"Contingent-Capital Solutions for Mitigating the Investment Risks of a Private Placement Stock." The Journal of Investing (Fall 2011). C.B. Garcia.
The final negotiations between angel investors and a company following a thorough due diligence usually involve the following impasse: The angel investors on one side and the company on the other are in disagreement regarding the company's valuation because the company's expectations are more optimistic than the investors'. There is no feasible solution. In this article, the author proposes a model that a mediator may use to help the two parties come to a settlement. For the model, we show why traditional instruments of equity or notes are oftentimes infeasible and how feasible solutions—solutions that are currently not considered in negotiations—may be uncovered. With this model, a mediator may at least get one side to understand the implication of the terms to his or her future ownership of the firm, the (in)feasibility of the offering, and more importantly, where the other side is coming from. Better yet, with the model, the mediator may be able to persuade the contending parties to jointly consider alternate feasible solutions, to add their own constraints, or to move their expectations (which are functions of their beliefs about capital needs, risk profiles, intellectual property, etc.) closer to each other in order to get to a quicker settlement.
"The Performance of Johnson Distributions for Computing Value at Risk and Expected Shortfall." The Journal of Derivatives (Fall 2011). Jean-Guy Simonato.
Option pricing plainly depends on the probability distribution of the underlying asset return, at least the portion of the distribution for which the option is in the money. Risk management also depends on the return distribution, but standard risk measures like value at risk (VaR) and expected shortfall (ES) concentrate only on its tails. While the lognormal may be (arguably) adequate for modeling option values, empirically, "risk" is inherently tied up with fat-tailed return processes. This result has led to interest in methods to approximate an unknown distribution by matching its first four moments. The Cornish–Fisher and Gram–Charlier expansions are frequent choices. Simonato argues, however, that these techniques do not actually work very well because the range of densities for which the approximations are valid is quite limited. For example, the density approximation may have negative portions, even when skewness and kurtosis seem quite reasonable. He proposes adopting the Johnson family of densities instead, which also uses four parameters to match the first four moments of an empirical distribution. A simulation study shows that with Johnson distributions, tail fitting is accurate and available over the full range of parameter values.
"How Do Private Equity Investors Create Value? A Summary of Findings from Ernst & Young's Extensive Research in North America over the Past Four Years." The Journal of Private Equity (Fall 2011). John Vester.
For the past four years, Ernst & Young has conducted in-depth annual surveys in North America on the largest private equity (PE) investment exits, to enable disciplined quantitative analyses for the purpose of identifying the major drivers of investment return. The results have been published and presented regularly in various forums, and each edition of the annual E&Y PE Value Creation Study (as it has become known) delves into new areas of investigation to keep the current findings innovative and fresh. This article presents for the first time a coherent and cumulative summary of the major findings from all of the editions of this substantive PE exit research and analyses over exit years from 2006 to 2009 for the largest PE exits in North America.
"The FTSE StableRisk Indices." The Journal of Index Investing (Fall 2011). Jeremiah H. Chafkin, Andrew W. Lo, and Robert W. Sinnott.
Implicit in most asset-allocation policies is the statistical assumption of "stationarity," which means that the means, variances, and covariances of asset returns are assumed to be constant over time. This assumption is a reasonable approximation during normal market conditions but fails dramatically during periods of market turmoil and dislocation. In such periods, market volatility is highly dynamic, correlations can jump to 100% in a matter of days, and risk premia can become negative for months at a time. FTSE and AlphaSimplex Group have developed a family of rule-driven (passive), transparent, and high-capacity indices whose volatilities are rescaled as often as daily with the goal of maintaining more stable risk levels. By stabilizing the risk of each asset class over time, the FTSE StableRisk Indices have the potential to capture the long-term risk premia of asset classes and simple strategies with less severe maximum drawdowns than those of traditional indices, which have no risk controls.