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07/20/2011

Recent Research: Highlights from July 2011


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The Role of Speculators During Times of Financial Distress.” The Journal of Alternative Investments (Summer 2011). Naomi E. Boyd, Jeffrey H. Harris, and Arkadiusz Nowak.

One of the best-known and largest hedge fund failures was the 2006 failure of Amaranth Advisors, LLC. The authors use detailed, trader-level data to examine the role of speculators during times of financial distress—in this case, the failure of Amaranth. They find that speculators served as a stabilizing force during the period by maintaining or increasing long positions, even while prices fell. The authors develop two testable propositions regarding liquidation versus transfer of positions and conclude that the probability of transfer was more likely for distant contract expirations and for contracts more dominantly held by the distressed trader. The article also examines the role of speculators in providing liquidity and mitigating the effects of liquidity risk by evaluating the change in the number of traders, the size and time between trades, and a Herfindahl measure of speculative trader concentration during the crisis period.

Algorithmic Trading Usage Patterns and Their Costs.” The Journal of Trading (Summer 2011). Ian Domowitz and Henry Yegerman.

Using algorithmic trading data across seven strategy types over 2009 and 2010, we examine usage patterns and performance for a sample of buy-side firms served by a multiplicity of brokers. Strategy usage is categorized by demand for liquidity, volatility, and concentration of orders traded. The data suggest employment of dominant strategies for the majority of firms, and shifts in strategy use are marginal across time and market conditions. In terms of performance, dominant strategies constitute a sensible approach at two ends of a spectrum: for easy orders and for situations that are extremely demanding in terms of liquidity and volatility. Performance matters, but does not distinguish individual strategy types in either regime. In all other circumstances, strategy shifts are possible and potentially profitable, given performance differences.

Modeling Ultimate Loss Given Default on Corporate Debt.” The Journal of Fixed Income (Summer 2011). Michael Jacobs, Jr., and Ahmet K. Karagozoglu.

Loss given default (LGD) is a critical parameter in various facets of credit risk modeling. This study empirically investigates the determinants of LGD and builds alternative predictive econometric models for LGD on bonds and loans using an extensive sample of most major U.S. defaults in the 1985–2008 period. The authors build simultaneous equation models in the beta-link generalized linear model (BLGLM) class, identifying several that perform well in terms of the quality of estimated parameters as well as overall model performance metrics. This extends prior work by modeling LGD both at the firm and the instrument levels. In a departure from the extant literature, the authors find the economic and statistical significance of firm-specific debt and equity market variables. In particular, they find that information from either the equity or the debt markets at around the time of default (measures of either cumulative equity returns or distressed debt prices, respectively) have predictive power with respect to the ultimate LGD, which is in line with recent prior recovery and asset pricing research. They also document a new finding: Larger firms have significantly lower LGDs while larger loans have higher LGDs.


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