Price Volatility of Horticulture Price Using ARCH-GARCH Model

Abstract

Introduction: Price volatility in horticultural markets poses significant challenges for producers, traders, and policymakers, especially for essential commodities such as shallots. These markets often exhibit dynamic price movements influenced by seasonality, perishability, weather conditions, and behavioral factors.

Objectives: This study aims to analyze the volatility characteristics of shallot prices using advanced time series models, particularly ARCH-GARCH, to better understand their fluctuation patterns and persistence.

Methods: Weekly time series data from 2019 to 2023 were utilized to capture price dynamics. The analysis involved descriptive statistics, stationarity tests, and heteroskedasticity diagnostics. The ARCH-LM test was conducted to detect ARCH effects, followed by model estimation using ARMA and GARCH approaches, with particular attention to the GARCH (1,1) specification.

Results: Descriptive statistics indicated non-normal price distributions with high kurtosis, confirming volatility clustering. Unit root tests showed that the series were integrated of order one. The ARCH-LM test confirmed the presence of ARCH effects, validating the use of ARCH-GARCH modeling. GARCH (1,1) models effectively captured the volatility persistence and autoregressive structure in price movements.

Conclusions: ARCH-GARCH models, particularly the GARCH (1,1) speenhancese in modeling the volatility of shallot prices. These findings offer valuable implications for price forecasting, risk management, and policy formulation in the agricultural sector.