
on Financial Markets 
By:  Michael S. Gibson 
Abstract:  The striking growth of credit derivatives suggests that market participants find them to be useful tools for risk management. I illustrate the value of credit derivatives with three examples. A commercial bank can use credit derivatives to manage the risk of its loan portfolio. An investment bank can use credit derivatives to manage the risks it incurs when underwriting securities. An investor, such as an insurance company, asset manager, or hedge fund, can use credit derivatives to align its credit risk exposure with its desired credit risk profile.> However, credit derivatives pose risk management challenges of their own. I discuss five of these challenges. Credit derivatives can transform credit risk in intricate ways that may not be easy to understand. They can create counterparty credit risk that itself must be managed. Complex credit derivatives rely on complex models, leading to model risk. Credit rating agencies interpret this complexity for investors, but their ratings can be misunderstood, creating rating agency risk. The settlement of a credit derivative contract following a default can have its own complications, creating settlement risk. For the credit derivatives market to continue its rapid growth, market participants must meet these risk management challenges. 
Date:  2007 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:200747&r=fmk 
By:  Henkel, Joachim 
Abstract:  The concept of risk is central to strategy research and practice. Yet, the expected positive association between risk and return, familiar from financial markets, is elusive. Measuring risk as the variance of a series of accountingbased returns, Bowman obtained the puzzling result of a negative association between risk and mean return. This finding, known as the Bowman paradox, has spawned a remarkable number of publications, and various explanations have been suggested. The present paper contributes to this literature by showing that skewness of individual firms’ return distributions has a considerable spurious effect on the meanvariance relationship. I devise a method to disentangle true and spurious effects, illustrate it using simulations, and apply it to empirical data. It turns out that the size of the spurious effect is such that, on average, it explains the larger part of the observed negative relationship. My results might thus help to reconcile meanvariance approaches to riskreturn analysis with other, exante, approaches. In concluding, I show that the analysis of skewness is linked to all three streams of literature devoted to explaining the Bowman paradox. 
Keywords:  meanvariance; risk; riskreturn paradox; skewness 
JEL:  C81 G39 M29 
Date:  2007–10 
URL:  http://d.repec.org/n?u=RePEc:cpr:ceprdp:6538&r=fmk 