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

【Author】 Stolfi, Paola; Bernardi, Mauro; Vergni, Davide

【Source】FINANCIAL INNOVATION

【影响因子】6.793

【Abstract】Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, 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 context. In 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 quantities. In 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 series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, 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.

【Keywords】Time-varying models; Robust methods; Kernel estimation; Precision matrix; Divergence

【发表时间】2022 MAY 5

【收录时间】2022-05-13

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-数字货币