Macroeconomic Determinants of the Coffee Price Volatility in Ethiopia. Application of the Garch-Midas Model

Master's Thesis , 2020 , 94 Pages , Grade: 24

Economics - Statistics and Methods

Application of GARCH type model is a key for modeling and forecasting volatility for high frequency data such as daily commodity price. Following the same framework, the objective of the present study is to apply the multiplicative GARCH-MIDAS model for daily exported coffee price as proxy of daily total coffee price of Ethiopia over the period of 1-1-2008 to 7-17-2018 with the purpose of fitting and forecasting coffee price returns volatility.

The GARCH-MIDAS model decomposes the conditional variance as short-term component of GARCH (1,1) process, and long-term component, with monthly frequencies of macroeconomic variables. In this study exchange rate (nominal exchange rate), inflation rate (general inflation), interest rate (lending interest rate), fuel oil price (price of imported petroleum and petroleum production), total consumption and money supply (broad money) macroeconomic variables were employed through MIDAS specification using beta-weighting scheme to analyze impact of the variables on the long-term volatility component. For fitted ARMA (1,1) of coffee price return ARCH effect test on the residual from the mean model revealed the existence of time varying conditional variance for the selected mean model. A conditional variance model GARCH (1,1) was selected and used to model the conditional variance of coffee price return with Quasi Maximum Likelihood along with Bayesian estimation methods and both estimation procedures indicated the persistence of conditional variance observed even for small sample under Bayesian estimation framework. Asymmetry test show the insignificance of the asymmetric term, while Lundbergh and Terasvirta Lagrange Multiplier and the Li-Mak portmanteau test for the residual of GARCH model show the existence of time varying unconditional variance and made call for GARCH-MIDAS model. From the result of estimated GARCH-MIDAS model exchange rate and inflation rate were found to be the best drivers of coffee price volatility in Ethiopia and used for in and out of sample forecast.

Finally, the Mean Absolute Error Root Mean Square Error and Diebold Mariano test were used for evaluating and comparing the forecasting ability of GARCH-MIDAS component model against standard GARCH (1,1) model which indicated that, including exchange rate and inflation rate make efficient forecasting of coffee price volatility in Ethiopia.

Inhaltsverzeichnis (Table of Contents)

• ABSTRACT
• 1. INTRODUCTION
• 1.1. Background
• 1.2. Statement of the Problem
• 1.3. Objectives of the Study
• 1.4. Significance of the Study
• 1.5. Scope and Limitations of the Study
• 1.6. Organization of the Thesis
• 2. LITERATURE REVIEW
• 2.1. Theoretical Review
• 2.1.1. Volatility
• 2.1.2. Price Volatility and Coffee Consumption in Ethiopia
• 2.1.3. Price Volatility and Coffee Export Status in Ethiopia
• 2.1.4. Economic Importance of Price Volatility
• 2.2. Related Theories of Price Volatility
• 2.2.1. Arbitrage Pricing Theory
• 2.2.2. Future Cash Flow Discounted Model Theory
• 2.3. Theoretical Framework of Volatility Models
• 2.3.1. Volatility Measurement and Theoretical Review of Volatility Models
• 2.3.2. Model of Mixed Data Sampling (MIDAS)
• 2.3.3. GARCH-MIDAS Component Model
• 2.4. Empirical Literature Review
• 3. RESEARCH METHODOLOGY
• 3.1. Data Source and Variables
• 3.2. Measurement and Definitions of Variables
• 3.3. Financial Time series and their Characteristics
• 3.4. Test for Stationarity
• 3.4.1. The Augmented Dickey Fuller (ADF) Test
• 3.4.2. The Phillips and Perron (PP) Test
• 3.5. Model Specification
• 3.5.1. Autoregressive Moving Average (ARMA)
• 3.5.2. Autoregressive Integrated Moving Average (ARIMA)
• 3.6. Conditional Volatility Model
• 3.6.1. ARCH model
• 3.6.2. GARCH model
• 3.7. Residual Diagnostics for Building GARCH Type Model
• 3.7.1. Lagrange Multiplier Test for ARCH Effect
• 3.7.2. Ljung Box Test
• 3.7.3. Test for Asymmetry in Volatility
• 3.7.4. Test of Normality of Residual
• 3.8. Estimation of GARCH (p, q)
• 3.8.1. Maximum Likelihood Estimation
• 3.8.2. Distribution Assumptions of Error
• 3.8.3. Bayesian Estimation of GARCH (p, q)
• 3.8.4. Model Priors and MCMC Schemes
• 3.9. GARCH-MIDAS Model
• 3.9.1. Short-term Volatility Component
• 3.9.2. Long-run Volatility Component (MIDAS)
• 3.9.3. MIDAS-Realized Volatility
• 3.9.4. MIDAS with Macroeconomic Variables
• 3.10. Assumptions of GARCH-MIDAS Component Models
• 3.11. Test for Building GARCH-MIDAS Model
• 3.11.1. Test for Time Varying Unconditional Volatility
• 3.11.2. Lagrange Multiplier (LM) Test
• 3.11.3. Portmanteau Test
• 3.12. Estimation of GARCH-MIDAS Component Model
• 3.12.1. Quasi Log-Likelihood Estimation
• 3.12.2. Quasi Log-Likelihood Function
• 3.13. Model Adequacy Checking
• 3.14. Forecasting Using GARCH-MIDAS Model
• 3.15. Evaluation of Forecasting Accuracy
• 3.15.1. Statistical Loss Function
• 3.15.2. Modified Diebold and Mariano Test
• 4. RESULTS AND DISCUSSION
• 4.1. Data Description
• 4.2. Descriptive Analysis Results
• 4.2.1. Graphical Analysis Results
• 4.2.2. Summary Statistics
• 4.3. Unit Root Test of Stationarity for Study Variables
• 4.4. Mean Model Specification
• 4.5. Residual Diagnostics for Mean Model
• 4.5.1. ARCH-LM Test for Estimated ARMA Model of Coffee Price Return
• 4.5.2. Ljung-Box Test from Estimated ARMA Model of Coffee Price Return
• 4.5.3. Result for Normality Test
• 4.6. Specification and Estimation of GARCH Model
• 4.6.1. Estimation of coffee price return using GARCH (1,1) under student-t distribution
• 4.6. 2. Normality test for residual from GARCH (1,1)
• 4.6. 3. Asymmetry Test
• 4.6.4. Time Varying Unconditional Volatility Test
• 4.6.5. Result of Li and Mak Portmanteau test
• 4.7. Bayesian Estimation Result of GARCH (1,1)
• 4.8. GARCH-MIDAS Model Estimation Result
• 4.9. In-sample Forecasting Using GARCH-MIDAS Model
• 4.10. Out-sample Forecasting Using GARCH-MIDAS
• 4.10.1. Evaluation of Forecasting Accuracy
• 4.10.2. Modified Diebold and Marino Test
• 5. CONCLUSION AND RECOMMENDATIONS
• 5.1. Conclusion
• 5.2. Recommendations

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This study aims to analyze the volatility of daily exported coffee prices in Ethiopia using the GARCH-MIDAS model. The GARCH-MIDAS model incorporates both short-term and long-term components of volatility, allowing for a comprehensive analysis of the factors driving coffee price fluctuations.

• Analysis of coffee price volatility in Ethiopia
• Application of the GARCH-MIDAS model
• Identification of macroeconomic determinants of volatility
• Evaluation of the forecasting ability of the GARCH-MIDAS model
• Policy recommendations for stabilizing coffee prices

Zusammenfassung der Kapitel (Chapter Summaries)

The study begins with an introduction that provides background information on coffee production and export in Ethiopia, outlines the research problem, objectives, significance, scope, and limitations of the study. Chapter 2 presents a literature review that explores theoretical concepts related to volatility, coffee price volatility in Ethiopia, and economic implications of price fluctuations. The chapter also reviews existing theoretical frameworks for volatility models, including the GARCH-MIDAS model.

Chapter 3 details the research methodology, including data sources, variable definitions, model specification, estimation techniques, and evaluation methods. Chapter 4 presents the results and discussion of the study, covering descriptive analysis, unit root tests, mean model specification, residual diagnostics, GARCH model estimation, GARCH-MIDAS model estimation, and forecasting performance evaluation.

Schlüsselwörter (Keywords)

The study focuses on key concepts such as daily coffee prices, volatility, GARCH-MIDAS model, Bayesian estimation, short-run and long-run volatility components, and the Ethiopian economy. This research utilizes various methodologies to explore the relationship between macroeconomic variables and coffee price volatility in Ethiopia, offering valuable insights for policymakers and stakeholders.

• College: Haramaya University
• Grade: 24
• Authors: Tekle Bobo (Author), Tesfaye Abera (Author), Jema Haji (Author)
• Publication Year: 2020
• Pages: 94
• Catalog Number: V936705
• ISBN (eBook): 9783346277268
• ISBN (Book): 9783346277275
• Language: English
• Tags: Daily coffee price, GARCH-MIDAS model, short-run and long run volatility component, ethiopia