLUSION
1. The behavior of the prices of shallots in Gorontalo Province shows a random pattern of movement from year to yearwith significant and unpredictable price fluctuation patterns in 2020.
2. The test results of the ARCH/GARCH model prove the surge in shallot prices during the Covid-19 pandemic with high price volatility.
RECOMMENDATIONS
1. Policy recommendations that can be taken by the government as an effort to reduce the level of price volatility are to increase local shallot production, set a basic prices, and implement a low-cost market.
2. It is necessary to conduct a more in-depth research on the factors that influence the volatility of shallot prices in Gorontalo Province which focuses on policies that should be taken by the government in dealing with volatility problems, especially those related to the agricultural commodities.
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