Using a novel dataset of leveraged loan trades executed by managers of collateralized loan obligations (CLOs), we document the importance of “active loan trades” – trades executed at a manager’s discretion. More active trading increases the returns to CLO equity investors, lowers collateral portfolio default rates, and increases the manager’s chances of closing a new deal. Examining the observed loan trades, we find that more active CLOs trade at better prices than less active CLOs, selling leveraged loans earlier and before they get downgraded. Our findings suggest that more active CLOs are better at anticipating deteriorations in loan credit quality.

With the recent significant growth in the single-name credit default swap (CDS) market has come the need for accurate and computationally efficient models to value these instruments. While the model developed by Duffie, Pan, and Singleton (2000) can be used, the solution is numerical (solving a series of ordinary differential equations) rather than explicit. In this paper, we provide an explicit solution to the valuation of a credit default swap when the interest rate and the hazard rate are correlated by using the “change of measure” approach and solving a bivariate Riccati equation. CDS transaction data for the period 2/15/2000 through 4/8/2003 for 60 firms are used to test both the goodness of fit of the model and provide estimates of the influence of economic variables in the market for credit-risky bonds.

In this paper, we introduce a new GARCH model with an infinitely divisible distributed innovation. This model, which we refer to as the rapidly decreasing tempered stable (RDTS) GARCH model, takes into account empirical facts that have been observed for stock and index returns, such as volatility clustering, non-zero skewness, and excess kurtosis for the residual distribution. We review the classical tempered stable (CTS) GARCH model, which has similar statistical properties. By considering a proper density transformation between infinitely divisible random variables, we can find the risk-neutral price process, thereby allowing application to option-pricing. We propose algorithms to generate scenarios based on GARCH models with CTS and RDTS innovations. To investigate the performance of these GARCH models, we report parameter estimates for the Dow Jones Industrial Average index and stocks included in this index. To demonstrate the advantages of the proposed model, we calculate option prices based on the index.

Robust portfolio optimization has been developed to resolve the high sensitivity to inputs of the Markowitz mean–variance model. Although much effort has been put into forming robust portfolios, there have not been many attempts to analyze the characteristics of portfolios formed from robust optimization. We investigate the behavior of robust portfolios by analytically describing how robustness leads to higher dependency on factor movements. Focusing on the robust formulation with an ellipsoidal uncertainty set for expected returns, we show that as the robustness of a portfolio increases, its optimal weights approach the portfolio with variance that is maximally explained by factors.

The sensitivity of a risk measure with respect to the parameters of the hypothesized distribution is a useful tool in investigating the impact of marginal rebalancing decisions on the portfolio return distribution and also in the analysis of the asymptotic variability of the risk estimator. We calculate the relative importance of the conditional value-at-risk (CVaR) sensitivity with respect to tail thickness and scale of the portfolio return distribution in the case of regularly varying tails and in the case of exponential and faster-than-exponential decay. We discuss the implications for asset return modeling and the asymptotic variability of the risk estimator.

In this paper, we analyze momentum strategies that are based on reward–risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. Reward–risk stock selection criteria include the standard Sharpe ratio with variance as a risk measure, and alternative reward–risk ratios with the expected shortfall as a risk measure. We investigate momentum strategies using 517 stocks in the S&P 500 universe in the period 1996–2003. Although the cumulative return criterion provides the highest average monthly momentum profits of 1.3% compared to the monthly profit of 0.86% for the best alternative criterion, the alternative ratios provide better risk-adjusted returns measured on an independent risk-adjusted performance measure. We also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non-normal stable Paretian distributions. Specifically, for every stock selection criterion, loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles. The lower tail index is associated with a lower mean strategy. The lowest tail index is obtained for the cumulative return strategy. Given our data-set, these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk, while strategies based on reward–risk criteria obtain better risk-adjusted performance with the acceptance of the lower tail risk.

Offering a comprehensive exploration of the revolutionary developments occuring in the world's financial markets and institutions, this edition looks at innovation, globalization and deregulation - with a focus on the actual practices of financial institutions, investors and financial instruments.

This paper presents an empirical investigation of the security selection and market timing performance of a random sample of 71 U.S. equity pension fund managers using monthly returns for the period 1983-1990.The 71 equity fund managers include banks, insurance companies and investment advisors who have been allocated funds by pension plan sponsors.The data were provided by the Frank Russell Company of Tacoma, WA.While there have been many studies of U.S. equity mutual funds, ours is the first such study of which we are aware of U.S. equity pension fund managers.The estimates of selectivity and timing were derived using the Treynor and Mazuy (1966) model and the Bhattacharya and Pfleiderer (1983) model.The total sample of managers is subdivided into four groups by investment style, and a benchmark portfolio is identified for each style.We also included two benchmarks for the broad equity market.Regardless of the choice of a benchmark portfolio or estimation model, the selectivity measure is positive on average and the timing measure is negative on average.However, both selectivity and timing do appear to be somewhat more sensitive to the choice of a benchmark portfolio (and, possibly, the time period) when managers are classified by investment style.A metaanalysis was performed to quantify the effect of sampling error on the cumulated regression results.In every case, meta-analysis revealed some real variation (in excess of that attributable to sampling error) around the mean values for both selectivity and timing.An examination of the 80% probability intervals for selectivity revealed that the best managers can deliver substantial risk-adjusted excess returns.Finally, consistent with previous studies of equity mutual fund performance, we also found a negative correlation between selectivity and timing.However, we argue that the observed negative correlation in our data is largely an artifact of negatively correlated sampling errors for the two estimates.The Investment Performance of U.S. Equit>' Pension Fund Managers: An Empirical InvestigationEach year Pensions & Investments , a leading trade newspaper for the pension management industry, profiles the top 1000 pubUc and private U.S. pension funds.At year-«Kl 1990, these funds had total pension assets of $1,876 trillion.Approximately $750 billion (40 percent) was invested in equities.The Investment Company Institute estimates that $250 billion was invested in open-and closed-end equity-oriented U.S. mutual funds at year-end 1990.This snapshot indicates a 3:1 ratio for p«ision fund equity investment versus mutual fund equity investment.Not only is the dollar difference large, but also the difference in the number of managers in each universe is large.The total number of pension fund managers is much larger than the number of mutual fund managers, by a ratio of approximately 10:1.Yet surprisingly little research has been done on the investment performance of U.S. equity pension fund managers.This paper begins to fill an important gap in the literature by providing empirical evidence on the investment performance of these managers.The focus of this study is on equity pension fund managers who have been allocated funds by a pension plan sponsor.Brinson, Hood and Beebower (1986), Ippolito and Turner (1987), and Berkov-tiz, Finney and Logue (1988) examined the investment performance of a sample of large U.S. pension plans.Each plan may be composed of many fund managen in different asset categories with their own specific investment objectives and styles.In a recent study containing a wealth of informatioQ about the pension management industry, Lakonishok, Shleifer and Vishny (1992) examined the annual returns of a sample of equity pension funds over

This paper presents an empirical examination of the selectivity and market timing performance of a sample of U.S. equity pension fund managers. Regardless of the choice of benchmark portfolio or estimation model, the average selectivity measure is positive and the average timing measure is negative. However both selectivity and timing appear to be somewhat sensitive to the choice of a benchmark when managers are classified by investment style. Meta‐analysis revealed some real variation around the mean values for each measure. The 80 percent probability intervals for selectivity revealed that the best managers produced substantial risk‐adjusted excess returns. We also found a negative correlation between selectivity and timing, but we argue that the observed negative correlation in our data is largely an artifact of negatively correlated sampling errors for the two estimates.