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Dependence Modeling最新文献

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Testing for explosive bubbles: a review 爆炸性气泡的测试：综述
IF 0.7
Dependence Modeling Pub Date : 2022-07-17 DOI: 10.2139/ssrn.3779111
A. Skrobotov
{"title":"Testing for explosive bubbles: a review","authors":"A. Skrobotov","doi":"10.2139/ssrn.3779111","DOIUrl":"https://doi.org/10.2139/ssrn.3779111","url":null,"abstract":"Abstract This review discusses methods of testing for explosive bubbles in time series. A large number of recently developed testing methods under various assumptions about innovation of errors are covered. The review also considers the methods for dating explosive (bubble) regimes. Special attention is devoted to time-varying volatility in the errors. Moreover, the modelling of possible relationships between time series with explosive regimes is discussed.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":" ","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47255067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Consistency of mixture models with a prior on the number of components 对成分数量有先验的混合模型的一致性
IF 0.7
Dependence Modeling Pub Date : 2022-05-06 DOI: 10.1515/demo-2022-0150
Jeffrey W. Miller
{"title":"Consistency of mixture models with a prior on the number of components","authors":"Jeffrey W. Miller","doi":"10.1515/demo-2022-0150","DOIUrl":"https://doi.org/10.1515/demo-2022-0150","url":null,"abstract":"Abstract This article establishes general conditions for posterior consistency of Bayesian finite mixture models with a prior on the number of components. That is, we provide sufficient conditions under which the posterior concentrates on neighborhoods of the true parameter values when the data are generated from a finite mixture over the assumed family of component distributions. Specifically, we establish almost sure consistency for the number of components, the mixture weights, and the component parameters, up to a permutation of the component labels. The approach taken here is based on Doob’s theorem, which has the advantage of holding under extraordinarily general conditions, and the disadvantage of only guaranteeing consistency at a set of parameter values that has probability one under the prior. However, we show that in fact, for commonly used choices of prior, this yields consistency at Lebesgue-almost all parameter values, which is satisfactory for most practical purposes. We aim to formulate the results in a way that maximizes clarity, generality, and ease of use.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"11 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44930100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 116
Fast inference methods for high-dimensional factor copulas 高维因子copula的快速推理方法
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0117
Alex Verhoijsen, Pavel Krupskiy
{"title":"Fast inference methods for high-dimensional factor copulas","authors":"Alex Verhoijsen, Pavel Krupskiy","doi":"10.1515/demo-2022-0117","DOIUrl":"https://doi.org/10.1515/demo-2022-0117","url":null,"abstract":"Abstract Gaussian factor models allow the statistician to capture multivariate dependence between variables. However, they are computationally cumbersome in high dimensions and are not able to capture multivariate skewness in the data. We propose a copula model that allows for arbitrary margins, and multivariate skewness in the data by including a non-Gaussian factor whose dependence structure is the result of a one-factor copula model. Estimation is carried out using a two-step procedure: margins are modelled separately and transformed to the normal scale, after which the dependence structure is estimated. We develop an estimation procedure that allows for fast estimation of the model parameters in a high-dimensional setting. We first prove the theoretical results of the model with up to three Gaussian factors. Then, simulation results confirm that the model works as the sample size and dimensionality grow larger. Finally, we apply the model to a selection of stocks of the S&P500, which demonstrates that our model is able to capture cross-sectional skewness in the stock market data.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"270 - 289"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67145578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analyzing and forecasting financial series with singular spectral analysis 金融序列的奇异谱分析与预测
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0112
A. Makshanov, A. Musaev, D. Grigoriev
{"title":"Analyzing and forecasting financial series with singular spectral analysis","authors":"A. Makshanov, A. Musaev, D. Grigoriev","doi":"10.1515/demo-2022-0112","DOIUrl":"https://doi.org/10.1515/demo-2022-0112","url":null,"abstract":"Abstract Modern techniques for managing multidimensional stochastic processes that reflect the dynamics of unstable environments are proactive, which refers to decision making based on forecasting the system’s state vector evolution. At the same time, the dynamics of open nonlinear systems are largely determined by their chaotic nature, which leads to a violation of stationarity and ergodicity of the series of observations and, as a result, to a catastrophic decrease in the efficiency of forecasting algorithms based on traditional methods of multivariate statistical data analysis. In this article, we make an attempt to reduce the instability influence by employing singular spectrum analysis (SSA) algorithms. This technique has been employed in a wide class of applied data analysis problems formulated in terms of singular decomposition of data matrices: technologies of immunocomputing and SSA.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"215 - 224"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43559671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Multiple inflated negative binomial regression for correlated multivariate count data 相关多元计数数据的多重膨胀负二项回归
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0149
Joseph Mathews, Sumangala Bhattacharya, Sumen Sen, I. Das
{"title":"Multiple inflated negative binomial regression for correlated multivariate count data","authors":"Joseph Mathews, Sumangala Bhattacharya, Sumen Sen, I. Das","doi":"10.1515/demo-2022-0149","DOIUrl":"https://doi.org/10.1515/demo-2022-0149","url":null,"abstract":"Abstract This article aims to provide a method of regression for multivariate multiple inflated count responses assuming the responses follow a negative binomial distribution. Negative binomial regression models are common in the literature for modeling univariate and multivariate count data. However, two problems commonly arise in modeling such data: choice of the multivariate form of the underlying distribution and modeling the zero-inflated structure of the data. Copula functions have become a popular solution to the former problem because they can model the response variables’ dependence structure. The latter problem is often solved by modeling an assumed latent variable Z Z generating excess zero-valued counts in addition to the underlying distribution. However, despite their flexibility, zero-inflation models do not account for the case of m m additional inflated values at k 1 , k 2 , … , k m {{bf{k}}}_{1},{{bf{k}}}_{2},ldots ,{{bf{k}}}_{m} . We propose a multivariate multiple-inflated negative binomial regression model for modeling such cases. Furthermore, we present an iterative procedure for estimating model parameters using maximum likelihood estimation. The multivariate distribution functions considering the dependence structure of the response vectors are found using copula methods. The proposed method is illustrated using simulated data and applied to real data.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"290 - 307"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49648189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Networks of causal relationships in the U.S. stock market 美国股市因果关系网络
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0110
Oleg Shirokikh, G. Pastukhov, Alexander Semenov, S. Butenko, Alexander Veremyev, E. Pasiliao, V. Boginski
{"title":"Networks of causal relationships in the U.S. stock market","authors":"Oleg Shirokikh, G. Pastukhov, Alexander Semenov, S. Butenko, Alexander Veremyev, E. Pasiliao, V. Boginski","doi":"10.1515/demo-2022-0110","DOIUrl":"https://doi.org/10.1515/demo-2022-0110","url":null,"abstract":"Abstract We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as “causal market graphs”) are constructed based on publicly available stock prices time series data during 2001–2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most “influential” market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"177 - 190"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45411069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Stable tail dependence functions – some basic properties 稳定的尾相关函数——一些基本性质
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0114
P. Ressel
{"title":"Stable tail dependence functions – some basic properties","authors":"P. Ressel","doi":"10.1515/demo-2022-0114","DOIUrl":"https://doi.org/10.1515/demo-2022-0114","url":null,"abstract":"Abstract We prove some important properties of the extremal coefficients of a stable tail dependence function (“STDF”) and characterise logistic and some related STDFs. The well known sufficient conditions for composebility of logistic STDFs are shown to be also necessary.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"225 - 235"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49231293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Time series with infinite-order partial copula dependence 具有无穷阶偏联结关系的时间序列
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0105
Martin Bladt, A. McNeil
{"title":"Time series with infinite-order partial copula dependence","authors":"Martin Bladt, A. McNeil","doi":"10.1515/demo-2022-0105","DOIUrl":"https://doi.org/10.1515/demo-2022-0105","url":null,"abstract":"Abstract Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that generalizes Gaussian ARMA and ARFIMA processes to allow both non-Gaussian marginal behaviour and a non-Gaussian description of the serial partial dependence structure. Extensions of classical causal and invertible representations of linear processes to general s-vine processes are proposed and investigated. A practical and parsimonious method for parameterizing s-vine processes using the Kendall partial autocorrelation function is developed. The potential of the resulting models to give improved statistical fits in many applications is indicated with an example using macroeconomic data.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"87 - 107"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45714859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Equity returns and sentiment 股票回报和市场情绪
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0109
Zibin Huang, R. Ibragimov
{"title":"Equity returns and sentiment","authors":"Zibin Huang, R. Ibragimov","doi":"10.1515/demo-2022-0109","DOIUrl":"https://doi.org/10.1515/demo-2022-0109","url":null,"abstract":"Abstract This paper analyzes approximately 100 Gigabytes of raw text data from Twitter with keywords “AAPL,” “S&P 500,” “FTSE100” and “NASDAQ” to explore the relationship between sentiment and the returns and prices on the Apple stock and the S&P 500, FTSE 100 and NASDAQ indices. The findings point to significant relationship and dependence between sentiment measures and the S&P 500 and FTSE 100 indices’ returns and prices. The econometric analysis of dependence between the aforementioned variables in the paper is presented in some detail for illustration of the methodology employed.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"159 - 176"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Maximal asymmetry of bivariate copulas and consequences to measures of dependence 二元联结的最大不对称性及其对依赖性度量的影响
IF 0.7
Dependence Modeling Pub Date : 2022-01-01 DOI: 10.1515/demo-2022-0115
Florian Griessenberger, W. Trutschnig
{"title":"Maximal asymmetry of bivariate copulas and consequences to measures of dependence","authors":"Florian Griessenberger, W. Trutschnig","doi":"10.1515/demo-2022-0115","DOIUrl":"https://doi.org/10.1515/demo-2022-0115","url":null,"abstract":"Abstract In this article, we focus on copulas underlying maximal non-exchangeable pairs ( X , Y ) left(X,Y) of continuous random variables X , Y X,Y either in the sense of the uniform metric d ∞ {d}_{infty } or the conditioning-based metrics D p {D}_{p} , and analyze their possible extent of dependence quantified by the recently introduced dependence measures ζ 1 {zeta }_{1} and ξ xi . Considering maximal d ∞ {d}_{infty } -asymmetry we obtain ζ 1 ∈ 5 6 , 1 {zeta }_{1}in left[frac{5}{6},1right] and ξ ∈ 2 3 , 1 xi in left[frac{2}{3},1right] , and in the case of maximal D 1 {D}_{1} -asymmetry we obtain ζ 1 ∈ 3 4 , 1 {zeta }_{1}in left[frac{3}{4},1right] and ξ ∈ 1 2 , 1 xi in left(frac{1}{2},1right] , implying that maximal asymmetry implies a very high degree of dependence in both cases. Furthermore, we study various topological properties of the family of copulas with maximal D 1 {D}_{1} -asymmetry and derive some surprising properties for maximal D p {D}_{p} -asymmetric copulas.","PeriodicalId":43690,"journal":{"name":"Dependence Modeling","volume":"10 1","pages":"245 - 269"},"PeriodicalIF":0.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46395105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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