Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors

Heng Lei, Minggao Xue and Huiling Liu

Energy Economics, 2022, vol. 113, issue C

Abstract: This study presents a hybrid model for forecasting the conditional probability distribution of carbon allowance prices. We primarily use the singular spectrum analysis method to process the non-stationary signals, and the non-crossing composite quantile regression neural network algorithm to achieve accurate, robust, and realistic quantile forecasts. We also include multiple influencing factors to enhance the forecasting performance. Empirical applications to the China and Europe carbon markets show that the proposed model significantly outperforms other benchmark models in terms of point and density forecasting accuracy. In addition, distribution forecasts can lead to economic gains using a simple switching trading strategy. Our hybrid framework is also useful for risk measurement and management.

Keywords: Carbon price; Probability forecasting; Conditional probability; Singular spectrum analysis; Non-crossing composite quantile regression neural network (search for similar items in EconPapers)
JEL-codes: C45 E37 G12 Q43 Q47 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:113:y:2022:i:c:s0140988322003395

DOI: 10.1016/j.eneco.2022.106189

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