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1. Arturo Cifuentes 2. Bernardo Pagnoncelli 1. To order reprints of this article, please contact David Rowe at d.rowe{at}pageantmedia.com or 646-891-2157. Molecular biology is undergoing a major shakeup. The cell-as-machine concept, the foundation on which the discipline has built

This article describes an analytical approach to examine the credit-risk behavior of a homogeneous portfolio. The authors demonstrate the usefulness of the approach using a synthetic index linked to high-yield corporate bonds (which resembles a synthetic CDO) and then analyze an actual synthetic CDO transaction. They show that the conventional approach to analyze these structures (Monte Carlo simulations combined with the Gaussian copula) fails to account for the tri-modal nature of the underlying portfolio default distribution, and consequently, risk assessments based on this method give a misguided view of the risk–reward profile of such portfolios. The authors further show that the benefits of portfolio diversification in the context of credit-risk portfolios are limited in high-correlation scenarios. These findings have important implications for risk managers and financial regulators.

Securitization has been a significant breakthrough in our ability to manage financial risk. In the same way that a futures contract permits exposure to price risk to be separated from ownership of a risky asset, securitization allows the separation of many types of risk exposure and other important characteristics from ownership of the securities in which they originate. Credit derivatives, in particular, can eliminate nearly all exposure to default risk for most investors in a pool of credit-risky bonds, even if the credit quality of the underlying securities is not strong. But the financial crisis of 2008 exposed several important facts: These instruments are complicated, their true risk characteristics had not been fully appreciated, and few people really understood them—apparently including some of those who were in the business of buying, selling, and creating them. This article is a basic primer on securitization of credit risk and the derivative securities that are created in the process. It provides an accessible overview that will be useful for teaching undergraduates about securitization of credit risk and as a general introduction to the subject for the non-technically-oriented reader.

This article introduces a new financial metric for the art market. The metric, which the authors call artistic power value (APV), is based on the price per unit of area (dollars per square centimeter) and is applicable to two-dimensional art objects such as paintings. In addition to its intuitive appeal and ease of computation, this metric has several advantages from the investor’s viewpoint. It makes it easy to 1) estimate price ranges for different artists; 2) perform comparisons among them; 3) follow the evolution of the artists’ creativity cycle over time; and 4) compare, for a single artist, paintings with different subjects or different geometric properties. Additionally, the APV metric facilitates the process of estimating total returns. Finally, due to its transparency, the APV can be used to design derivatives-like instruments that can appeal to both investors and speculators. Several examples validate this metric and demonstrate its usefulness.

The one-factor Gaussian copula method has become the de facto standard to analyze most synthetic collateralized debt obligation structures. Unfortunately, this method produces a peculiar phenomenon known as a correlation smile (the implied correlation determined by the model depends on the CDO tranche one is considering instead of being tranche-independent). Market participants are divided regarding this issue. Many suspect that the correlation smile is caused by a flaw in the above-mentioned modeling strategy although they have been unable to articulate why. Others insist that the smile is actually correct and reveals important and relevant tranche-dependent characteristics, but have failed to produce convincing evidence to support this view. In this article the authors present evidence that the correlation smile is really a by-product (artifact) of an unfortunate modeling strategy and has no financial or market-driven interpretation whatsoever. Moreover, the authors argue that this modeling approach should be abandoned at once.