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Using Econometric Models to Manage the Price Risk of Cocoa Beans: A Case from India

Kepulaje Abhaya Kumar (), Cristi Spulbar (), Prakash Pinto (), Iqbal Thonse Hawaldar (), Ramona Birau () and Jyeshtaraja Joisa ()
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Kepulaje Abhaya Kumar: Department of Business Administration, Mangalore Institute of Technology & Engineering, Mangalore 574225, India
Cristi Spulbar: Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania
Prakash Pinto: Department of Business Administration, St. Joseph Engineering College, Mangalore 575028, India
Iqbal Thonse Hawaldar: Department of Accounting & Finance, College of Business Administration, Kingdom University, Sanad 40434, Bahrain
Ramona Birau: Doctoral School of Economic Sciences, University of Craiova, 200585 Craiova, Romania
Jyeshtaraja Joisa: Department of Business Administration, Mangalore Institute of Technology & Engineering, Mangalore 574225, India

Risks, 2022, vol. 10, issue 6, 1-18

Abstract: This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R 2 are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model’s goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes.

Keywords: ARIMA models; VAR; price risk; risk management; volatility; financial market; cocoa beans; price movements (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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