Research

Publications

• Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices
  • Co-Authors: Jozef Barunik
  • Journal: Journal of Financial Econometrics
  • Year: 2022, forthcoming
  • Link: Paper Link
  • Code: R package
  • Award: 1st place in the Competition for the Best Student Paper in Theoretical Economics, The Czech Econometric Society
  • Abstract: This article investigates how two important sources of risk—market tail risk (TR) and extreme market volatility risk—are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral (QS) beta representation of risk based on the decomposition of covariance between indicator functions that capture fluctuations over various frequencies. We study the asymptotic behavior of the proposed estimators of such risk. Empirically, we find that TR is a short-term phenomenon, whereas extreme volatility risk is priced by investors in the long term when pricing a cross-section of individual stocks. In addition, we study popular industry, size and value, profit, investment, or book-to-market portfolios, as well as portfolios constructed from various asset classes, portfolios sorted on cash flow duration, and other strategies. These results reveal that tail-dependent and horizon-specific risks are priced heterogeneously across datasets and are important sources of risk for investors.

Working Papers

• Common Idiosyncratic Quantile Risk
  • Co-Authors: Jozef Barunik
  • Status: Revise & Resubmit, Review of Finance
  • Link: Paper Link
  • Abstract: We identify a new type of risk that is characterised by commonalities in the quantiles of the cross-sectional distribution of asset returns. Our newly proposed quantile risk factor is associated with a quantile-specific risk premium and provides new insights into how upside and downside risks are priced by investors. In contrast to the previous literature, we recover the common structure in cross-sectional quantiles without making confounding assumptions or aggregating potentially non-linear information. We discuss how the new quantile-based risk factor differs from popular volatility and downside risk factors, and we identify where the quantile-dependent risks deserve greater compensation. Quantile factors also have predictive power for aggregate market returns.
• Asymmetric Risks: Alphas or Betas?
  • Co-Authors: Solo-authored
  • Status: Early draft
  • Link: Paper Link
  • Abstract: I show that systematic asymmetric risk measures, such as coskewness or tail risk beta, can complement each other when implementing an investment strategy based on them. I propose a simple approach to combining these measures and obtaining anomalous returns above the premiums associated with each measure separately. I show that various multivariate regression setups that combine the asymmetric risk measures per- form poorly. Instead, I use instrumented principal component analysis and construct portfolios that are neutral with respect to the common sources of risk associated with these measures. The resulting portfolios enjoy abnormal returns that no other factor model can fully explain, although there is a clear relation between asymmetric risk measures and the momentum factor. I also show that some measures can contribute significantly to the performance of a model with a linear factor structure.