An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics
提出一个基于copula的膨胀多元整数计数模型，适用于高频金融数据中买卖报价变化的联合分布建模，并展示了如何推导隐含的买卖价差条件离散密度。
Abstract In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ℤ n , n ∈ ℕ . Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain; (ii) the tendency to cluster at certain outcome values; and (iii) contemporaneous dependence. These kinds of properties can be found for high‐ or ultra‐high‐frequency data describing the trading process on financial markets. We present a straightforward sampling method for such an inflated multivariate density through the application of an independence Metropolis–Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivariate density of bid and ask quote changes in a high‐frequency setup. We show how to derive the implied conditional discrete density of the bid–ask spread, taking quote clusterings (at multiples of 5 ticks) into account. Copyright © 2009 John Wiley & Sons, Ltd.