Package CovRegpy: Regularised Covariance Regression and Forecasting in Python

This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.7. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression; along with a powerful portfolio allocation framework for dynamic multi-period asset investment management.<br><br>The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: 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 implementations of software features including multivariate covariance time series models based on 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), Singular Spectrum Decomposition (SSD), and X11 decomposition framework, respectively. <br><br>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 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 developed through seeking to develop Covariance Regression for financial forecasting applications.