Exploring the risk spillover effects between carbon market and electricity market: A bidimensional empirical mode decomposition based conditional value at risk approach

Zhu, Bangzhu; Huang, Liqing; Yuan, Lili; Ye, Shunxin; Wang, Ya Ping

doi:10.1016/j.iref.2020.01.003

Cited by 68 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spillover effect is another global issue that affect commodity market either positively or negatively, but more often related to negative effects. In studying the spillover effect between electricity and carbon markets, [44] proposed EMD method to decompose both commodities price series into individual IMFs; the spillover effects of the two markets were detected by using a conditional value at risk. The evaluation results revealed a positive spillover effect of carbon commodity market over electricity commodity market prices, however, there was negative spillover effects of electricity commodity market over carbon commodity market prices.…”

Section: A Related Workmentioning

confidence: 99%

Modeling and Forecasting Commodity Futures Prices: Decomposition Approach

et al. 2022

View full text Add to dashboard Cite

Price instability is a paramount concern since commodity prices are associated with the livelihood and the economy of a nation as a whole; any extraordinary price fluctuation in the futures market shows that forecasts in commodities is an essential venture. The difficulties in predicting commodity prices are due to the unpredictability of the world's financial issues, fiscal dispensation, the speculative market's exacerbation, and several other elements. This study aims to model and forecast the market price of commodity futures. We applied decomposition techniques, empirical mode decomposition (EMD), and variational mode decomposition (VMD) to three commodities: corn, crude oil, and gold over the commodity spot market prices. We used the Granger causality test to establish mutual relationships among the three commodity futures prices. Three commodity price data with different periods were decomposed into several intrinsic modes. Using three forecasting performance evaluation criteria, statistical measures such as mean absolute error (MAE), root mean square error (RMSE), and mean percentage error (MAPE) to compare the capabilities of the suggested models. We also introduced Diebold Mariano (DM) test in selecting the optimal models for each commodity, since MAE, RMSE and MAPE have some shortcomings. We found that the combined models outperformed the individual back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models in forecasting corn and crude oil futures prices series, while BPNN emerged as the optimal model for predicting gold futures prices series. Variational mode decomposition emerged as the ideal data pre-treatment method and contributed to enhancing the predicting ability of the BPNN and the ARIMA models. The empirical results showed that models combined with decomposition methods predict commodity futures prices accurately and can easily capture the volatility in commodity futures prices.

show abstract

Section: A Related Workmentioning

confidence: 99%

Modeling and Forecasting Commodity Futures Prices: Decomposition Approach

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Zhu et al [36] proposed the EMD method to study the spillover effects between carbon market and electricity market. EMD was applied to decompose carbon and electricity prices into several modes with different frequencies and used conditional value at risk was used to detect the spillover effects between carbon market and electricity market.…”

Section: Related Studiesmentioning

confidence: 99%

Determinants of Commodity Futures Prices: Decomposition Approach

Antwi

Gyamfi

Kyei

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Developing models to analyze time series is a very sophisticated, time-consuming, but interesting experience for researchers. Commodity price component determination is challenging due to remarkable price volatility, uncertainty, and complexity in the futures market. This study aims to determine the components that drive the market price of commodity futures. This study utilized the decomposition methods, empirical mode decomposition (EMD), and variational mode decomposition (VMD), to analyze three commodity futures prices data: corn from agricultural products, crude oil from energy, and gold from industrial metal. We applied these techniques to decompose the daily data of each commodity price from different periods and frequencies into individual intrinsic mode functions for EMD and modes for VMD. We used the hierarchical clustering method and Euclidean distance approach to classify the IMFs and modes into high-frequency, low-frequency, and trend. Next, applying statistical measures, particularly, the Pearson product-moment correlation coefficient, Kendall rank correlation, and Spearman rank correlation coefficient, we observed that the trend and low-frequency parts of the market price are the main drivers of commodity futures markets’ price fluctuations. The low-frequencies are caused by special events. In a nutshell, commodity futures prices are affected by economic development rather than short-lived market variations caused by ordinary disequilibrium of supply-demand.

show abstract

“…Since the establishment of the carbon market, its price has changed dramatically, and there are more uncertainties and higher risks in the carbon market compared with the traditional financial market (Chai & Zhou, 2018;Zhu et al, 2020). Therefore, exclusively analyzing the return spillover effect cannot help us fully understand the relationship between the carbon market and other markets.…”

Section: Introductionmentioning

confidence: 99%

The volatility spillover effect of the European Union (EU) carbon financial market

Zeng

Jia

et al. 2021

Journal of Cleaner Production

View full text Add to dashboard Cite

This paper modifies the BEKK-GARCH model based on the empirical results of the VAR model to analyze the dynamic volatility spillover effect between the European Union allowance (EUA) and certified emissions reduction (CER) markets during the second and third phases of the European Union Emission Trading System (EU ETS). The empirical results show that (1) an asymmetric volatility spillover effect exists between the EUA and CER markets and that the EUA market has a more significant volatility spillover effect on the CER market, and (2) the volatility spillover effect between the EUA and CER becomes weaker in phase III since the European Commission has limited the substitution of CER for EUA more strictly and the global carbon reduction requirements have become less demanding. Our study can help investors and managers of carbon market to have a more comprehensive understanding of the information and risk transmission mechanism between the EUA and CER markets, thus, providing them with a basis make investment decisions and formulate policies.

show abstract

Exploring the risk spillover effects between carbon market and electricity market: A bidimensional empirical mode decomposition based conditional value at risk approach

Cited by 68 publications

References 32 publications

Modeling and Forecasting Commodity Futures Prices: Decomposition Approach

Modeling and Forecasting Commodity Futures Prices: Decomposition Approach

Determinants of Commodity Futures Prices: Decomposition Approach

The volatility spillover effect of the European Union (EU) carbon financial market

Contact Info

Product

Resources

About