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Recent Research: Highlights from October 2013

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"A Conditional Assessment of the Relationships Between Commodity and Equity Indexes"
The Journal of Alternative Investments (Fall 2013)
David P. Simon

This study models the conditional relationships between the Goldman Sachs Total Return Commodity Index and Sub-Indexes and the S&P 500 index from January 1991 through June 2011 within a bivariate GARCH framework that uses instruments to model time-varying conditional correlations. The results indicate the presence of important spillovers between the conditional means and volatilities of commodity and equity index returns. The findings also indicate that conditional correlations increase from roughly zero to about 0.4 during the sample period, consistent with an increased integration of commodity and equity markets. The results also indicate that conditional correlations rise when the conditional volatility of equity returns increases and when business cycle conditions deteriorate. The greater integration of these markets is also reflected in the increase of conditional betas from around zero to roughly 0.6 over the sample period. Overall, the results indicate that while the diversification benefits of commodities diminished over the sample period, the estimated conditional correlations remain low enough for commodities to provide meaningful diversification benefits to equity investors.

"Optimal Trading Algorithm Selection and Utilization: Traders’ Consensus versus Reality"
The Journal of Trading (Fall 2013)
Jingle Liu and Kapil Phadnis

We study the usage patterns of traders employing execution algorithms with the goal of statistically minimizing implementation shortfall. Based on widely accepted concepts, algorithms are categorized into scheduled, participation, dark, and dynamic types. We present the consensus patterns that emerge in the trading behavior and offer insight into how to improve performance. Our goal is to help improve implicit trade cost by studying the right context in which traders use algorithms. We use the mean, standard deviation, skewness, and kurtosis of the distribution of trade cost against arrival price to quantify expected cost and risk. We quantitatively characterize the relationship between trade cost and order size, participation rate, average daily volume, algorithm duration, limit price, and algorithm type. We find that for a given market condition and order requirement, selecting the right algorithm and tuning its parameters could significantly affect execution performance. Implementation shortfall algorithms exhibit superior performance for small orders, and dark algorithms perform better even in high-participation-rate scenarios. Scheduled algorithms are most sensitive to participation rate and order size, and dark algorithms are least sensitive. The use of limit price and lower participation rate in algorithms greatly reduces the unattractive positive skewness of trade cost distribution and thus improves performance. We also offer insight into setting optimal participation rate and limit prices.

"The Financial Crisis and Temporary Liquidity Guarantee Program: Their Impact on Fixed-Income Markets"
The Journal of Fixed Income (Fall 2013)
Brent W. Ambrose, Yiying Cheng, and Tao-Hsien Dolly King

The authors examine the Federal Deposit Insurance Corporation’s (FDIC’s) Temporary Liquidity Guarantee Program to determine the effectiveness of this government-sponsored solution for the recent banking crisis. They explore the impact of the program on the liquidity and credit crisis in the debt markets and the market value of the FDIC guarantee. They find a negative and significant two-day abnormal stock return of –1.53% around FDIC-backed debt issue dates, suggesting stockholders held pessimistic views on the future prospects of the banks. Bondholders reacted positively to the issuances, with a two-day excess return of 0.32%, confirming the positive liquidity and credit effects in the debt market. The authors also find a significant drop in yield spreads of AAA debt issues around the announcements of FDIC-guaranteed debt issues. Using multiple regressions, they confirm the drop in yield spreads of AAA financial bonds after the program started. Both findings suggest that the program effectively encouraged liquidity and confidence. Finally, FDIC-backed debt trades at a discount of 132 bps to nonguaranteed debt, implying that the guarantee is valuable.

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