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.
The package is based on the paper:
J. Liu, S. Kumar, and D. P. Palomar (2019). 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.