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Robust estimation of time-dependent precision matrix with application to the cryptocurrency market

Financial Innovation 2022年第1期8卷 1313-1337页

作者： Paola Stolfi Mauro Bernardi Davide Vergni Institute for Applied Mathematics“Mauro Picone”(IAC)-National Research Council of Italy Via dei Taurini 1900185 RomeItaly Department of Statistical Sciences University of PadovaVia C.Battisti24135121 PaduaItaly

Most financial signals show time dependency that,combined with noisy and extreme events,poses serious problems in the parameter estimations of statistical ***,when addressing asset pricing,portfolio selection,and investment strategies,accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent *** this regard,fundamental tools that increasingly attract research interests are precision matrix and graphical models,which are able to obtain insights into the joint evolution of financial *** this paper,we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time ***,we provide an algorithm to handle parameter estimations that uses the“maximization–minimization”*** apply the methodology to synthetic data to test its ***,we consider the cryptocurrency market as a real data application,given its remarkable suitability for the proposed method because of its volatile and unregulated nature.

关键词： Time-varying models Robust methods Kernel estimation precision matrix Divergence

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Bayesian Lasso with Neighborhood Regression Method for Gaussian Graphical Model

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Acta Mathematicae Applicatae Sinica 2017年第2期33卷 485-496页

作者： Fan-qun LI Xin-sheng ZHANG Department of Statistics School of Management Fudan University Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics

In this paper, we consider the problem of estimating a high dimensional precision matrix of Gaussian graphical model. Taking advantage of the connection between multivariate linear regression and entries of the precision matrix, we propose Bayesian Lasso together with neighborhood regression estimate for Gaussian graphical model. This method can obtain parameter estimation and model selection simultaneously. Moreover, the proposed method can provide symmetric confidence intervals of all entries of the precision matrix.

关键词： gaussian graphical model regression precision matrix Bayesian Lasso Frobenius loss

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