We find that liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4,000 corporate bonds and spanning both investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads, and an improvement in liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond-specific, firm-specific, and macroeconomic variables, and are robust to issuers' fixed effect and potential endogeneity bias. Our findings justify the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants.A NUMBER OF RECENT STUDIES (Collin-Dufresne, Goldstein, and Martin (2001) and Huang and Huang (2003)) indicate that neither levels nor changes in the yield spread of corporate bonds over Treasury bonds can be fully explained by credit risk determinants proposed by structural form models. Longstaff, Mithal, and Neis (2005) suggest that illiquidity may be a possible explanation for the failure of these models to more properly capture the yield spread variation. Yet much of the current literature abstracts from liquidity's inf luence (Elton et al. (2001), focuses on aggregate liquidity proxies (Grinblatt (1995), Duffie and Singleton (1997), Collin-Dufresne et al. (2001), andTaksler (2003) or assumes that simply the unexplained portion of the yield spread is liquidity based (Duffee (1999)). This paper comprehensively assesses bond-specific liquidity for a broad spectrum of corporate investment grade and speculative grade bonds and examines the association between bond-specific liquidity estimates and corporate bond yield spreads.

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In this paper we identify conditions under which a true generator does or does not exist for an empirically observed Markov transition matrix. We show how to search for valid generators and choose the "correct" one that is the most compatible with bond rating behaviors. We also show how to obtain an approximate generator when a true generator does not exist. We give illustrations using credit rating transition matrices published by Moody's and by Standard and Poor's.

This paper has two objectives: (1) to propose and implement a valuation framework for temperature derivatives (a specific class of weather derivatives); and (2) to study the significance of the market price of weather risk. The objectives are accomplished by generalizing the Lucas model of 1978 to include the weather as another fundamental source of uncertainty in the economy. Daily temperature is modeled by incorporating such key properties as seasonal cycles and uneven variations throughout the year. The temperature variable is related to the aggregate dividend or output through both contemporaneous and lagged correlations, as corroboratedThe previous version of this paper was titled "Equilibrium Valuation of Weather Derivatives." We are grateful to the Social Sciences and Humanities Research Council of Canada for financial support.