Joshua Traut
Thumbnail Image
Last Name
Traut
First name
Joshua
Email
joshua.traut@unisg.ch
ORCID
Phone
+41 71 224 70 36
Homepage

Filters

3 2
2 1
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    What we know about the low-risk anomaly: a literature review
    ( 2023-04-28)
    Joshua Traut orcid-logo
    It is well documented that less risky assets tend to outperform their riskier counterparts across asset classes. This paper provides a structured summary of the current state of literature regarding this so-called low-risk anomaly. It provides an overview of empirical findings across implementation methodologies and asset classes. Furthermore, it presents the most prevailing causes, which are namely exposure to other factors, coskewness risk, investor constraints, behavioral biases, and agency problems. The paper concludes that despite some critiques there are good reasons to believe that the low-risk anomaly can be evaluated as an investment factor. It also identifies that more research is required to disentangle the proposed causes to fully understand the big picture of the anomaly with certainty.
    Type: journal article
    Journal: Financial Markets and Portfolio Management
    Volume: 37
    Issue: 3
    DOI: 10.1007/s11408-023-00427-0
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118173
    Scopus© Citations 3
  • Publication
    Estimating Forward-Looking Stock Correlations from Risk Factors
    ( 2022-05-12)
    Wolfgang Schadner
    ;
    Joshua Traut orcid-logo
    This study provides fully mathematically and economically feasible solutions to estimating implied correlation matrices in equity markets. Factor analysis is combined with option data to receive ex ante beliefs for cross-sectional correlations. Necessary conditions for implied correlation matrices to be realistic, both in a mathematical and in an economical sense, are developed. An evaluation of existing models reveals that none can comply with the developed conditions consistently. This study overcomes this pitfall and provides two estimation models via exploiting the underlying factor structure of returns. The first solution reformulates the task into a constrained nearest correlation matrix problem. This method can be used either as a stand-alone instrument or as a repair tool to re-establish the feasibility of another model’s estimate. One of these properties is matrix invertibility, which is especially valuable for portfolio optimization tasks. The second solution transforms common risk factors into an implied correlation matrix. The solutions are evaluated upon empirical experiments of S&P 100 and S&P 500 data. They turn out to require modest computational power and comply with the developed constraints. Thus, they provide practitioners with a reliable method to estimate realistic implied correlation matrices.
    Type: journal article
    Journal: Mathematics
    Volume: 10
    Issue: 10
    DOI: 10.3390/math10101649
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/118171
  • Publication
    Which is Worse: Heavy Tails or Volatility Clusters?
    ( 2023-06-15)
    Joshua Traut orcid-logo
    ;
    Wolfgang Schadner
    Heavy tails and volatility clusters are both stylized facts of financial returns that destabilize markets. The former are extreme events by definition and the latter can accelerate adverse market developments. This work disentangles the two sources and examines which one does the greater damage to financial stability, whether the threat can be reduced via diversification, and how an acknowledgment of volatility clustering can enhance the quality of risk models. The analysis is carried out for index return series representing seven different asset classes and for individual stock portfolio return series. The isolation of the stylized facts is achieved under recent developments in surrogate analysis (IAAFT, IAAWT). While tail risk historically received more attention, especially in financial regulation, our analysis shows that volatility clusters have a greater impact on maximum drawdowns and aggregate losses across all return series. We further find that diversification does not yield any protection from those risks. These findings have important implications for financial regulators, risk managers, and investors seeking to understand and mitigate the risks of financial markets.
    Type: conference paper
    DOI: 10.2139/ssrn.4410908
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117505