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A Bayesian-Tuned Gaussian Process Approach to Forecasting Carbon Market Prices: A Case Study of China’s Emissions Trading Scheme
16 references·0 citing·38 second hop
A Bayesian-Tuned Gaussian Process Approach to Forecasting Carbon Market Prices: A Case Study of China’s Emissions Trading Scheme (2026)A Bayesian-Tuned Gaussian Process Approac…Machine learning gold price forecasting (2025)Machine learning gold price f…High-Frequency CSI300 Spot and Futures Price Predictions via the Neural Network (2025)High-Frequency CSI300 Spot an…China commodity price index (CCPI) forecasting via the neural network (2025)China commodity price index (…Machine learning Brent crude oil price forecasts (2024)Machine learning Brent crude …Forecasts of China Mainland New Energy Index Prices through Gaussian Process Regressions (2024)Forecasts of China Mainland N…A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors (2021)A Carbon Price Prediction Mod…The efficiency of carbon trading market in China: evidence from variance ratio tests (2019)The efficiency of carbon trad…Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition (2018)Predicting the Carbon Price S…Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk (2017)Modeling and forecasting the …Exploring the price dynamics of CO2 emissions allowances in China's emissions trading scheme pilots (2017)Exploring the price dynamics …Emission trading and carbon market performance in Shenzhen, China (2017)Emission trading and carbon m…Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models (2016)Modeling and forecasting the …Forecasting carbon futures volatility using GARCH models with energy volatilities (2013)Forecasting carbon futures vo…Chapter 4 Forecast Combinations (2006)Chapter 4 Forecast Combinatio…A test for independence based on the correlation dimension (1996)A test for independence based…Forecasting Multiple Time Series with Little Prior Information (1990)Forecasting Multiple Time Ser…Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)ARCH modeling in finance (1992)Analysis of Financial Time Series (2006)Introductory Econometrics for Finance (2008)The Combination of Forecasts (1969)Combining forecasts: A review and annotated bibliography (1989)The M3-Competition: results, conclusions and implications (2000)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Investor Sentiment Aligned: A Powerful Predictor of Stock Returns (2014)Forecasting: theory and practice (2022)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)The dynamic volatility spillover between European carbon trading market and fossil energy market (2015)The dynamic spillover between carbon and energy markets: New evidence (2018)A hybrid model for carbon price forecastingusing GARCH and long short-term memory network (2021)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Carbon price forecasting with complex network and extreme learning machine (2019)Forecasting carbon price using empirical wavelet transform and gated recurrent unit neural network (2019)A multi-factor integrated model for carbon price forecasting: Market interaction promoting carbon emission reduction (2021)A forecast comparison of volatility models: does anything beat a GARCH(1,1)? 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References

  • 1996A test for independence based on the correlation dimension3,526 cited
  • 2006Chapter 4 Forecast Combinations1,318 cited
  • 2013Forecasting carbon futures volatility using GARCH models with energy volatilities346 cited
  • 2017Emission trading and carbon market performance in Shenzhen, China196 cited
  • 2024Forecasts of China Mainland New Energy Index Prices through Gaussian Process Regressions178 cited
  • 2017Modeling and forecasting the volatility of carbon emission market: The role of outliers, time-varying jumps and oil price risk176 cited
  • 2025China commodity price index (CCPI) forecasting via the neural network166 cited
  • 2025High-Frequency CSI300 Spot and Futures Price Predictions via the Neural Network160 cited
  • 2024Machine learning Brent crude oil price forecasts133 cited
  • 2016Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models123 cited
  • 2021A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors85 cited
  • 2017Exploring the price dynamics of CO2 emissions allowances in China's emissions trading scheme pilots77 cited
  • 2025Machine learning gold price forecasting70 cited
  • 2018Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition

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61 cited
  • 1990Forecasting Multiple Time Series with Little Prior Information58 cited
  • 2019The efficiency of carbon trading market in China: evidence from variance ratio tests47 cited
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