Paper: arXiv 2110.07138

Abstract

We discuss how to build ETF risk models. Our approach anchors on i) first building a multilevel (non-)binary classification/taxonomy for ETFs, which is utilized in order to define the risk factors, and ii) then building the risk models based on these risk factors by utilizing the heterotic risk model construction of https://ssrn.com/abstract=2600798 (for binary classifications) or general risk model construction of https://ssrn.com/abstract=2722093 (for non-binary classifications). We discuss how to build an ETF taxonomy using ETF constituent data. A multilevel ETF taxonomy can also be constructed by appropriately augmenting and expanding well-built and granular third-party single-level ETF groupings.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces advanced mathematical frameworks like heterotic risk models and discusses factor covariance matrix construction with detailed formulas, but lacks concrete backtesting results, performance metrics, or implementation-specific data.

Research Flowchart

  flowchart TD
  A["Research Goal: Build ETF Risk Models"] --> B{"Select Classification Type"}
  B -->|Binary| C1["Heterotic Risk Model"]
  B -->|Non-Binary| C2["General Risk Model"]
  
  D["ETF Constituent Data"] --> E["Construct Multilevel ETF Taxonomy"]
  E --> B
  
  C1 --> F["Risk Factor Definition"]
  C2 --> F
  
  F --> G["Compute ETF Risk Models"]
  G --> H["Key Findings: Improved Risk Measurement & Taxonomy Framework"]