Predicting credit spreads

Predictions of firm-level credit spreads based on the current spot and forward credit spreads can be significantly improved upon by using the information contained in the shape of the credit-spread curve. However, the current credit-spread curve is not a sufficient statistic for predicting future out-of-sample credit spreads; predictions can be significantly improved upon by exploiting the information contained in the shape of the riskless yield curve. In the presence of credit-spread and riskless factors, other macroeconomic, marketwide, and firm-specific risk variables do not significantly improve predictions of credit spreads. These results have important implications for credit-spreads modeling as well as for better understanding corporate capital structure and risk management policies.     

Corporate Political Contributions and Stock Returns

We develop a new and comprehensive database of firm‐level contributions to U.S. political campaigns from 1979 to 2004. We construct variables that measure the extent of firm support for candidates. We find that these measures are positively and significantly correlated with the cross‐section of future returns. The effect is strongest for firms that support a greater number of candidates that hold office in the same state that the firm is based. In addition, there are stronger effects for firms whose contributions are slanted toward House candidates and Democrats.     

Networking as a Barrier to Entry and the Competitive Supply of Venture Capital

We examine whether strong networks among incumbent venture capitalists (VCs) in local markets help restrict entry by outside VCs, thus improving incumbents' bargaining power over entrepreneurs. More densely networked markets experience less entry, with a one‐standard deviation increase in network ties among incumbents reducing entry by approximately one‐third. Entrants with established ties to target‐market incumbents appear able to overcome this barrier to entry; in turn, incumbents react strategically to an increased threat of entry by freezing out any incumbents who facilitate entry into their market. Incumbents appear to benefit from reduced entry by paying lower prices for their deals.     

Nonparametric estimation for a stochastic volatility model

Consider discrete-time observations (X _{? δ} )_1≤?≤n+1 of the process X satisfying $dX_{t}=\sqrt{V_{t}}dB_{t}Consider discrete-time observations (X _{ℓ δ} )_{1≤ℓ≤n+1} of the process X satisfying dX_t=?{V_t}dB_tdX_{t}=\sqrt{V_{t}}dB_{t} , with V a one-dimensional positive diffusion process independent of the Brownian motion B. For both the drift and the diffusion coefficient of the unobserved diffusion V, we propose nonparametric least square estimators, and provide bounds for their risk. Estimators are chosen among a collection of functions belonging to a finite-dimensional space whose dimension is selected by a data driven procedure. Implementation on simulated data illustrates how the method works.     

Inter-market competition for NYSE-listed securities under decimals

Blume and Goldstein (J Finance 52:221–244, 1997) suggest that quote competition between trading venues may diminish following tick size reductions. We test this suggestion by studying the competitive landscape in the NYSE-listed stocks before and after decimalization. We find that NBBO (National Best Bid and Offer) participation by non-NYSE venues declines following decimalization consistent with the prediction. At the same time, the importance of quote competitiveness in attracting order flow increases. In addition, although not as active in determining and maintaining the best quotes under decimals, non-NYSE venues become more active in price discovery. Finally, decimalization leads to lower trading costs and to smaller differences in trading costs across trading venues.     

Bank mergers and components of risk: An evaluation

The present study empirically examines the contribution of the acquired banks in only the nonconglomerate types of mergers (i.e., banks with banks), where the bulk of the payment is in the form of equity to the acquiring bank and finds overwhelmingly statistically significant evidence that nonconglomerate types of mergers definitely reduce the total as well as the unsystematic risk while having no statistically significant effect on systematic risk. Therefore, it seems that diversification may be a possible motive for bank mergers.     

Optimal solutions of multiplier DEA models

Journal of Productivity Analysis - Conventional models of data envelopment analysis (DEA) are based on the constant and variable returns-to-scale production technologies. Any optimal input and...     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.