Package CovRegpy : Regularised Covariance Regression and Forecasting in Python

This paper will outline the functionality available in the CovRegpy package for actuarial practitioners, wealth managers, fund managers, and portfolio analysts written in Python 3.7. The major contributions of CovRegpy can be found in the CovRegpy_DCC.py, CovRegpy_IFF.py, CovRegpy_RCR.py, CovRegpy_RPP.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py. These seven scripts contain the Dynamic Conditional Correlation (DCC) framework, Instantaneous Frequency Forecasting (IFF) framework, Regularised Covariance Regression (RCR) framework, Risk Premia Parity (RPP) weighting functions, Singular Spectrum Analysis (SSA), and Singular Spectrum Decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. The CovRegpy_IFF.py, CovRegpy_SSA.py, CovRegpy_SSD.py, and CovRegpy_X11.py scripts were developed originally out of the study of Empirical Mode Decomposition (EMD) and the development of the AdvEMDpy package. The CovRegpy_DCC.py, CovRegpy_RCR.py, and CovRegpy_RPP.py scripts were developed out of seeking to develop Covariance Regression for financial applications