标题： 高频数据的贝叶斯随机波动模型 显示英文标题

标题： Bayesian stochastic volatility models for high-frequency data

摘要： 我们针对高频股市数据制定了一种离散时间的贝叶斯随机波动率模型，该模型直接考虑了微观结构噪声，并概述了一种用于参数估计的马尔可夫链蒙特卡洛算法。 本文所描述的方法旨在所有采样时间尺度上保持一致性，目的是从任意短采样周期收集的数据中估算潜在的对数波动率信号。 为了实现这一目标，我们仔细开发了一种提取先验的方法。 模拟数据和真实数据得出的经验结果表明，在状态空间公式中直接考虑微观结构，可以得到驱动价格的对数波动率过程的良好校准估计。 显示英文摘要

摘要： We formulate a discrete-time Bayesian stochastic volatility model for high-frequency stock-market data that directly accounts for microstructure noise, and outline a Markov chain Monte Carlo algorithm for parameter estimation. The methods described in this paper are designed to be coherent across all sampling timescales, with the goal of estimating the latent log-volatility signal from data collected at arbitrarily short sampling periods. In keeping with this goal, we carefully develop a method for eliciting priors. The empirical results derived from both simulated and real data show that directly accounting for microstructure in a state-space formulation allows for well-calibrated estimates of the log-volatility process driving prices.