The price of defaultable or credit-risky bonds differs from the equivalent maturity price of a risk-free bond for a well identified number of factors: the positive probability of default prior to the bond maturity, the estimated loss given default, that depends on the adopted assumption on the recovery rate for that class, see Duffie and Singleton [Duffie, D., Singleton, K.J., 1999. Modeling term structures of defaultable bonds. Rev. Financ. Stud. 12, 687–720] for several models of recovery rates, the probability that the bond issuer will migrate from the current rating class to a lower class. In this study we apply two well-known modelling approaches, due to Jarrow et al. [Jarrow, R.A., Lando, D., Turnbull, S.M., 1997. A Markov model for the term structure of credit risk spreads. Rev. Financ. Stud. 10, 481–523] and Schönbucher [Schönbucher, P.J., 2002. A tree implementation of a credit spread model for credit. J. Comput. Finance 6 (2), 175–196] to price specifically two risk sources affecting the evolution of bond prices over time: the risk to move from a current risk class to a different one over the bond residual life, and the risk associated with comovements of the credit spread curves and the risk-free term structure. The former is referred to as transition risk, the latter as correlation risk. The analysis is conducted extending appropriately to a multinomial setting the classical discrete binomial model of the term structure formulated by Black et al. [Black, F., Derman, E., Toy, W., 1990. A one-factor model of interest rates and its application to treasury bond options. Financ. Analysts J. (January/February), 33–39], applied previously by Abaffy et al. [Abaffy, J., Bertocchi, M., Dupačová, J., Moriggia, V., 2000. On generating scenarios for bond portfolios. Bull. Czech Econom. Soc. 11, 3–27, and references ibidem] and many other authors in literature. The generalised model, with transition [by Jarrow et al.] and correlation risk [by Schönbucher], is applied to a large dataset of corporate spreads to evaluate the sensitivity of the isolated risk sources on the fair price of risky bonds traded in the Eurobond market.

An update of a classic book in the field, Modern Portfolio Theory examines the characteristics and analysis of individual securities as well as the theory and practice of optimally combining securities into portfolios. It stresses the economic intuition behind the subject matter while presenting advanced concepts of investment analysis and portfolio management. Readers will also discover the strengths and weaknesses of modern portfolio theory as well as the latest breakthroughs.

Under Medicare Part D, senior citizens choose prescription drug insurance offered by numerous private insurers. We examine nonpoor enrollees' actions in 2006 and 2007 using panel data. Our sample reduced overspending by $298 on average, with gains by 81 percent of them. The greatest improvements were by those who overspent most in 2006 and by those who switched plans. Decisions to switch depended on individuals' overspending in 2006 and on individual-specific effects of changes in their current plans. The oldest consumers and those initiating medications for Alzheimer's disease improved by more than average, suggesting that real-world institutions help overcome cognitive limitations.

We propose regression‐based tests for mean‐variance spanning in the case where investors face market frictions such as short sales constraints and transaction costs. We test whether U.S. investors can extend their efficient set by investing in emerging markets when accounting for such frictions. For the period after the major liberalizations in the emerging markets, we find strong evidence for diversification benefits when market frictions are excluded, but this evidence disappears when investors face short sales constraints or small transaction costs. Although simulations suggest that there is a possible small‐sample bias, this bias appears to be too small to affect our conclusions.

Most bank merger studies do not control for hidden bailouts, which may lead to biased results. In this study we employ a unique data set of approximately 1000 mergers to analyze the determinants of bank mergers. We use undisclosed information on banks’ regulatory intervention history to distinguish between distressed and non-distressed mergers. Among merging banks, we find that improving financial profiles lower the likelihood of distressed mergers more than the likelihood of non-distressed mergers. The likelihood to acquire a bank is also reduced but less than the probability to be acquired. Both distressed and non-distressed mergers have worse CAMEL profiles than non-merging banks. Hence, non-distressed mergers may be motivated by the desire to forestall serious future financial distress and prevent regulatory intervention.