Risk models vs characteristic models from an investor’s perspective

Purpose The purpose of this paper is to analyze the implications of the risk versus characteristic debate from the perspective of a mean-variance investor. Design/methodology/approach Expected returns and the variance-covariance matrix are estimated based on various characteristic and risk models and evaluated for the purpose of mean-variance portfolios. Findings Return estimates from characteristic models are most informative to investors. Risk-factor models provide the most informative estimates of the risk. A mean-variance investor should rely on combinations of the two model types. Originality/value Although the risk vs characteristic debate is a binary academic debate, our findings from an investor's perspective suggest to make use of the best of both worlds.

Constraint Violations in Stochastically Generated Data: Detection and Correction Strategies

We consider the generation of stochastic data under constraints where the constraints can be expressed in terms of different parameter sets. Obviously, the constraints and the generated data must remain the same over each parameter set. Otherwise, the parameters and/or the generated data would be inconsistent. We consider how to avoid or detect and then correct such inconsistencies under three proposed classifications: (1) data versus characteristic parameters, (2) macro- versus microconstraint scopes, and (3) intra- versus intervariable relationships. We propose several strategies and a heuristic for generating consistent stochastic data. Experimental results show that these strategies and heuristic generate more consistent data than the traditional discard-and-replace methods. Since generating stochastic data under constraints is a very common practice in many areas, the proposed strategies may have wide-ranging applicability.