Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

Maruotti Antonello; Punzo Antonio; Bagnato Luca

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Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

机译：具有多元Leptokurtic-正态分量的隐马尔可夫和半马尔可夫模型，用于日收益序列的稳健建模

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We introduce multivariate models for the analysis of stock market returns. Our models are developed under hidden Markov and semi-Markov settings to describe the temporal evolution of returns, whereas the marginal distribution of returns is described by a mixture of multivariate leptokurtic-normal (LN) distributions. Compared to the normal distribution, the LN has an additional parameter governing excess kurtosis and this allows us a better fit to both the distributional and dynamic properties of daily returns. We outline an expectation maximization algorithm for maximum likelihood estimation which exploits recursions developed within the hidden semi-Markov literature. As an illustration, we provide an example based on the analysis of a bivariate time series of stock market returns.

机译：我们引入多元模型来分析股票市场收益。我们的模型是在隐马尔可夫和半马尔可夫设置下开发的，用于描述收益的时间演变，而收益的边际分布则由多元变态正态（LN）分布描述。与正态分布相比，LN具有一个额外的参数来控制超峰度，这使我们更适合日收益率的分布和动态属性。我们概述了最大似然估计的期望最大化算法，该算法利用了隐藏的半马尔可夫文献中开发的递归。作为说明，我们提供了一个基于股票市场收益的双变量时间序列分析的示例。

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来源
《Journal of Financial Econometrics》 |2019年第1期|91-117|共27页
作者
Maruotti Antonello; Punzo Antonio; Bagnato Luca;
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作者单位

Libera Univ Ss Maria Assunta, Rome, Italy|Univ Southampton, Southampton Stat Sci Res Inst, Southampton, Hants, England;

Univ Catania, Catania, Italy;

Univ Cattolica Sacro Cuore, Via Emilia Parmense 84, I-29122 Piacenza, Italy;

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正文语种 eng
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关键词
daily returns; elliptical distributions; EM algorithm; hidden Markov mode; hidden semi-Markov model; kurtosis; multivariate time series;

机译：日收益率;椭圆分布;EM算法;隐马尔可夫模型;隐半马尔可夫模型;峰度;多元时间序列;

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1. Robust classification of multivariate time series by imprecise hidden Markov models [J] . Alessandro Antonucci, Rocco De Rosa, Alessandro Giusti, 高分子論文集 . 2015,第ptab期

机译：通过不精确的隐马尔可夫模型对多元时间序列进行稳健分类
2. A New Framework for Modelling and Monitoring the Conversion of Cultivated Land to Built-up Land Based on a Hierarchical Hidden Semi-Markov Model Using Satellite Image Time Series [J] . Yuan Yuan, Lei Lin, Jingbo Chen, Remote Sensing . 2019,第2期

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3. An improved noise-robust voice activity detector based on hidden semi-Markov models [J] . Yuan Liang, Xianglong Liu, Yihua Lou, Pattern recognition letters . 2011,第7期

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4. Data monitoring of spacecraft using mixture probabilistic principal component analysis and hidden Semi-Markov models [C] . Tagawa Takaaki, Yairi Takehisa, Takata Noboru, 3rd International Conference on Data Mining and Intelligent Information Technology Applications . 2011

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5. Modeling of multivariate time series using hidden Markov models. [D] . Kirshner, Sergey. 2005

机译：使用隐马尔可夫模型对多元时间序列进行建模。
6. Clustering Multivariate Time Series Using Hidden Markov Models [O] . Shima Ghassempour, Federico Girosi, Anthony Maeder 2014

机译：使用隐马尔可夫模型对多元时间序列进行聚类
7. Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series [O] . Bulla Jan 2006

机译：隐马尔可夫模型和隐马尔可夫模型在金融时间序列中的应用

Hidden Markov and Semi-Markov Models with Multivariate Leptokurtic-Normal Components for Robust Modeling of Daily Returns Series

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