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Common Idiosyncratic Quantile Factors and Asset Prices

Jozef Barunik, Matej Nevrla

Abstract

We investigate whether the tails of firm-level idiosyncratic return distributions are driven by common shocks. We use quantile factor analysis to extract such common idiosyncratic quantile factors with asymmetric pricing effects and we find a significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year. This premium remains significant even when controlling for standard factors, idiosyncratic volatility and tail-risk measures. The downside factor strengthens when intermediary capital is weak and market liquidity is low, and it predicts aggregate market excess returns.

Common Idiosyncratic Quantile Factors and Asset Prices

Abstract

We investigate whether the tails of firm-level idiosyncratic return distributions are driven by common shocks. We use quantile factor analysis to extract such common idiosyncratic quantile factors with asymmetric pricing effects and we find a significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year. This premium remains significant even when controlling for standard factors, idiosyncratic volatility and tail-risk measures. The downside factor strengthens when intermediary capital is weak and market liquidity is low, and it predicts aggregate market excess returns.
Paper Structure (45 sections, 4 theorems, 57 equations, 4 figures, 22 tables)

This paper contains 45 sections, 4 theorems, 57 equations, 4 figures, 22 tables.

Table of Contents

  1. Introduction
  2. Common Idiosyncratic Quantile (CIQ) Factors: Asymmetric Idiosyncratic Risk
  3. Data and Descriptive Statistics
  4. Economic Mechanism: Intermediary Risk-Bearing Capacity and Common Idiosyncratic Tail Risk
  5. A Reduced-Form Representation.
  6. Intermediary Constraints and the Pricing Kernel
  7. CIQ Risks in the Economy
  8. Pricing the CIQ($\tau$) Risks in the Cross-Section
  9. Univariate Portfolio Sorts
  10. Bivariate Portfolio Sorts
  11. Fama-MacBeth Regressions
  12. Robustness Checks
  13. CIQ Premium across Distribution
  14. Stability Checks
  15. Beyond $\Delta$CIQ Betas
  16. ...and 30 more sections

Key Result

Proposition 1

Under standard large-$N$ conditions for approximate factor models, the lower-tail CIQ innovation at $\tau_L$ consistently estimates an affine transformation of $\Delta s_t$, with $\kappa\neq 0$ and an error term $\nu_t$.

Figures (4)

  • Figure 1: CIQ Factors
  • Figure 2: Performance of the $\Delta$CIQ Portfolios
  • Figure 3: CIQ premia
  • Figure 4: Cross-Sectional Dispersion of Stock Returns

Theorems & Definitions (4)

  • Proposition 1: Quantile factor representation
  • Proposition 2: Quantile factor representation
  • Proposition 3: CIQ beta as fragility exposure
  • Proposition 4: Left-tail asymmetry