Dynamic Discrete Mixtures for High-Frequency Prices
针对股票价格变动在微观结构上的离散性，提出一个多元离散价格变动模型，用两个隐马尔可夫链驱动零膨胀Skellam分布的动态混合，并通过EM算法估计，实证检验了纽约证券交易所多只股票价格变动的联合分布、密度预测和波动率预测。
The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivariate time series at hand. We study the properties of the model, which is a dynamic mixture of zero-inflated Skellam distributions. We develop an expectation-maximization algorithm with closed-form M-step that allows us to estimate the model by maximum likelihood. In the empirical application, we study the joint distribution of the price changes of a number of assets traded on NYSE. Particular focus is dedicated to the assessment of the quality of univariate and multivariate density forecasts, and of the precision of the predictions of moments like volatility and correlations. Finally, we look at the predictability of price staleness and its determinants in relation to the trading activity on the financial markets. Copyright © 2021 Informa UK Limited