Systematic and multifactor risk models revisited
arXiv:1312.5271v12 citations
Originality Synthesis-oriented
AI Analysis
This work addresses risk modeling for finance or related fields, but it is incremental as it adapts existing methods to a known problem.
The paper tackles the problem of systematic and multifactor risk models by applying existing methods from signal processing and automatic control, resulting in successful computer experiments that bypass usual criticisms of such models.
Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.