标题： 高频数据同步 显示英文标题

标题： Data Synchronization at High Frequencies

摘要： 高频金融市场中的异步交易给计量分析带来了显著偏差，扭曲了风险估计并导致次优的投资组合决策。 现有的同步方法，如前一时刻法，存在信息丢失并造成人为的价格停滞。 我们引入了一个新的框架，将数据同步挑战重新表述为一个约束矩阵补全问题。 我们的方法通过最小化其核范数来恢复高频价格增量的潜在矩阵——捕捉底层低秩因子结构——受制于从观察到的异步价格变化得出的大规模线性系统。 理论上，我们证明了我们的估计量的存在性和唯一性，并建立了其收敛速率。 一个关键的理论见解是，我们的方法能够准确且稳健地利用来自频繁和不频繁交易资产的信息，克服了传统方法中效率损失的一个关键困难。 实证上，通过大量的模拟和一个大型标普500股票面板，我们证明我们的方法明显优于现有基准。 它不仅实现了显著更低的同步误差，还纠正了系统性风险估计（即特征值）以及由价格停滞引起的贝塔值估计中的偏差。 至关重要的是，使用我们同步数据构建的组合产生了持续且经济上显著更高的样本外夏普比率。 我们的框架提供了一个强大的工具，用于揭示资产价格的真实动态，对高频风险管理、算法交易和计量推断有直接的影响。 显示英文摘要

摘要： Asynchronous trading in high-frequency financial markets introduces significant biases into econometric analysis, distorting risk estimates and leading to suboptimal portfolio decisions. Existing synchronization methods, such as the previous-tick approach, suffer from information loss and create artificial price staleness. We introduce a novel framework that recasts the data synchronization challenge as a constrained matrix completion problem. Our approach recovers the potential matrix of high-frequency price increments by minimizing its nuclear norm -- capturing the underlying low-rank factor structure -- subject to a large-scale linear system derived from observed, asynchronous price changes. Theoretically, we prove the existence and uniqueness of our estimator and establish its convergence rate. A key theoretical insight is that our method accurately and robustly leverages information from both frequently and infrequently traded assets, overcoming a critical difficulty of efficiency loss in traditional methods. Empirically, using extensive simulations and a large panel of S&P 500 stocks, we demonstrate that our method substantially outperforms established benchmarks. It not only achieves significantly lower synchronization errors, but also corrects the bias in systematic risk estimates (i.e., eigenvalues) and the estimate of betas caused by stale prices. Crucially, portfolios constructed using our synchronized data yield consistently and economically significant higher out-of-sample Sharpe ratios. Our framework provides a powerful tool for uncovering the true dynamics of asset prices, with direct implications for high-frequency risk management, algorithmic trading, and econometric inference.