Multi-frequency bandwidth Empirical Market Factors in regularised covariance regression

Abstract

We survey and test non-constructive basis decomposition algorithms capable of analysing the structures present in financial security times series data. We name these implicit financial structures Empirical Market Factors (EMFs) as a homage to Huang et al. (1998) and Empirical Mode Decomposition (EMD) upon which part of this work is based. The EMF covariates are isolated via implicit factor extraction (IFE) which is a decomposition algorithm or feature engineering technique. `Implicit' is used to differentiate these covariates from explicit (easily observable or contructable) covariates such as the return of a market portfolio, ratios of market capitalisations, and book-to-market ratios such as in Fama and French (1993). The forthcoming investment period's covariance structure is forecast using these estimated EMFs in a regularised covariance regression (RCR) framework from Hoff and Niu (2012) to which we made very modest extensions. We present a real-world case study in which we test our method in forecasting the covariance of the potential investments before we weight the portfolio accordingly. The strategies assessed are also restricted to Long/Short Equity (LSE) and Risk Premia Parity (RPP) weighting strategies in which there are cumulative weight shorting restrictions (speci cally the 130/30 strategy) as opposed to restrictions on the individual weights - this mimics real-world shorting limitations. All these techniques and technologies (IFE, RCR, RPP, and LSE) are combined to construct risk-conscious leveraged RPP portfolios using EMFs in a lagged RCR framework