A Bayesian Covariance Graphical and Latent Position Model for Multivariate Financial Time Series
نویسندگان
چکیده
منابع مشابه
Dynamic Covariance Models for Multivariate Financial Time Series
The accurate prediction of time-changing covariances is an important problem in the modeling of multivariate financial data. However, some of the most popular models suffer from a) overfitting problems and multiple local optima, b) failure to capture shifts in market conditions and c) large computational costs. To address these problems we introduce a novel dynamic model for time-changing covar...
متن کاملGraphical Gaussian modelling of multivariate time series with latent variables
In time series analysis, inference about causeeffect relationships among multiple times series is commonly based on the concept of Granger causality, which exploits temporal structure to achieve causal ordering of dependent variables. One major problem in the application of Granger causality for the identification of causal relationships is the possible presence of latent variables that affect ...
متن کاملBayesian Latent Threshold Modeling: Multivariate Time Series and Dynamic Networks
We discuss dynamic network modeling for multivariate time series, exploiting dynamic variable selection and model structure uncertainty strategies based on the recently introduced concept of “latent thresholding.” This dynamic modeling concept addresses a critical and challenging problem in multivariate time series and dynamic modeling: that of inducing formal probabilistic structures that are ...
متن کاملGraphical modelling for multivariate time series
Graphical models for multivariate time series is a concept extended by Dahlhaus (2000) from a random vector to a time series. We propose a test statistic to identify a graphical model for multivariate time series with the Kullback-Leibler distance between two spectral density matrices characterized by graphical models. Asymptotic null distribution is derived to be normal with the mean and varia...
متن کاملMultivariate Dynamic Kernels for Financial Time Series
We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done throu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2017
ISSN: 1556-5068
DOI: 10.2139/ssrn.3090236