炭素価格に対する季節的Ornstein–Uhlenbeckジャンプモデルの診断的評価：サンプル内適合とサンプル外性能

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:ジャンプ付きOUモデルの識別性と予測性能の乖離を示す、モデル選択における実証的教訓。

🏢実務担当者:炭素価格予測モデルへの過信を避け、シナリオ分析では不確実性幅を明示すべきという実務的示唆。

🏛政策担当者:炭素価格制度の投資評価に用いる価格モデルの限界を理解するための参考。

Reliable modelling of carbon prices is critical for climate policy design, investment appraisal, and risk management issues in emissions trading systems. In this paper, a seasonal Ornstein–Uhlenbeck (OU) jump-diffusion model is developed for the monthly EU Allowance (EUA) prices. The long-run mean is a causal 12-month trailing moving average plus a Fourier seasonal component without lookahead bias. The likelihood employs exact Poisson probabilities and is modeled by a compound Poisson process with Laplace jumps, capturing discontinuous movements. Maximum likelihood estimation of parameters is performed on EUA prices between January 2013 and December 2025. The model is evaluated in-sample (2013–2023) and out-of-sample (2024–2025) using RMSE, MAE, directional accuracy, and Diebold–Mariano tests, and benchmarked against GARCH (1,1), GJR-GARCH, and EGARCH, the natural models for return-scale volatility clustering, complementary to the OU family’s focus on price-level dynamics. AIC and BIC favor the jump-augmented specification in-sample, but this preference does not carry over out-of-sample: point forecasts only slightly outperform a random walk, Diebold–Mariano tests cannot rule out equal accuracy over the short evaluation window, and the jump model’s directional accuracy (48%) falls below both the seasonal-only specification (61%) and chance. Four further checks—joint maximum-likelihood estimation of the jump intensity λ, block-bootstrap parameter uncertainty, an extended 48-month out-of-sample window, and density-forecast diagnostics (log-score, CRPS, PIT)—are treated as central results rather than as robustness add-ons. They show that λ is weakly identified when estimated jointly, that several parameters carry wide confidence intervals, and that the in-sample preference of AIC and BIC does not translate into better out-of-sample point or density forecasts. These identification and forecasting findings, rather than the jump specification’s in-sample fit, are the paper’s principal contribution, and limitations are discussed explicitly.

• semanticscholar https://doi.org/10.3390/econometrics14040056first seen 2026-11-12 05:40:17 · last seen 2026-11-13 05:48:39