PRICE FORECASTING OF STRATEGIC FOOD COMMODITIES IN VARIOUS MARKETS IN MALANG REGENCY: IMPLEMENTATION OF THE

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CITATION: Sujarwo, Laili, F., (2023) PRICE FORECASTING OF STRATEGIC FOOD COMMODITIES IN VARIOUS MARKETS IN MALANG REGENCY: IMPLEMENTATION OF THE ARMA-GARCH MODEL, Agricultural

PRICE FORECASTING OF STRATEGIC FOOD COMMODITIES IN VARIOUS MARKETS IN MALANG REGENCY: IMPLEMENTATION OF THE

ARMA-GARCH MODEL

Sujarwo, Fitrotul Laili

Department of Agricultural Socio-Economics, Brawijaya University, Indonesia

*corresponding author: [email protected]

Abstract Fluctuations in food prices can trigger vulnerability, disrupting people's access to food. This study aims to predict the prices of strategic food commodities: rice, corn, shallots, garlic, cayenne pepper, large chilies, chicken meat, chicken eggs, beef, cooking oil, and granulated sugar at the wholesaler level in the district. Poor. The research method used is the ARMA-GARCH forecasting method. Price forecasting carried out on all strategic food commodities in Malang Regency shows a fluctuating pattern with a tendency for price increases, with an average change increasing gradually in each period. The causality relationship in various markets about price changes in strategic food commodities in Malang Regency shows a unidirectional and two-way causality pattern.

Keywords: Price, Forecasting, Volatility, Causality

http://dx.doi.org/10.21776/ub.agrise.2023.023.2.9 Received 15 December 2022 Accepted 20 March 2023 Available online 30 April 2023

INTRODUCTION

Domestic food production, which contributes to world food reserves, also influences food price fluctuations if there is a delay in fulfilling food.

Fluctuations in food prices impact the unstable condition of the supply quantity seen from the production side with demand in the community.

The state of food prices that fluctuate continuously can result in price volatility (Bathla, 2012) which worries producers or farmers, and consumers (Nugrahapsari and Arsanti, 2019). The volatility of food prices (food volatiles) influences the formation of inflation and deflation rates in Indonesia. Food groups indicated that volatile prices contributed to secondary inflation after core inflation. Several commodities that fall into the volatile food category and contribute to inflation include rice, beef, purebred chicken eggs, bird's eye

chilies, red chilies, and shallots (Bank Indonesia, 2021).

On a micro level, high prices have a negative impact on households in making staple food consumption decisions (Marvasti & Lamberte, 2016). Volatility in food prices will have a long- term effect on producer income and disrupt commodity trading activities. It will make long- term production planning difficult due to the uncertainty that occurs due to prices that are difficult to predict.

Efforts to fulfill public consumption to achieve food security are inseparable from population growth. The results of the Indonesian population census show that in the last ten years, the growth rate of Indonesia's population has reached 1.25% or 3.26 million people per year (BPS, 2021). This population increase has an impact on increasing food needs (Tilman et al., 2011). Malang Regency is the second largest city in East Java, with a

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population of 861,414 people. However, it has only 865 hectares of agricultural land remaining.

Limited agricultural land in Malang Regency results in an inability to meet their own food needs, so they depend on other cities. This condition causes price fluctuations, especially in food commodities (Rizaldy, 2017).

Therefore, a method is needed to predict food prices that are included in strategic commodities so that they can be used as supporting references in decision-making, especially regarding price stabilization. Therefore, this study aims to predict the prices of strategic food commodities: corn, shallots, garlic, bird's eye chilies, large chilies, chicken, and beef in various markets in the Malang Regency.

Knowing the price forecasting of strategic commodities consisting of rice, corn, shallots, garlic, bird's eye chilies, large chilies, chicken meat, chicken eggs, beef, cooking oil, and granulated sugar is the basis for creating price stability for the government, thereby providing guarantees price certainty for consumers and producers, as well as minimizing the occurrence of strategic commodity price volatility has an impact on the welfare of both consumers and producers.

RESEARCH METHODS

This study uses data with research objects, namely monthly data on the prices of shelled corn, shallots, garlic, cayenne pepper, large chilies, and beef in various markets in Malang Regency for the period January 2013-December 2019. The data is sourced from the Information Center for Strategic Food Prices and the Central Bureau of Statistics.

The data analysis procedure includes the following stages:

1. Data Preparation

2. Stationarity Test or Unit Root Test, using Augmented Dickey-Fuller (ADF) at the same degree (level or different) until stationary data is obtained, namely data whose variance is not too large and has a tendency to approach the average value (Enders & Walter., 2004).

……… (1)

− If the statistical ADF <critical ADF, reject H0, meaning the data is stationary.

− If statistical ADF ≥ critical ADF, accept H0, meaning the data is not stationary.

The Autoregressive Integrated Moving Average (ARIMA) tests the model by looking at the largest coefficient of determination (R-squared) and using the smallest Akaike Information Criterion (AIC) and Schwartz Criterion (SC) criteria (Juanda and Junaidi, 2012).

……….. (2)

3. ARCH/GARCH Model Test, where this volatility (fluctuation) is reflected in the residual variance that does not meet the assumption of homoscedasticity or constant residual variance over time (Firdaus, 2011).

Bollerslev (1968) developed ARCH to become GARCH which can be interpreted as a time series data modeling technique that uses past variances, and the alleged past variances are used to forecast future variances.

…….. (3) Equation 3 is called the GARCH model (p,q), = variance error at t period; = previous period squared error; = variance error in the previous period; , , = estimation parameters.

4. ARCH-LM Heteroscedasticity Test, determined by looking at the F probability value and the Chi-square probability value, is significant with a significant level of 5% (Juanda and Junaidi, 2012). If the F probability value and the Chi- square probability value are less than the 5%

significance level or 0.05, then the model has the ARCH Rosadi (2012). In that case, the analysis will be continued using ARCH- GARCH analysis. If the model does not have an ARCH effect, then the analysis is carried out only up to the ARIMA model analysis.

5. Selection of the best model, considering the significance of the estimated parameter with a significance level of 5%, the most significant coefficient of determination (R-squared), and the smallest AIC and SC criteria (Nachrowi and Usman, 2006).

ARCH:

…. (4) GARCH:

……… (5)

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6. Simulation of strategic food commodity price forecasting for the next six years based on selecting the best ARIMA, ARCH/GARCH models. The measurement of price forecasting results is based on looking at the MAD (Mean Absolute Deviation), MSE (Mean Squared Error), Mean Percentage Error (MPE), and Mean Percentage Absolute Error (MAPE) values.

RESULTS AND DISCUSSION 1. Garlic

The equation for the volatility of garlic prices in Malang Regency is shown in detail in Table 1.

Based on this equation, it can be seen that the volatility of garlic prices in all markets through the

coefficients of AR and MA.

Table 1. Garlic Price Volatility Equation in Malang Regency

Market Equation Volatility (α + β)

Kepanjen

Yt = μ + Σ^pi=0 φiYt–i + Σ^qj=0 θjεt–j + εt (0.773968) (-0.590729)

(0.0094) (0.0304)

0.183239

Karang Ploso

Yt = μ + Σ^pi=0 φiYt–i + Σ^qj=0 θjεt–j + εt (-0.855409) (0.929071)

(0.0000) (0.0000)

0.073662

Lawang

Yt = μ + Σ^pi=0 φiYt–i + Σ^qj=0 θjεt–j + εt (-0.164692) (-1.000000)

(0.2438) (0.9987)

-1.164692

Singosari

Yt = μ + Σ^qj=0 θjεt – j + εt

(-0.999999) (0,9989)

-0.999999

Turen

(-0.175939) (0.2301)

-0.175939 From the ARIMA equation, the price of garlic

in all markets in this study is included in the low volatility category. This categorization is indicated by the total AR and MA coefficient values of garlic prices at Kepanjen, Karang Ploso, and Lawang, which are less than 1. The probability of AR and MA coefficients for garlic prices at Kepanjen and Karang Ploso shows a value less than a significance level of 5 %. This means that the volatility of garlic prices in both markets is influenced by the previous price and the residual value of the previous price at

the 95% confidence level. Meanwhile, the price volatility of garlic in the Singosari and Turen is not affected by the residual value of the previous price.

It is confirmed by the probability value of the MA coefficient below the 5% significance level.

Among the five markets in this study, the price of garlic in the Kepanjen has a higher volatility value than other markets. This condition illustrates that garlic traders' loss risk in the Kepanjen is higher than in other markets.

Table 2. Price Forecast of Garlic (per Kg) in Malang Regency

Tahun Bulan Ramalan Harga

Kepanjen Karang Ploso Lawang Singosari Turen

2020

1 24.038 27.032 25.784 26.162 25.361

2 23.676 26.825 25.660 26.326 25.820

3 23.395 27.599 25.763 26.491 26.125

4 23.178 27.524 25.926 26.659 26.431

5 23.010 28.184 26.047 26.829 26.736

6 22.880 28.207 26.153 27.001 27.041

7 22.779 28.783 26.261 27.174 27.346

8 22.701 28.879 26.366 27.350 27.652

9 22.641 29.389 26.467 27.528 27.957

10 22.594 29.542 26.563 27.707 28.262

11 22.558 30.005 26.655 27.889 28.568

12 22.530 30.198 26.743 28.073 28.873

2021 1 22.508 30.627 26.827 28.258 29.178

2 22.491 30.850 26.906 28.446 29.484

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3 22.478 31.253 26.982 28.635 29.789

4 22.468 31.499 27.053 28.827 30.094

5 22.461 31.882 27.120 29.020 30.399

6 22.455 32.145 27.183 29.216 30.705

7 22.450 32.513 27.242 29.413 31.010

8 22.446 32.788 27.297 29.613 31.315

9 22.444 33.146 27.348 29.814 31.621

10 22.441 33.431 27.394 30.018 31.926

11 22.440 33.780 27.436 30.223 32.231

12 22.438 34.072 27.474 30.430 32.537

2022

1 22.437 34.415 27.508 30.640 32.842

2 22.437 34.713 27.538 30.851 33.147

3 22.436 35.051 27.564 31.064 33.452

4 22.436 35.353 27.585 31.280 33.758

5 22.435 35.687 27.603 31.497 34.063

6 22.435 35.992 27.616 31.716 34.368

7 22.435 36.324 27.625 31.937 34.674

8 22.435 36.632 27.630 32.160 34.979

9 22.434 36.961 27.631 32.386 35.284

10 22.434 37.271 27.628 32.613 35.589

11 22.434 37.598 27.620 32.842 35.895

12 22.434 37.909 27.608 33.073 36.200

2023

1 22.434 38.235 27.592 33.306 36.505

2 22.434 38.548 27.572 33.541 36.811

3 22.434 38.872 27.548 33.778 37.116

4 22.434 39.186 27.520 34.017 37.421

5 22.434 39.510 27.488 34.258 37.727

6 22.434 39.825 27.451 34.501 38.032

7 22.434 40.148 27.410 34.746 38.337

8 22.434 40.463 27.365 34.993 38.642

9 22.434 40.784 27.316 35.242 38.948

10 22.434 41.101 27.263 35.493 39.253

11 22.434 41.422 27.206 35.746 39.558

12 22.434 41.740 27.144 36.001 39.864

2024

1 22.434 42.060 27.079 36.258 40.169

2 22.434 42.378 27.009 36.516 40.474

3 22.434 42.698 26.935 36.777 40.780

4 22.434 43.016 26.857 37.040 41.085

5 22.434 43.336 26.774 37.305 41.390

6 22.434 43.654 26.688 37.571 41.695

7 22.434 43.974 26.597 37.840 42.001

8 22.434 44.292 26.503 38.111 42.306

9 22.434 44.612 26.404 38.384 42.611

10 22.434 44.930 26.301 38.658 42.917

11 22.434 45.250 26.194 38.935 43.222

12 22.434 45.568 26.082 39.214 43.527

2025

1 22.434 45.888 25.967 39.494 43.833

2 22.434 46.206 25.847 39.777 44.138

3 22.434 46.526 25.724 40.061 44.443

4 22.434 46.845 25.596 40.348 44.748

5 22.434 47.164 25.464 40.636 45.054

6 22.434 47.483 25.327 40.927 45.359

7 22.434 47.802 25.187 41.219 45.664

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8 22.434 48.121 25.043 41.514 45.970

9 22.434 48.440 24.894 41.810 46.275

10 22.434 48.759 24.741 42.109 46.580

11 22.434 49.078 24.584 42.409 46.886

12 22.434 49.397 24.423 42.711 47.191

Forecasts for the price of garlic at the Kepanjen tend to experience stable movements, with a maximum price of IDR 24,038 in January 2020 and a minimum price of IDR 22,434 starting in September 2022 until December 2025. At the Karang Ploso Market, garlic prices move with an increasing trend. The maximum price is predicted to reach IDR 49,397 in December 2025, while the minimum price will be IDR 26,825 in February 2020. Similar conditions occur for garlic prices at Singosari, where the maximum price reaches IDR 42,711 occurring during December 2025. Trends Garlic prices an increasing trend also occurred in the Turen. The maximum price is expected to occur in December 2025 of IDR 42,711. The price forecast for garlic at Lawang will gradually decrease from January 2020 to December 2025, with a decrease of up to IDR 24,423.

The causal between garlic markets in Malang Regency only happens in one direction. The Kepanjen market has the same causality as the Lawang and Singosari. This shows that a change in the price of garlic in the Lawang and Singosari will cause a change in the price of garlic in the Kepanjen market. In other words, the Kepanjen is the price taker, while the Lawang and Singosari are the price makers.

2. Red Chili

The equation for the volatility of red chili prices in Malang Regency is shown in detail in Table 4. Based on this equation, it can be seen that the volatility values of red chili prices in all markets are through the coefficients of AR and MA.

Table 3. Granger Causality Test on Garlic Commodities

Kepanjen Karangploso Lawang Singosari Turen

Kepanjen 0.06793

(0.9344)

0.28790 (0.7506)

0.28790

(0.7506) 0.97558 (0.3816)

Karangploso 1.98902

(0.1438)

1.69202 (0.1909)

0.21891 (0.8039)

0.10506 (0.9004)

Lawang

5.17556 (0.0078)**

Lawang → Kepanjen

1.06406

(0.3501) 1.51058

(0.2273)

2.51987 (0.0871)

Singosari

6.54263 (0.0024)**

Singosari → Kepanjen

0.21891

(0.8039) 2.61470 (0.0797)

1.36785 (0.2608)

Turen 0.64794

(0.5260)

0.24064

(0.7867) 1.70652 (0.1883)

1.25161 (0.2919)

Table 4. Red Chili Price Volatility Equation in Malang Regency

Market Equation Volatility (α + β)

Kepanjen

σ²POt = α0 + α1ε²POt-1 + εt

(0.171429) (0.4664)

0.171429

Karang Ploso

(-0.332937) (0.0004)

-0.332937

Lawang

(0.2201) (0.9989)

-1.16457

Singosari σ²POt = α0 + β1σ²POt-1 + εt -0.092828

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(-0.092828) (0.9806) Turen

(0.0385) (0.9986)

-1.19319

Table 5. Price Forecast of Garlic (per Kg) in Malang Regency

Year Month Price Forecast

2020

1 25.063 28.113 24.690 22.944 23.368

2 23.705 27.119 24.599 21.751 23.570

3 23.837 27.442 24.515 21.837 23.337

4 23.915 27.765 24.557 21.879 23.317

5 23.960 28.088 24.591 21.900 23.375

6 23.986 28.411 24.597 21.910 23.384

7 24.002 28.733 24.598 21.915 23.371

8 24.011 29.056 24.595 21.917 23.360

9 24.016 29.379 24.587 21.919 23.346

10 24.019 29.702 24.572 21.919 23.325

11 24.021 30.025 24.551 21.919 23.297

12 24.022 30.348 24.524 21.920 23.263

2021

1 24.023 30.671 24.490 21.920 23.224

2 24.023 30.993 24.451 21.920 23.178

3 24.024 31.316 24.406 21.920 23.127

4 24.024 31.639 24.354 21.920 23.070

5 24.024 31.962 24.296 21.920 23.007

6 24.024 32.285 24.233 21.920 22.937

7 24.024 32.608 24.163 21.920 22.862

8 24.024 32.930 24.087 21.920 22.781

9 24.024 33.253 24.005 21.920 22.694

10 24.024 33.576 23.916 21.920 22.601

11 24.024 33.899 23.822 21.920 22.502

12 24.024 34.222 23.722 21.920 22.397

2022

1 24.024 34.545 23.615 21.920 22.286

2 24.024 34.867 23.502 21.920 22.169

3 24.024 35.190 23.384 21.920 22.046

4 24.024 35.513 23.259 21.920 21.917

5 24.024 35.836 23.128 21.920 21.782

6 24.024 36.159 22.991 21.920 21.642

7 24.024 36.482 22.848 21.920 21.495

8 24.024 36.805 22.698 21.920 21.342

9 24.024 37.127 22.543 21.920 21.184

10 24.024 37.450 22.382 21.920 21.019

11 24.024 37.773 22.214 21.920 20.848

12 24.024 38.096 22.040 21.920 20.672

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2023

1 24.024 38.419 21.861 21.920 20.489

2 24.024 38.742 21.675 21.920 20.301

3 24.024 39.064 21.483 21.920 20.107

4 24.024 39.387 21.285 21.920 19.906

5 24.024 39.710 21.080 21.920 19.700

6 24.024 40.033 20.870 21.920 19.488

7 24.024 40.356 20.654 21.920 19.269

8 24.024 40.679 20.431 21.920 19.045

9 24.024 41.001 20.203 21.920 18.815

10 24.024 41.324 19.968 21.920 18.579

11 24.024 41.647 19.727 21.920 18.337

12 24.024 41.970 19.480 21.920 18.089

2024

1 24.024 42.293 19.227 21.920 17.835

2 24.024 42.616 18.968 21.920 17.575

3 24.024 42.939 18.703 21.920 17.309

4 24.024 43.261 18.431 21.920 17.037

5 24.024 43.584 18.154 21.920 16.759

6 24.024 43.907 17.870 21.920 16.475

7 24.024 44.230 17.581 21.920 16.186

8 24.024 44.553 17.285 21.920 15.890

9 24.024 44.876 16.983 21.920 15.588

10 24.024 45.198 16.675 21.920 15.281

11 24.024 45.521 16.361 21.920 14.967

12 24.024 45.844 16.041 21.920 14.647

2025

1 24.024 46.167 15.714 21.920 14.322

2 24.024 46.490 15.382 21.920 13.990

3 24.024 46.813 15.043 21.920 13.653

4 24.024 47.135 14.699 21.920 13.310

5 24.024 47.458 14.348 21.920 12.960

6 24.024 47.781 13.991 21.920 12.605

7 24.024 48.104 13.628 21.920 12.244

8 24.024 48.427 13.259 21.920 11.876

9 24.024 48.750 12.884 21.920 11.503

10 24.024 49.073 12.503 21.920 11.124

11 24.024 49.395 12.116 21.920 10.739

12 24.024 49.718 11.722 21.920 10.348

The price of red chili in all markets in this study is included in the low volatility category.

This categorization is indicated by the total AR and MA coefficient values of red chili prices at Karang Ploso, Lawang, and Turen, which are less than 1.

Based on the probability value which is less than

5% significant probability value of red chili prices at Karang Ploso is influenced by the residual value price of the previous period. Meanwhile, in the Turen, the volatility of red chili prices is influenced by the previous period’s price. Forecasts for red chili prices at the Kepanjen and Singosari will

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decrease in February 2020, which will then tend to be stable with a monthly price of IDR 24,024 until the last month of 2025. In the Lawang and Turen, red chili prices tended down from the maximum price of IDR 24,690 in January 2020 is predicted to be IDR 11,722 in December 2025. Meanwhile, in

the Turen, the maximum price of IDR 23,570 gradually drops to IDR 10,348. A different condition is shown by the Karang Ploso, where the price of red chilies gradually increases with the highest maximum price of IDR 49,718 expected to occur in December 2025.

Table 6. Granger Causality Test on Red Chili Commodities

Kepanjen 0.56309

(0.5718)

0.11942 (0.8876)

0.74783 (0.4536)

0.08628 (0.9174)

(0.9800)

0.16721 (0.8463)

0.79861 (0.8039)

1.45206 (0.2404)

Lawang 0.31147

(0.7333)

0.21707 (0.9476)

0.64744 (0.5262)

0.45189 (0.6381)

Singosari 2.91236

(0.0604)

0.05384 (0.8039)

0.29001 (0.7491)

0.24849 (0.7806)

Turen 0.98118

(0.3795)

2.09736 (0.1297)

0.08131 (0.9220)

0.74488 (0.4782) There is no causality between markets in

Malang Regency. This shows that any changes in the price of red chilies in one market will not affect changes in the prices of red chilies in other markets.

3. Cayenne pepper

The price of cayenne pepper in all markets in this study is included in the low volatility category.

This categorization is shown by the sum of the AR and MA coefficients of the price of cayenne pepper

at Kepanjen, Karang Ploso, Singosari, and Turen which is less than 1. The probability of the AR and MA coefficients for the price of cayenne pepper at Karang Ploso and Turen shows a value less than the level significance of 5%. That is, the price volatility of cayenne pepper in both markets is influenced by the previous price and the residual value of the previous price at the 95% confidence level.

Table 7. The Equation of Cayenne Pepper Price Volatility in Malang Regency

Market Equation Volatility (α + β)

Kepanjen

(0.171429) (0.4664)

0.171429

Karang Ploso

(-0.332937) (0.0004)

-0.332937

Lawang

(0.2201) (0.9989)

-1.16457

Singosari

σ²POt = α0 + β1σ²POt-1 + εt

(-0.092828) (0.9806)

-0.092828

Turen

(0.0385) (0.9986)

-1.19319 Among the five markets in this study, the price

of cayenne pepper at Karang Ploso has a higher volatility value than other markets. High volatility indicates that price volatility occurs over a longer

period of time (Ligot et al., 2021) which is called permanent volatility (Ahmed et al., 2012).

Therefore, future price fluctuations should be monitored.

Table 8. Forecast of Cayenne Pepper Prices (per Kg) in Malang Regency

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Year Month Price Forecast

Kepanjen Karang Ploso Lawang Singosari Turen

2020

1 35.388 30.051 30.188 35.730 35.424

2 36.641 27.945 30.540 36.860 36.340

3 36.829 26.256 30.900 37.194 36.854

4 37.017 24.900 31.260 37.388 37.296

5 37.206 23.813 31.620 37.538 37.730

6 37.394 22.941 31.980 37.680 38.161

7 37.582 22.242 32.340 37.819 38.592

8 37.770 21.681 32.700 37.958 39.024

9 37.958 21.231 33.060 38.097 39.455

10 38.146 20.870 33.419 38.236 39.886

11 38.334 20.581 33.779 38.375 40.317

12 38.522 20.348 34.139 38.513 40.748

2021

1 38.710 20.162 34.499 38.652 41.179

2 38.898 20.013 34.859 38.791 41.611

3 39.086 19.893 35.219 38.930 42.042

4 39.274 19.797 35.579 39.069 42.473

5 39.462 19.720 35.939 39.208 42.904

6 39.650 19.658 36.299 39.346 43.335

7 39.838 19.608 36.658 39.485 43.766

8 40.026 19.568 37.018 39.624 44.198

9 40.214 19.536 37.378 39.763 44.629

10 40.402 19.511 37.738 39.902 45.060

11 40.590 19.490 38.098 40.040 45.491

12 40.779 19.474 38.458 40.179 45.922

2022

1 40.967 19.461 38.818 40.318 46.354

2 41.155 19.450 39.178 40.457 46.785

3 41.343 19.441 39.538 40.596 47.216

4 41.531 19.435 39.898 40.735 47.647

5 41.719 19.429 40.257 40.873 48.078

6 41.907 19.425 40.617 41.012 48.509

7 42.095 19.421 40.977 41.151 48.941

8 42.283 19.418 41.337 41.290 49.372

9 42.471 19.416 41.697 41.429 49.803

10 42.659 19.414 42.057 41.568 50.234

11 42.847 19.413 42.417 41.706 50.665

12 43.035 19.412 42.777 41.845 51.096

2023

1 43.223 19.411 43.137 41.984 51.528

2 43.411 19.410 43.496 42.123 51.959

3 43.599 19.409 43.856 42.262 52.390

4 43.787 19.409 44.216 42.400 52.821

5 43.975 19.409 44.576 42.539 53.252

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Year Month Price Forecast

Kepanjen Karang Ploso Lawang Singosari Turen

6 44.163 19.408 44.936 42.678 53.684

7 44.352 19.408 45.296 42.817 54.115

8 44.540 19.408 45.656 42.956 54.546

9 44.728 19.408 46.016 43.095 54.977

10 44.916 19.408 46.376 43.233 55.408

11 45.104 19.407 46.735 43.372 55.839

12 45.292 19.407 47.095 43.511 56.271

2024

1 45.480 19.407 47.455 43.650 56.702

2 45.668 19.407 47.815 43.789 57.133

3 45.856 19.407 48.175 43.927 57.564

4 46.044 19.407 48.535 44.066 57.995

5 46.232 19.407 48.895 44.205 58.426

6 46.420 19.407 49.255 44.344 58.858

7 46.608 19.407 49.615 44.483 59.289

8 46.796 19.407 49.974 44.622 59.720

9 46.984 19.407 50.334 44.760 60.151

10 47.172 19.407 50.694 44.899 60.582

11 47.360 19.407 51.054 45.038 61.014

12 47.548 19.407 51.414 45.177 61.445

2025

1 47.736 19.407 51.774 45.316 61.876

2 47.924 19.407 52.134 45.455 62.307

3 48.113 19.407 52.494 45.593 62.738

4 48.301 19.407 52.854 45.732 63.169

5 48.489 19.407 53.214 45.871 63.601

6 48.677 19.407 53.573 46.010 64.032

7 48.865 19.407 53.933 46.149 64.463

8 49.053 19.407 54.293 46.287 64.894

9 49.241 19.407 54.653 46.426 65.325

10 49.429 19.407 55.013 46.565 65.757

11 49.617 19.407 55.373 46.704 66.188

12 49.805 19.407 55.733 46.843 66.619

The price of cayenne pepper in the Kepanjen, Lawang, Singosari and Turen is predicted to experience growth with an increasing trend. At Kepanjen t, the highest increase in the price of cayenne pepper reached IDR 49,805 in December 2025, while at Lawang the highest price reached IDR 55,733, and IDR 46,843 at Singosari. The

highest price increase occurred in the Turen reaching IDR 66,619 which is predicted to occur in December 2025. Meanwhile, the price of cayenne pepper at the Karang Ploso is predicted to tend to be stable until the end of 2025 with an average price of IDR 30,051.

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Table 9. Granger Causality Test on Cayenne Pepper Commodities

Kepanjen 0.26916

(0.7647)

0.11942 (0.8876)

0.05631 (0.9453)

0.64066 (0.5297)

(0.3244)

0.36141 (0.6979)

0.98445 (0.3783)

2.16452 (0.3783)

Lawang

3.26774 (0.0434)**

Lawang → Kepanjen

6.11186 (0.0034)**

Lawang → Karangploso

2.91977 (0.0599)

8.55023 (0.0004)**

Lawang → Turen

Singosari 0.14545

(0.8649)

0.34623 (0.7084)

0.27689 (0.7589)

0.71035 (0.4947)

Turen 0.66733

(0.5160)

1.32996 (0.2705)

1.04305 (0.3573)

0.49383 (0.6122) The causality that occurs between cayenne

pepper markets in Malang Regency does not occur in two directions. Lawang has causality in the same direction as the Kepanjen, Karangploso, and Turen.

This shows that changes in the price of cayenne pepper at the Lawang will affect changes in the price of cayenne pepper at the Kepanjen, Karangploso and Turen. In other words, the Lawang is the price maker, while the Kepanjen, Karangploso, and Turen are the price takers.

4. Beef

the price of beef in all markets in this study is included in the low volatility category. This categorization is indicated by the sum of the AR and MA coefficient values of the beef price at Singosari Market, and the MA coefficient of the beef price at Karang Ploso and Turen which is less than 1. The low volatility of beef prices is also indicated by the ARCH coefficient of beef prices.

in Kepanjen and Lawang.

Table 10. Equation of Beef Price Volatility in Malang Regency

Kepanjen

(0.171429) (0.4664)

0.171429

Karang Ploso

(-0.332937) (0.0004)

-0.332937

Lawang

(0.2201) (0.9989)

-1.16457

Singosari

(-0.092828) (0.9806)

-0.092828

Turen

(0.0385) (0.9986)

-1.19319 The probability of the MA coefficient on the

price of beef at the Karang Ploso and Turen shows a value less than the 5% significance level. This means that the volatility of beef prices in both markets is affected by the residual value of the previous price at the 95% confidence level.

Meanwhile, the volatility of beef prices in the Singosari Market is influenced by the previous price and the residual value of the previous price.

Confirmed from the probability value of the AR

and MA coefficients less than 5% significance level. The volatility of beef prices at the Kepanjen has been confirmed to be affected by price variances in the previous period, as evidenced by the probability that the ARCH coefficient is less than 5%. Among the five markets in this study, the price of beef in Turen has the highest volatility value compared to other markets.

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Table 11. Beef Price Forecast (per Kg) in Malang Regency

2020

1 108.453 108.084 110.625 111.255 103.105

2 108.804 109.366 112.301 111.813 104.327

3 109.155 110.648 113.977 112.722 105.550

4 109.506 111.929 115.653 113.213 106.773

5 109.857 113.211 117.329 113.914 107.995

6 110.208 114.493 119.005 114.365 109.218

7 110.558 115.775 120.681 114.943 110.440

8 110.909 117.057 122.357 115.369 111.663

9 111.260 118.338 124.033 115.872 112.885

10 111.611 119.620 125.709 116.284 114.108

11 111.962 120.902 127.385 116.742 115.331

12 112.313 122.184 129.061 117.146 116.553

2021

1 112.664 123.465 130.737 117.577 117.776

2 113.015 124.747 132.414 117.975 118.998

3 113.365 126.029 134.090 118.390 120.221

4 113.716 127.311 135.766 118.786 121.443

5 114.067 128.593 137.442 119.191 122.666

6 114.418 129.874 139.118 119.585 123.889

7 114.769 131.156 140.794 119.984 125.111

8 115.120 132.438 142.470 120.376 126.334

9 115.471 133.720 144.146 120.772 127.556

10 115.821 135.002 145.822 121.164 128.779

11 116.172 136.283 147.498 121.558 130.002

12 116.523 137.565 149.174 121.949 131.224

2022

1 116.874 138.847 150.850 122.342 132.447

2 117.225 140.129 152.526 122.733 133.669

3 117.576 141.410 154.202 123.125 134.892

4 117.927 142.692 155.878 123.516 136.114

5 118.278 143.974 157.554 123.907 137.337

6 118.628 145.256 159.230 124.298 138.560

7 118.979 146.538 160.906 124.689 139.782

8 119.330 147.819 162.582 125.080 141.005

9 119.681 149.101 164.258 125.471 142.227

10 120.032 150.383 165.934 125.862 143.450

11 120.383 151.665 167.610 126.253 144.672

12 120.734 152.946 169.286 126.643 145.895

2023

1 121.085 154.228 170.962 127.034 147.118

2 121.435 155.510 172.638 127.425 148.340

3 121.786 156.792 174.314 127.816 149.563

4 122.137 158.074 175.990 128.207 150.785

5 122.488 159.355 177.666 128.597 152.008

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6 122.839 160.637 179.342 128.988 153.231

7 123.190 161.919 181.018 129.379 154.453

8 123.541 163.201 182.694 129.770 155.676

9 123.891 164.482 184.370 130.160 156.898

10 124.242 165.764 186.046 130.551 158.121

11 124.593 167.046 187.722 130.942 159.343

12 124.944 168.328 189.398 131.333 160.566

2024

1 125.295 169.610 191.074 131.723 161.789

2 125.646 170.891 192.750 132.114 163.011

3 125.997 172.173 194.426 132.505 164.234

4 126.348 173.455 196.103 132.895 165.456

5 126.698 174.737 197.779 133.286 166.679

6 127.049 176.019 199.455 133.677 167.901

7 127.400 177.300 201.131 134.068 169.124

8 127.751 178.582 202.807 134.458 170.347

9 128.102 179.864 204.483 134.849 171.569

10 128.453 181.146 206.159 135.240 172.792

11 128.804 182.427 207.835 135.631 173.402

12 129.154 183.709 209.511 136.021 174.624

2025

1 129.505 184.991 211.187 136.412 175.847

2 129.856 186.273 212.863 136.803 177.069

3 130.207 187.555 214.539 137.194 178.292

4 130.558 188.836 216.215 137.584 179.515

5 130.909 190.118 217.891 137.975 180.737

6 131.260 191.400 219.567 138.366 181.960

7 131.611 192.682 221.243 138.756 183.182

8 131.961 193.963 222.919 139.147 184.405

9 132.312 195.245 224.595 139.538 185.627

10 132.663 196.527 226.271 139.929 186.850

11 133.014 197.809 227.947 140.319 188.073

12 133.365 199.091 229.623 140.710 189.295

Beef prices in all markets in this study are predicted to gradually increase in price until the last month of 2025. The highest beef price is at Lawang, reaching IDR 229,623. Meanwhile, the

lowest beef price is predicted to occur at the Kepanjen of IDR 133,365. During 2020-2025, the lowest beef price was at the Turen of IDR 103,105 in January 2020.

Table 12. Granger Causality Test on Beef Commodities

Kepanjen 1.48521

(0.2329)

1.45854 (0.2389)

1.42321 (0.2472)

1.71732 (0.1864)

Karangploso 0.36935 (0.6924)

1.58069 (0.2124)

2.19167 (0.1187)

1.36393 (0.2618)

Lawang 0.35979 1.04190 1.93726 0.31607

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(0.6990) (0.3577)** (0.1511) (0.7300)

Singosari 1.84494

(0.1650)

1.98780 (0.1440)

2.08440 (0.1313)

2.16894 (0.1212)

Turen 0.27711

(0.7587)

1.03803 (0.3591)

1.44644 (0.2417)

2.68047 (0.0749) Causality does not occur between markets in

Malang Regency. This shows that any changes in beef commodity prices in one market will not affect changes in beef prices in other markets.

5. Shelled Corn

The price of shelled corn in all markets is classified as low price volatility. This classification

is based on the GARCH coefficient for the price of shelled corn in Kepanjen, the ARCH coefficient for the price of shelled corn in Lawang, the ARCH coefficient for the price of shelled corn in Karang Ploso and Turen, and the MA coefficient for the price of shelled corn in the Singosari.

Table 10. The Equation of Shelled Corn Price Volatility in Malang Regency

Kepanjen

(0.171429) (0.4664)

0.171429

Karang Ploso

(-0.332937) (0.0004)

-0.332937

Lawang

(0.2201) (0.9989)

-1.16457

Singosari

(-0.092828) (0.9806)

-0.092828

Turen

(0.0385) (0.9986)

-1.19319 Judging from the probability value, the price

volatility of shell corn in Karang Ploso is affected by the residual value of the previous period's price,

while in the Singosari it is affected by the price of the previous period.

Table 11. Forecast of Shelled Corn Prices (per Kg) in Malang Regency

2020

1 6.821 7.455 6.632 6.583 6.669

2 6.642 7.406 6.735 6.629 6.695

3 6.688 7.511 6.839 6.662 6.751

4 6.632 7.635 6.942 6.694 6.844

5 6.622 7.719 7.046 6.727 6.929

6 6.592 7.797 7.149 6.759 7.004

7 6.571 7.887 7.253 6.792 7.082

8 6.546 7.977 7.356 6.824 7.162

9 6.522 8.065 7.460 6.857 7.241

10 6.498 8.152 7.564 6.889 7.320

11 6.474 8.240 7.667 6.922 7.399

12 6.450 8.329 7.771 6.954 7.478

2021

1 6.426 8.417 7.874 6.987 7.557

2 6.402 8.505 7.978 7.019 7.636

3 6.378 8.593 8.081 7.052 7.715

4 6.354 8.681 8.185 7.084 7.794

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5 6.330 8.769 8.288 7.117 7.873

6 6.306 8.857 8.392 7.149 7.952

7 6.282 8.945 8.495 7.182 8.032

8 6.258 9.033 8.599 7.214 8.111

9 6.234 9.121 8.703 7.247 8.190

10 6.210 9.209 8.806 7.279 8.269

11 6.186 9.297 8.910 7.312 8.348

12 6.162 9.385 9.013 7.344 8.427

2022

1 6.138 9.385 9.117 7.344 8.506

2 6.114 9.496 9.220 7.393 8.585

3 6.090 9.584 9.324 7.425 8.664

4 6.066 9.666 9.427 7.458 8.743

5 6.042 9.754 9.531 7.490 8.822

6 6.018 9.844 9.634 7.523 8.901

7 5.994 9.932 9.738 7.555 8.980

8 5.970 10.020 9.842 7.588 9.059

9 5.946 10.108 9.945 7.620 9.138

10 5.922 10.196 10.049 7.653 9.217

11 5.898 10.284 10.152 7.685 9.297

12 5.874 10.372 10.256 7.718 9.376

2023

1 5.850 10.460 10.359 7.750 9.455

2 5.826 10.548 10.463 7.783 9.534

3 5.802 10.636 10.566 7.815 9.613

4 5.778 10.724 10.670 7.848 9.692

5 5.754 10.812 10.773 7.880 9.771

6 5.730 10.900 10.877 7.913 9.850

7 5.706 10.988 10.981 7.945 9.929

8 5.682 11.076 11.003 7.978 10.008

9 5.658 11.164 11.107 8.010 10.087

10 5.634 11.252 11.210 8.043 10.166

11 5.610 11.340 11.314 8.075 10.245

12 5.586 11.428 11.417 8.108 10.324

2024

1 5.562 11.516 11.521 8.140 10.403

2 5.538 11.605 11.625 8.173 10.482

3 5.514 11.693 11.728 8.205 10.561

4 5.490 11.781 11.832 8.238 10.641

5 5.466 11.869 11.935 8.270 10.720

6 5.442 11.957 12.039 8.302 10.799

7 5.418 12.045 12.142 8.335 10.878

8 5.394 12.133 12.246 8.367 10.957

9 5.370 12.221 12.349 8.400 11.036

10 5.346 12.309 12.453 8.432 11.115

11 5.322 12.397 12.556 8.465 11.194

12 5.298 12.485 12.660 8.497 11.273

2025

1 5.275 12.573 12.764 8.530 11.352

2 5.251 12.661 12.867 8.562 11.431

3 5.227 12.749 12.971 8.595 11.510

4 5.203 12.837 13.074 8.627 11.589

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5 5.179 12.925 13.178 8.660 11.668

6 5.155 13.013 13.281 8.692 11.747

7 5.131 13.101 13.385 8.725 11.826

8 5.107 13.189 13.488 8.757 11.906

9 5.083 13.277 13.592 8.790 11.985

10 5.059 13.366 13.695 8.822 12.064

11 5.035 13.454 13.799 8.855 12.143

12 5.011 13.542 13.903 8.887 12.222

It is predicted that the price of shelled corn in Karang Ploso, Lawang, Singosari and Turen will gradually increase until the end of 2025. The highest price of shelled corn is predicted to reach IDR 13,542 at Karang Ploso Market, IDR 13,903 at

Lawang Market, IDR 8,887 at Singosari, and IDR 12,222 at the Turen. Different conditions are shown by the price movement of shelled corn at the Kepanjen with a downward trend to IDR 5,011 in

December 2025.

Table 12. Granger Causality Test on Peeled Corn Commodities

Kepanjen 2.78109

(0.0682)

6.21212 (0.0032)**

Kepanjen → Lawang

2.65503 (0.0767)

3.85684 (0.0253)**

Kepanjen → Turen Karangploso 1.59065

(0.2104)

2.98283 (0.0565)

0.57013 (0.5678)

1.52002 (0.2252)

Lawang

4.82328 (0.0106)**

Lawang → Kepanjen

0.27474 (0.7605)

1.35564 (0.2639)

1.17378 (0.3147)

Singosari 2.20828 (0.1168)

1.30333 (0.2776)

5.97467 (0.0039)**

Singosari → Lawang

2.78172 (0.0682)

Turen 1.29809

(0.2790)

1.98246 (0.1447)

2.09470 (0.1301)

0.30548 (0.7377) There is a two-way causality in the Lawang

and Kepanjen for pipil corn, indicating that price changes in both markets will affect one another.

Meanwhile, the Kepanjen and Turen show a unidirectional causality relationship, where if there is a change in the price of shelled corn in the Kepanjen market it will affect changes in the price of shelled corn in the Turen. Unidirectional causality also occurs in the Singosari and Lawang for shelled corn. It can be concluded that for shelled corn in Malang Regency, the Singosari is the price maker, while the Turen is the price taker.

CONCLUSION

The price volatility of all commodities in various markets, including the Kepanjen, Karangploso, Singosari, Lawang, and Turen markets, it shows that volatility tends to increase in the future, with the volatility category at low

volatility. Price forecasting carried out on all strategic food commodities in Malang Regency shows a fluctuating pattern with a tendency for price increases, with an average change that increases gradually in each time period.

The causality relationship in various markets in relation to price changes in strategic food commodities in Malang Regency shows a unidirectional and two-way causality pattern. The Kepanjen market has the same causality as the Lawang and Singosari markets. This shows that a change in the price of garlic in the Lawang and Singosari markets will cause a change in the price of garlic in the Kepanjen market. In other words, the Kepanjen market is the price taker, while the Lawang and Singosari markets are price makers.

Lawang Market has causality in the same direction as the Kepanjen, Karangploso, and Turen markets. This shows that changes in the price of cayenne pepper at the Lawang market will affect changes in the price of cayenne pepper at the

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Kepanjen, Karangploso and Turen markets. In other words, the Lawang market is the price maker, while the Kepanjen, Karangploso, and Turen markets are the price takers. Meanwhile, the Lawang and Kepanjen markets for pipil corn indicate that price changes in both markets will affect one another. Meanwhile, the Kepanjen and Turen markets show a unidirectional causality relationship, where if there is a change in the price of shelled corn in the Kepanjen market it will affect changes in the price of shelled corn in the Turen market. Unidirectional causality also occurs in the Singosari and Lawang markets for shelled corn. It can be concluded that for shelled corn in Malang Regency, the Singosari market is the price maker, while the Turen market is the price taker.

Data limitations are the main obstacle for the smooth analysis in this study. Therefore, it is suggested that Indonesia's information management agencies, including the Central Statistics Agency (BPS) and the Industry and Trade Service (Disperindag), as well as the Agriculture Service, are in a better position in collecting data, bearing in mind that all food commodities in this study are strategic commodities which plays an important role in the economy.

The existence of a fluctuating pattern of strategic food commodity price movements with an overall increasing trend which is known through the results of research in Malang Regency is a form of an imbalance between the amount of production and demand for the amount of production and demand. Therefore, to overcome problems in the upstream sector of strategic food commodities, the government through the Ministry of Agriculture, Agriculture Service and Food Security can make technical efforts to ensure sustainable production.

This effort is intended as an anticipatory measure when price fluctuations occur due to production experiencing a deficit or surplus. These efforts can be in the form of providing seeds that are adaptive to weather changes, as well as providing post- harvest handling technologies such as efficient, appropriate and affordable storage so that farmers can easily implement them. Besides being able to control availability and keep products fresh and durable, this effort can also help farmers to manage their sales in order to obtain favorable prices.

ACKNOWLEDGEMENTS

This research is supported by the Faculty of Agriculture, University of Brawijaya

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