Frank J. Fabozzi, Thom B. Thurston, State Taxes and Reserve Requirements as Major Determinants of Yield Spreads Among Money Market Instruments, The Journal of Financial and Quantitative Analysis, Vol. 21, No. 4 (Dec., 1986), pp. 427-436

Frank J. Fabozzi, Richard R. West, Negotiated Versus Competitive Underwritings of Public Utility Bonds: Just One More Time, The Journal of Financial and Quantitative Analysis, Vol. 16, No. 3 (Sep., 1981), pp. 323-339

Since Markowitz [15, pp. 98–101] and Sharpe [19] developed the single-index market model (SIMM hereafter) it has received considerable research attention. Empirical tests have established the model's econometric significance [3, 12, 13] in partial equilibrim analysis. However, research into the relationship between the SIMM and its macroeconomic environment has been meager. It has been shown that the market factor changes intertemporally [13, 16, 18, 20]. However, whether these changes in the market factor and, more basically, changes in the macroeconomic situation affect the SIMM is unknown.

After Markowitz [14, p. 100] and Sharpe [19, 20] suggested estimating the beta systematic risk coefficient for market assets, finance professors, stock brokers, investment managers, and others began expending large quantities of resources each year on estimating betas. Unfortunately however, it appears that the ordinary least-squares (OLS) regressions used in nearly every instance may be inappropriate. This paper suggests that many stocks' beta coefficients move randomly through time rather than remain stable as the OLS model presumes.

Based on the theory of the pricing of capital assets developed by Sharpe [12], Lintner [9] and Mossin [11], Professor Jensen formulated a return-generating model to measure portfolio performance [5]. In a subsequent paper, Professor Jensen [6] investigated the impact of the investment horizon on the functional form of the model. Lee [8] has proposed a generalized specification of the model to resolve this problem. Alternative estimation methods for testing the linearity of the model in terms of time-series data have also been suggested by Lee. Moreover, the stability of the beta coefficient over time and the impact of the market's condition on both the alpha (or, Jensen's measure of performance [5]) and beta of the model have come under scrutiny in financial research.

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.