碳价格预测能否改善合规采购？来自欧盟配额的证据
Do Carbon Price Forecasts Improve Compliance Procurement? Evidence from European Union Allowances
AI总结：
研究欧盟碳排放配额价格的短期可预测性对合规采购的影响，通过特定方法进行预测，结果显示预测能降低采购成本，收益源于固定窗口内重新分配购买，而非次日方向时机。
AI中文摘要：
排放交易系统覆盖的企业不仅需要预测来评估配额价值，还需决定购买时机。本文探讨欧盟配额（EUA）价格是否具有短期可预测性，且在预测源信息设计下仍能改善模拟合规采购。利用2019年至2025年的每日数据进行提前一至五个交易日的直接预测。所有预测变量在预测源处均可观测，校准和模型选择规则在最终验证前固定。发布的预测在14个基准中各预测期的点估计均方根误差最低，在第三和第四期损失差异证据最强。相对于随机游走，样本外R^2从一天的1.2%升至五天的15.5%。然后在考虑执行成本、市场影响、容量限制和尾部风险的受限采购问题中使用预测路径，敏感性分析增加需求不确定性。对于固定的10万EUA订单，优化后的时间表相对于在h = 2至h = 5各期均匀执行，平均实现成本降低8.5至38.5个基点。收益来自在固定窗口内重新分配购买，而非可靠的次日方向时机。
英文摘要：
Firms covered by emissions trading systems need forecasts not only to value allowances, but also to decide when to buy them. This paper asks whether European Union Allowance (EUA) prices contain short-horizon predictability that survives a forecast-origin information design and improves simulated compliance procurement. Using daily data from 2019 to 2025, we produce direct forecasts for one to five trading days ahead. All predictors are observable at the forecast origin, and calibration and model-selection rules are fixed before the final holdout. The released forecast has the lowest point-estimate RMSE at every horizon among fourteen benchmarks, with the strongest loss-difference evidence at horizons three and four. Relative to a random walk, out-of-sample R^2 rises from 1.2% at one day to 15.5% at five days. We then use the forecast path in a constrained procurement problem with execution costs, market impact, capacity limits, and tail risk; sensitivity exercises add demand uncertainty. For a fixed 100,000-EUA order, optimized schedules lower average realized costs by 8.5 to 38.5 basis points relative to uniform execution across horizons h=2 to h=5. The gains come from reallocating purchases within a fixed window, not from reliable next-day directional timing.
