Long/Short Equity Risk Premia Parity Portfolios via Implicit Factors in Regularized Covariance Regression

A time-series basis decomposition and trend extraction technique known as Empirical Mode Decomposition (EMD), designed for multi-scale time-frequency decomposition in non-stationary time-series settings, will be combined with Regularised Covariance Regression (RCR) methods to produce a framework termed in this work: the EMD-RCR covariance forecasting model. This will produce a model framework capable of generating multi-time resolution adaptive forecasting models of predictive covariance forecasts for a universe of selected asset returns. This provides a unique method to obtain predictive covariance regression structures for the study of short and long time scale portfolio dynamics. An illustration is developed for active asset management which is based on dynamic management of a risk parity portfolio-of-portfolios investment strategy. In illustration of this methodology sector-based portfolios are constructed for the S&P500 which are termed in this work sector indices, from these eleven sector indices one can construct a dynamically evolving equal risk parity portfolio framework utilising the EMD-RCR methodology developed. The portfolio will be reweighted monthly based on the covariance structure forecast using covariance regression in which covariance regression factors will be obtained at multiple time-frequency scales endogenously from the asset price returns within each sector. The performance of the portfolios will be measured using multiple performance measures and contrasted against multiple benchmark portfolios constructed using several well-known portfolio optimisation techniques applied to the same assets.Empirical Mode Decomposition (EMD), Singular Spectrum Analysis (SSA), and Singular Spectrum Decomposition (SSD) will be used to isolate different frequency structures in the price data to be used as covariates in Covariance regression to optimise a risk parity portfolio with weighting restrictions. This paper serves to promote the use of what we term "implicit factor" extraction and RCR in the inter-related fields of portfolio optimization, horizon-specific active portfolio optimization, long/short equity portfolios, and risk parity portfolios