针对自相关不确定性，提出新优化方法提升决策质量。
Autocorrelated Optimize-via-Estimate: Predict-then-Optimize versus Finite-sample Optimal
- 基于足够统计量构建递归计算的A-OVE模型
- 在投资组合优化中表现接近理想情况，显著优于预测-再优化方法
- 即使模型有小偏差仍保持稳定，适合实际应用
在有限样本条件下，直接优化外样本性能的模型已成为数据驱动优化中替代传统估计-再优化方法的有力选择。本文针对自相关不确定性（具体为向量自回归移动平均VARMA(p,q)过程），提出一种自相关优化-通过-估计（A-OVE）模型，该模型将外样本最优解表示为足够统计量的函数，并给出其递归计算形式。我们在含交易成本的投资组合优化问题上评估了这些模型，结果表明：A-OVE的遗憾值接近完美信息预言机，显著优于预测-再优化的机器学习基准。值得注意的是，尽管某些机器学习模型精度更高，但其决策质量反而更差，呼应了数据驱动优化领域的最新发现。该方法在小误设情况下仍能保持良好性能。
原文摘要 · Abstract (English)
Models that directly optimize for out-of-sample performance in the finite-sample regime have emerged as a promising alternative to traditional estimate-then-optimize approaches in data-driven optimization. In this work, we compare their performance in the context of autocorrelated uncertainties, specifically, under a Vector Autoregressive Moving Average VARMA(p,q) process. We propose an autocorrelated Optimize-via-Estimate (A-OVE) model that obtains an out-of-sample optimal solution as a function of sufficient statistics, and propose a recursive form for computing its sufficient statistics. We evaluate these models on a portfolio optimization problem with trading costs. A-OVE achieves low regret relative to a perfect information oracle, outperforming predict-then-optimize machine learning benchmarks. Notably, machine learning models with higher accuracy can have poorer decision quality, echoing the growing literature in data-driven optimization. Performance is retained under small mis-specification.
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