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Agro Ekonomi Vol. 28/No. 2, Desember 2017

157

VOLATILITY ANALYSIS ON PRODUCER PRICE OF RED PEPPER AND
CAYENNE PEPPER IN WEST JAVA PROVINCE INDONESIA
Analisis Volatilitas Harga Produsen Cabai Merah dan Cabai Rawit
di Provinsi Jawa Barat, Indonesia
Candarisma Dhanes Noor Viana, Slamet Hartono, Lestari Rahayu Waluyati
Faculty of Agriculture, Universitas Gadjah Mada
St. Flora, Bulaksumur, Caturtunggal, Depok District, Sleman Regency, Daerah Istimewa
Yogyakarta, Indonesia
candarismadhanes@gmail.com
Diterima tanggal : 14 Juni 2017 ; Disetujui tanggal : 6 November 2017
ABSTRACT
This study aims to determine the price volatility in producer of red pepper and cayenne
pepper in West Java Province. The data used in this research was secondary data focusing
on monthly price statistics of red pepper and cayenne pepper producer in West Java Province
from 2008 to 2015. The method used in this research was ARCH/GARCH method with best
model determination. Based on the research, it was found that the best model for the price
of red pepper in producer was GARCH (1,1) with the volatility value of 0,96. It means

that volatility of price of red pepper in producer level was inluenced by the increase and
luctuations on producer price of red pepper one month before. The best model for the price
of the cayenne pepper producer was ARCH (1) with the volatility value of 0,52. It means
that price volatility of cayenne pepper in producer level was also inluenced by luctuations
producer price of cayenne pepper one month before. The value of volatility price of red
pepper in West Java Province is closed to one, so it can be categorized of high volatility.
Meanwhile, the price on the producer of cayenne pepper in West Java Province can be
categorized of low volatility.
Keywords : ARCH/GARCH, cayenne pepper, red pepper, volatility
INTISARI
Penelitian ini bertujuan untuk mengetahui volatilitas harga produsen cabai merah
dan cabai rawit di Provinsi Jawa Barat. Data yang digunakan dalam penelitian ini
yaitu data sekunder statistik harga produsen cabai merah dan cabai rawit Provinsi
Jawa Barat tahun 2008 hingga 2015 yang disajikan dalam bentuk bulanan. Metode
yang digunakan dalam penelitian ini yaitu metode ARCH/GARCH dengan penentuan
model terbaik. Berdasarkan penelitian tersebut diperoleh bahwa model terbaik untuk
harga produsen cabai merah yaitu GARCH (1,1) dengan nilai volatilitasnya sebesar
0,96 hal ini berarti bahwa volatilitas harga produsen cabai merah di Provinsi Jawa
Barat dipengaruhi oleh peningkatan harga satu bulan sebelumnya dan luktuasi harga
satu bulan sebelumnya. Model terbaik untuk harga produsen cabai rawit yaitu ARCH

(1) dengan nilai volatilitasnya sebesar 0,52, hal ini berarti bahwa volatilitas harga
produsen cabai rawit di Provinsi Jawa Barat dipengaruhi oleh peningkatan harga satu
bulan sebelumnya. Nilai volatilitas harga produsen cabai merah di Provinsi Jawa Barat

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mendekati satu sehingga dapat dikatakan volatilitasnya tinggi. Harga produsen cabai
rawit di Provinsi Jawa Barat dapat dikategorikan volatilitasnya rendah.
Kata kunci : ARCH/GARCH, cabai rawit, cabai merah, volatilitas
INTRODUCTION

The seasonal nature causes an

Horticulture is one of the important

abundant supply of chili commodity in

sub-sectors in agricultural development

harvest season, so that the price of chili

and is one of the strategic commodities

goes down. Whereas when the supply is

to be developed. Based on the National

limited, the price of chili rises. This is

Medium-Term Development Plan 2015-

because chili is a seasonal commodity that

2019 of Directorate General of Horticulture,

depends on the climate and a perishable

horticultural commodities will become a

commodity, so chilies cannot be planted

strategic issue that gets serious attention

and harvested throughout the year.

from the government and business actors

Based on the research conducted by

that inluences inlation and contributes

Aryasita (2013), the supply of chili price still

to the national economic growth. One of

depends on the amount of chili produced,

horticultural commodities that becomes a

while the amount of chili produced depends

strategic issue and inluences inlation is

on the harvest area and land productivity.

chili. Most people of Indonesia use chili

The amount of chili on offer changes every

as a raw material to cook every day. Chili

year. This is due to luctuations in harvest

is not only used in household scale but

area and production of chili. The amount

also used as a raw material in culinary and

of chili on offer also depends on rainfall.

industrial businesses, both food and non-

During high rainfall, the amount of chili

food industries. In recent years, there are

production will be small, so the price of

many culinary businesses using chili as the

chili becomes expensive. The price of chili

raw material. Moreover, the public interest

also inluenced by production and rainfall,

towards the products offered is also high.

according to the research conducted by

One type of chili that is often sought by

Farid and Subekti (2012), the factors that

consumers is red chili. The red chili is one

inluence the price of chili in Indonesia

of the most vulnerable agricultural products

are production pattern, production cost,

experiencing price luctuations. Prices can

distribution, consumption during fasting

jump high but can also turn into very low.

month and Eid el-Fitr.

Game prices of red chili by middlemen

Fluctuation in the price of chili

lead to losses to farmers (Devi et al, 2015).

caused by various factors makes people

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worry. This is because chili has become

chain. High prices will cause high production,

one of the staple food as a side dish

then a lot of supply will result in low prices,

to complement the main course. Price

and will lead to low production, and so on.

becomes one of the indicators of food

Three models of cobweb are convergent,

suficiency of society, so, price stability is

divergent, and continuous.

needed to support the stability of national
economy (Edi et al, 2014).

The following are some studies on
price volatility that have been conducted,

Basically, excess supply or excess

one of this is the research conducted by

demand causes high price and unpredictable

Asmara et al (2011) showing that world oil

fluctuation. Gilbert and Morgan (2011)

price volatility varies over time. According

state that low supply and high demand

to Bakari et al (2013), the volatility values

cause price to rise and vice versa, so the

of cooking oil price in the world and

price becomes more volatile. Too high price

Indonesia are close to one, indicating a

and unpredictable luctuation can increase

long-term volatility trend with a greater

price volatility. According to Hirshleifer

volatility level, resulting in high volatility

(1984) the market equilibrium represents

in the future to be cautious, especially as

the amount demanded which is equal to the

a result of an increase in fuel or biofuel

amount offered. This is different from the real

demand for its effect on the price of CPO

conditions occurring in the ield where there is

at global level.

an often high and unpredictable luctuation on

Other research that use ARCH/

price due to the imbalance between the supply

GARCH model is conducted by Sumaryanto

and demand from the consumers. As Lepetit

(2009) the volatility of retail price among

(2011) if the volatility is more increasing, then

food commodities such as rice, wheat lour,

price uncertainty in the future will be higher.

granulated sugar, eggs, cooking oil, red

High price luctuation also allows traders to

chili, and onion indicates that the prices of

manipulate price information at the farmer

food commodities became more volatile.

level. If the price luctuation and uncertainty
gets higher, it will harm some parties.

Other studies about volatility with
ARCH/GARCH method is research

Cobweb model is a theory that explains

according to Wijaya et al (2014), the

the relationship between price and quantity

volatility of garlic producers price in East

(in this case that is production), so this

Java before and after trade liberalization

model can be used to illustrate the state

showed high volatility. This is because

of the volatility of a product seen from

garlic production in Indonesia cannot meet

the price. Price and quantity are described

the demand after trade liberalization, so

interconnected as a continuous and recurring

the demand is met by increasing imports.

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Increased imports cause the price of garlic

analysis used was volatility analysis using

producers in the market to increase, causing

ARIMA and ARCH/GARCH. Here was an

price luctuations more dificult to predict

analytical framework (steps) conducted in

and price uncertainty at the producer level.

the research (Figure 1).

Price uncertainty is commonly referred to
as a price volatility problem.

Producer Price
Correlogram

West Java Province is one of the

Producer Price
Stationary Test

chili suppliers which supplies both Spanish
pepper (red chili) and cayenne pepper
to various regions of Indonesia to meet
consumer demand. When the inventory or

Estimation of
ARIMA

No

Augmented
Dickey Fuller
(ADF)

Model
Heteroscedasticity Test

stock is reduced, it will lead to a mark-up
in all regions in Indonesia. Some districts

Estimation of ARCHGARCH Model

in West Java that contribute greatly to the
supply of red chili and cayenne pepper are
Garut, Cianjur, Bandung, Tasikmalaya,
Sukabumi, and Purwakarta. Garut is one
of the areas with the highest production
of red chili and cayenne pepper with

Diagnosis Residual
Test

ARCHLM

Volatility Analysis

Figure 1. Analytical Framework
Notes :

contribution of 31.44% and 28.29%. Based

: next step

on the description, this research aimed to

: choose one method

know the volatility of prices at the producer
level both on red chili and cayenne pepper
in West Java Province as one of the centers
of chili production in Indonesia.

Stationaryity Test
To avoid spurious regression, the
data analyzed must be stationary (Diebold
& Killian, 2000 cit Sumaryanto, 2009).

METHODS

The stationary data are the ones that do not

The type of data used in this study

contain the root unit. Root unit test method

was secondary data. Secondary data

used was the test of Augmented Dickey

used were Producer Price Statistics of

Fuller (ADF). Criteria of conclusion

Red Pepper and Cayenne Pepper for

stationaryity test:

8 years (2008-2015) which presented

i) If the ADF statistic value ≤ the critical

monthly. The data used was obtained

value of MacKinnon α = 1%, α = 5%, α

from Central Bureau of Statistics (BPS).

= 10%, so data of chilli producer price

Based on the purpose of this research, the

are not stationary

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161

that is signiicant, fulilling Durbin Watson

ii) If the ADF statistic value >
MacKinnon critical value α = 1%,

test requirements and the probability of the

α = 5%, α = 10%, so data of chilli

F-test (Sumaryanto, 2009).

producer price are stationary

The following is a common form of
ARCH and GARCH models:

Heteroscedasticity Test

ARCH Model :

The best model that has been obtained

Yt = β0 + β1X1t + εt

is then tested to determine whether the
ARIMA model contains heteroscedasticity
or not by using ARCH Lagrange Multiplier
(ARCH-LM test). If the value of Obs*R-

GARCH Model :

squared > X2 table value, the model will
contain heteroscedasticity problem or
it can be said that the model contains

Notes :

ARCH element. Thus, the analysis can

Y

: dependent variable

be continued for ARCH/GARCH model

X

: independent variable

estimation.
Criteria of conclusion:

ε

: residual

σt2

: residual variance

ω

: mean

i) If Obs*R-squared value ≤ X table
2

value, so the data of chilli producer

: component of ARCH

price is not contain heteroscedasticity

: component of GARCH

element
ii) If Obs*R-squared value > X2 table
value, so the data of chilli producer
price is contain heteroscedasticity
element

After the estimation of the ARCH/GARCH
model is completed, a further test of
residuals is required through ARCH LM
test to determine whether there is no
remaining ARCH effect or Correlogram

Estimation of ARCH / GARCH Model
Generally ARCH/GARCH model

Squared Residuals (CSR) test (Sumaryanto,
2009).

estimation can not be done once, but it
takes several times of ARCH/GARCH
form tests with different assumptions
of the distribution so that the parameter
coeficient is eligible (matching mark and
its size range as required in ARCH model)

Volatility Analysis
Volatility can be known by looking
at the value of α+β. The value of α is the
ARCH value whereas the value of β is the

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GARCH value. Based on the value of α+β,

price of producer price of red peper was

the volatility can be categorized as follows

Rp 7.940/kg on July, 2011 and the highest

(Lepetit, 2011) :

price of producer price of red pepper was

i) If α+β = 1 or α+β >1, the volatility is

Rp 14.367/kg on August, 2015.

high
ii) If α+β MacKinnon critical value at α =

of spurious regression. Data is said to

1%, α = 5%, and α = 10%, then the data

be stationary if the value of mean and

can be said to be stationary.

variance are constant. To identify whether

Table 1 of stationery test results

the data is stationary or not can be done

using ADF (Augmented Dickey Fuller)

by looking at the ACF (Auto Correlation

method shows that the prices of red chili

Function) value on the results of the

and cayenne pepper in West Java province

correlogram. ACF describes how big the

is stationary at the level and conidence of

correlation of consecutive data in time

99%. After knowing the data of producer

coherence. The data to be used must

prices of red chili and cayenne pepper to be

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Table 2. The Best ARIMA Models for Producer Prices of Red Chili and Cayenne Pepper
in West Java Province
Commodity
Red Chili
Cayenne Pepper

Best Model

R2

ARIMA (1,0,1)
ARIMA (3,0,2)

0.684
0.832

S.E. of
Regression
0.071
0.073

Log likeli
hood
118.733
116.991

AIC
-2.390
-2.292

SIC
-2.283
-2.105

Source: Secondary Data Analysis, 2017
in stationary state, the next step taken was

can be written as follow :

to determine the best ARIMA models by

HPCMJBt = 9.311805 + 0.737631

determining the best AR and MA degrees.

AR(1) + 0.279102 MA(1) +

AR model is a form of relationship between

et

dependent variable (producer price of red

(1)

HPCRJBt = 9.253871 + 0.28804 AR(1)

pepper or cayenne pepper) with independent

– 0.681920 AR(2) +

variable (previous period of producer price

0.643340 AR(3) + 0.487308

of red pepper or cayenne pepper. While MA

MA(1) + 0.716670 MA(2) +

model shows the dependence of dependent

et

(2).

variable of producer price of red pepper
or cayenne pepper on the residual values

Based on equations 1 and 2, it can be

of the previous period in sequence. The

interpreted that the producer price of red

determination of AR and MA to be used

chili in West Java Province is inluenced by

for the best model estimation is by using

the producer price of red chili in West Java

trial and error method, by considering

Province one month earlier and luctuation

the autocorrelation line and the partial

in producer price of red chili in West

correlation line out of the Bartlett line.

Java Province one month earlier. While

Based on the trial and error results,

the producer price of Cayenne pepper in

the best ARIMA models for the producer

West Java Province is inluenced by the

prices of red chili and cayenne pepper in

producer price of Cayenne pepper in West

West Java Province is obtained as shown

Java Province three months earlier and

in Table 2. The best ARIMA models were

luctuation in producer price of Cayenne

chosen based on several criteria, namely,

pepper in West Java Province two months

high R2 and log likelihood values, AIC

earlier.

(Akaike Info Criterion) value, SIC (Schwarz

The next step taken after obtaining the

Criterion), low standard error value and

best ARIMA models was to identify whether

signiicant variable. The equations for the

the model contains heteroscedasticity

best ARIMA models that have been obtained

element or not. It aimed to continue to the

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Table 3. The Results of ARCH/GARCH of Producer Price of Red Chili and Cayenne

Pepper in West Java Province
Commodity
Red Chili
Cayenne Pepper

Best Model

R2

S.E. of
Regression

Log
likelihood

AIC

SIC

GARCH (1,1)
ARCH(1)

0.678
0.829

0.072
0.073

126.391
123.978

-2.508
-2.416

-2.348
-2.203

Source: Secondary Data Analysis, 2017
Table 4. The ARCH-LM Test Results
Commodity
Red Chili
Cayenne Pepper

Prob. F (36,23)
0.9617
0.8764

Prob. Chi-Square (36)
0.8631
0.7340

Source: Secondary Data Analysis, 2017
next analysis, is volatility analysis. One of

and log likelihood values, standard error

the methods used to know the existence of

value, AIC (Akaike Info Criterion), low

heteroscedasticity effect is by correlogram

SIC (Schwarz Criterion), and signiicant

squared residuals value. If the probability

variable. The next step taken after obtaining

value of correlogram squared residuals is

the best ARCH/GARCH models was to

signiicant, then it can be said that the model

retest whether the ARCH/GARCH models

still contains heteroscedasticity. The results

still contain heteroscedasticity element or

of the test heteroscedasticity by correlogram.

not. One of the tests that can be done was

The best ARIMA models of producer price

by ARCH-LM test.

of red chili and cayenne pepper in West Java

Based on Table 4 of the ARCH-

Province in some lags were signiicant. So,

LM test results, the best ARCH-GARCH

it can be said that the best ARIMA models

estimation model of producer prices of

listed in Table 2 contain ARCH element

red chili and cayenne pepper in West Java

or the residuals is random. Thus, the next

Province does not contain heteroscedasticity

analysis can be performed, i.e., to look for

element. This can be seen from the prob.

the best models for the ARCH-GARCH.

Chi-Square value greater than 0,05. Thus,

The estimation results of ARCH/GARCH

the next step can be taken, i.e., to know the

best models will be used to estimate its

volatility value from the sum of coeficients

volatility value, whether it is included in

α and β. The following are equations of the

high volatility or low volatility category.

volatility of producer price of red chili and

The selection of the best ARCH/
GARCH models was based on high R2

cayenne pepper in West Java Province.

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Table 5. The Results of Volatility Analysis of Producer Prices of Red Chili and Cayenne
Pepper in West Java Province
Variable
Red Chili
Cayenne Pepper

Variance Equation
σt2 = 0.000239 + 0.200256 ε2t-1 + 0.756427 σ2t-1 + εt
σt2 = 0.002552 + 0.522501 ε2t-1 + εt

Volatility
0.96
0.52

Source: Secondary Data Analysis, 2017
The factors inluencing the volatility

Based on Table 5, the volatility

of the producer price of red chili in

value of producer price of red chili in

West Java Province were the volatility

West Java Province is smaller than one,

of producer price of red chili in West

so it can be categorized as low volatility

Java Province one month earlier and the

but it is almost close to one. The volatility

variance of producer price of red chili in

of producer price of red chili in West Java

West Java Province one month earlier.

Province cannot be interpreted to be stable

While the factor inluencing the volatility

and always predictable because its value

of the producer price of cayenne pepper in

is less than one. However, this should

West Java Province was the volatility of

be noted because the volatility value

the producer price of of cayenne pepper

approaching one indicates the tendency of

in West Java Province one month earlier.

volatility to last in a long time with greater

The volatility value of producer price

volatility level. Supply and demand shocks

of red chili and cayenne pepper in West

inluence the volatility of high price, i.e.,

Java Province can be seen by looking at

the demand is increasing but not balanced

the value of α and β. According to Lepetit

with suficient stock due to the decrease in

(2011), volatility can be known by looking

production. In addition, volatility of high

at the value of α + β. The α value is obtained

price is also caused by weather and disease

from the ARCH value, while the β value is

in plants. Chili is a commodity that cannot

obtained from the GARCH value. If α + β

be stored for a long time, so if there is no

= 1 or α + β > 1, then it can be said to be

good storage treatment, chili will quickly

high volatility. Whereas if α + β < 1, then it

decay, especially during the rainy season.

can be said to be low volatility. According

So, during the rainy season, the supply will

to Burhani (2013), if the ARCH value is

decrease, thus driving the price luctuation.

relatively low or not close to one, it indicates

The volatility of producer price of

that the volatility is low. Whereas if the

cayenne pepper in West Java Province can

GARCH value is relatively high or close to

be said to be low. It is seen from the value of

one, it indicates that the shocks in the price

0,52, meaning that the volatility of producer

variance will luctuate within longer period.

price of cayenne pepper occurred only in

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167

certain period and the time is relatively short.

resource availability, supply elasticity will

The volatility of cayenne pepper price in West

change so that production cannot continue to

Java Province is classiied as low volatility

be done. As for the volatility producer price

but still must be considered because of the

of cayenne pepper in West Java Province was

possibility of price luctuation which will

0,52 so it can be classiied into low volatility.

cause high volatility.

The Cobweb model that can be represented

The theory that explains the

this result was Covergent Cobweb Model.

phenomenon of price volatility is Cobweb

Covergent Cobweb Model explains that this

theory. Cobweb theory explains the price

model leads to equilibrium point. This occurs

cycles and quantities of production that

due to the elasticity of supply which is smaller

luctuate over time. Based on the results of

than the elasticity of demand.

analysis using ARCH-GARCH method to

One district in West Java Province

determine the value of volatility, it is obtained

which is a center of chili production is Garut

that the volatility of producer price of red

Regency. According to research conducted

pepper of West Java Province is 0,96 so it

by Anwaruddin (2015) the contribution of

can be said high volatility. High volatility

Garut Regency to the production of chili

can be said to stay away from the balance

in West Java Province (BPS West Java

point. Cobweb model that can describe the

Province, 2011) is 28,49% to 30,77% while

phenomenon is Divergent Cobweb Model.

the contribution to the country is 6,52% to

The Divergent Cobweb Model occurs as

7,41%. The average productivity of chili in

a result of greater supply elasticity when

Garut Regency is 15,07 tons per hectare while

compared to demand elasticity. Price cycle

the average productivity of Cayenne pepper

begins with an offer of Q0 , that it is a lack

in Garut Regency is 9,82 tons per hectare.

of the number of goods offered so that prices

Compared with chili productivity nationally

will tend to increase. As the price increases

that is 5,53 to 5,89 tons per hectare, chili

then the offer will also increase so that it

productivity in Garut Regency is considered

changes to Q1. An increase in supply will lead

high. The planting of chili in Garut Regency

to a fall in prices. The fall in prices will lead

is done both on dry land and in paddy ield.

to a decrease in the amount of production,

On dry land, chili is usually planted ahead of

this will lead to an increase in prices. High

the rainy season while in the paddy ield, chili

prices again encourage a larger increase

is usually planted before the dry season. The

in the production side resulting in excess

pattern of pepper crop is mostly polyculture.

supply. This cycle will occur continuously

The planting time of chili in paddy ield,

until the end of production becomes very

rainfed paddy ield, and on dry land lasts

abundant or there is scarcity related to

for 6 months, namely, April, May, June,

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September, November, and December. The

The price of chilies at the producer level that

harvest period of chili lasts for 10 months

luctuates from one period to another causes

from January to October and there is no chili

the possibility of price at the consumer level

harvest in November and December, so there

experiencing luctuations, such things can

is no supply of chili for two months in Garut.

be detrimental both at the community and

The absence of chili stock for two

national level. Thus the need for further

months can affect the price both at provincial

research on price volatility at the consumer

and national level. When there is no stock,

level and research on the factors that

the price is rising and the condition will get

inluence volatility, as well as the attention

worse with increasing demand for chili due

to the issue of the price, is by monitoring

to religious festivals. Thus, the low volatility

the stock or supply of chilli sufficient.

of producer prices in West Java Province is

Thus, it is necessary to address the problem

caused by the supply of chilli that can meet

of volatility. One of the ways is on-farm

the demand, and some chilli needs in West

activities. One of the on-farm activities that

Java Province can be met from the region

can be done is to improve the management

itself or still in one province.

of cropping patterns of red pepper and
cayenne pepper. So that either during the

CONCLUSION AND SUGGESTION
Price volatility at the producer level of
red pepper and cayenne pepper in West Java

harvest season or not, the supply will still be
available and the price does not soar both at
the producer and consumer level.

Province can be categorized as low volatility
because the value of volatility is smaller than

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Marendra, dan Y. Hilman. 2015.

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Dinamika produksi dan volatilitas

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