วิเคราะห์การไหลล้นของความผันผวนระหว่างราคาเอทานอลและราคาพืชพลังงานทดแทน

Abstract

The purpose of this paper is to analyze the volatility spillover between the energy crop market and ethanol markets in the world's largest consumers and producers of ethanol including the United States, Brazil, and China. We also study the volatility spillover in the price of renewable energy crops from major exporting countries to China, namely corn from the United States and cassava from Thailand to the price of ethanol in China over the period of 2015-2020. We will forecast the return and volatility of return in 12 months ahead. To achieve this purpose, we utilize dynamic forecasting models which is BEKK GARCH model applied to the Bayesian Estimation method to estimate volatility behaviors of an energy crop and ethanol return.

Our empirical results indicate significant bidirectional shock and volatility spillovers between the renewable energy crops and ethanol markets in the world's three major ethanol-producing countries. Brazil is the country that has shock and volatility spillover more than other countries. We can be classified co-volatility into two types: (1) low and constant co-volatility between renewable energy crops return and ethanol return in the USA market. However, the co-volatility has fluctuated in 2020 as a result of the COVID-19 pandemic. (2) moderate co-volatility between renewable energy crops return and ethanol return in Brazil and China markets. However, the co-volatility of Brazil and China markets showed a significant increase in the mid-2016 and 2020 as a result of the oil glut in 2016 and the COVID-19 pandemic in 2020. When we consider volatility spillover from renewable energy crop exporting countries to China, we found a volatility spillover between Thailand's cassava return and China's ethanol return.

The result of forecasting volatility of renewable energy crops re-turn and ethanol return showed that the forecasting value of China’s ethanol return fluctuated slightly, ranging from 0.0000 but not exceeding 0.1400. The forecasting value of China’s corn return is between 0.0000 and 0.0400. The forecasting value of the USA’s corn return is between 0.0000 and 0.1400, and the forecasting value of Thailand’s cassava return is between 0.0000 and 0.0800. Furthermore, we are forecasting the renewable energy crops return and ethanol return. Our result found that the models VAR.lag and BVAR.lag had the same appropriate lag length of 1. We also found that the BVAR.lag.1 model was more suitable for forecasting a return. The renewable energy crops’ return in the previous month had an impact on the renewable energy crops’ return in the next months. However, Electric Vehicle (EV) car volume was positively correlated with ethanol return in the next months.

The results are very useful for portfolio managers and investors when they design their asset allocation and portfolio optimizations against the downside risk. If investors want to decrease the portfolio risk, they should not consider both the renewable energy crop asset and ethanol asset in the same portfolio. We are recommended that policymakers be aware of the co-volatility between renewable energy crops return and ethanol return in the future. Policymakers should formulate policies to maintain market stability over the long term.