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03/08/2012

Recent Research: Highlights from March 2012


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"Topics in Applied Investment Management: From a Bayesian Viewpoint"
The Journal of Investing (Spring 2012)
Harry M. Markowitz

When John Guerard, the special editor for this issue, was assembling the articles to be published, he asked Harry Markowitz to write the introduction. By the author’s own words, once he had completed that task he could see that his remarks were more like discussant comments than an introduction and could equally well be read after reading the articles.

"Correlation Smile, Volatility Skew, and Systematic Risk Sensitivity of Tranches"
The Journal of Derivatives (Spring 2012)
Alfred Hamerle, Andreas Igl, and Kilian Plank

In collateralized debt obligations and other securitized credit derivatives, the expected tranche payoffs depend heavily on the default correlations among the securities in the pool. Like volatility, correlation is not directly observable, but it is possible to infer it from the market price of a tranche. But unfortunately, also like volatility, these implied correlations don’t behave well. They should be equal across all of the tranches created from a given pool, but they never are. Instead, implied correlations exhibit the pattern known as correlation skew. In this article, Hamerle, Igl, and Plank consider two ways in which the real world departs from the assumptions of the Gaussian copula model. Different correlations extracted from different tranches is one, but the other departure is that investors require expected risk premia for bearing a security’s downside exposure. This is not the same as a (symmetrical) distaste for “volatility,” say, and it is reflected in an asymmetrical implied volatility skew exhibited by options on a bond issuer's equity. The standard Gaussian copula model allows for the first effect, but not the second. In this article, the authors look at both. Their most successful model estimates downside risk premia from the risk-neutral probability densities extracted from the issuers’ equity options and then imposes a fixed and moderate degree of correlation. This combination captures market pricing very well for all of the tranches above the equity tranche.

"The “Have Nots” and “Have Lots” in the US Venture Capital Industry"
The Journal of Private Equity (Spring 2012)
K. Vaidyanathan

The Dodd–Frank Act has made it mandatory for all US venture capital funds (VCs) with assets under management (AuM) exceeding $150 million to register with the US Securities and Exchange Commission (SEC). We ask what the optimal threshold size for registration is and find that the distribution of capital managed by VCs in the US closely follows a Pareto distribution. We try to explain why. We demonstrate theoretically that this can happen only if the shape of the VCs’ return distribution is indistinguishable. We prove that heterogeneous return distributions among VCs would lead to a non-Pareto distribution. We do a cost-benefit analysis of regulations and arrive at an optimal threshold of $250 million, which means that a regulator could oversee 79% fewer VCs. The VCs with AuM between $150 million and $250 million are only 17% of total industry AuM. Supervising too large a number of VCs may spread regulatory resources too thin and dilute the effectiveness of supervision. Keeping the size threshold at $250 million would allow a regulator to optimize monitoring time and effort.

"The Rich Get Richer and So Can You: Investing in a Billionaires’ Index"
The Journal of Index Investing (Spring 2012)
Joel M. Shulman and Erik Noyes

Are the rich getting richer? Our data suggest so. Rather than complaining about the unfairness of it all, this article offers a simple approach for the average investor to get richer, too. An index of publicly traded stocks controlled, created, or managed by the world’s wealthiest individuals, known as the Billionaires’ Index, tracks the annual list compiled by Forbes magazine. It doesn’t work every year, but on average, it seems to provide excellent results. Will the new Index work in the future for you?


 

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