摘要翻译：
我们引入了Barndorff-Nielsen和Shephard随机波动模型的一个变体，其中非高斯Ornstein-Uhlenbeck过程描述了交易强度的一些度量，如交易量或交易次数，而不是不可观察的瞬时方差。在一个不是扩散但允许跳跃的二元模型中，我们基于鞅估计函数建立了一个显式估计量。假定这两个量都是在固定宽度的离散网格上观测的，且观测视界趋于无穷大。我们证明了估计量是相合的和渐近正态的，并给出了渐近协方差矩阵的显式表达式。通过对纽约证券交易所(NYSE)的国际商用机器公司(IBM)股票和纳斯达克的微软公司(MSFT)股票五年来的有限样本实验和统计分析，说明了我们的方法。
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英文标题：
《Joint analysis and estimation of stock prices and trading volume in
  Barndorff-Nielsen and Shephard stochastic volatility models》
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作者：
Friedrich Hubalek and Petra Posedel
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Mathematics        数学
二级分类：Probability        概率
分类描述：Theory and applications of probability and stochastic processes: e.g. central limit theorems, large deviations, stochastic differential equations, models from statistical mechanics, queuing theory
概率论与随机过程的理论与应用：例如中心极限定理，大偏差，随机微分方程，统计力学模型，排队论
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  We introduce a variant of the Barndorff-Nielsen and Shephard stochastic volatility model where the non Gaussian Ornstein-Uhlenbeck process describes some measure of trading intensity like trading volume or number of trades instead of unobservable instantaneous variance. We develop an explicit estimator based on martingale estimating functions in a bivariate model that is not a diffusion, but admits jumps. It is assumed that both the quantities are observed on a discrete grid of fixed width, and the observation horizon tends to infinity. We show that the estimator is consistent and asymptotically normal and give explicit expressions of the asymptotic covariance matrix. Our method is illustrated by a finite sample experiment and a statistical analysis on the International Business Machines Corporation (IBM) stock from the New York Stock Exchange (NYSE) and the Microsoft Corporation (MSFT) stock from Nasdaq during a history of five years.
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PDF链接：
https://arxiv.org/pdf/0807.3464