Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed.
For a quick help see the README file: GitHub-README.
For more details see the vignette: CRAN-vignette.
Junyan Liu, Rui Zhou, and Daniel P. Palomar
J. Liu, S. Kumar, and D. P. Palomar, "Parameter estimation of heavy-tailed AR model with missing data via stochastic EM," IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172, Apr. 2019. <https://doi.org/10.1109/TSP.2019.2899816>
R. Zhou, J. Liu, S. Kumar, and D. P. Palomar, "Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling," IEEE Trans. on Signal Processing, vol. 68, pp. 6198-6211, Oct. 2020. <https://doi.org/10.1109/TSP.2020.3033378>
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.