Publication

Can Equity Volatility Explain the Global Loan Pricing Puzzle?

2011
Working Papers

Abstract

We examine whether equity volatility can explain the difference in syndicated corporate loan spreads paid by U.S. and European borrowers first documented by Carey and Nini (2007). We argue that OLS estimates of the association between equity volatility and loan spreads are biased and inconsistent. We suggest instrumental variables that potentially identify consistent estimates. Our instrumental variable results indicate that there is no statistically significant difference in loan spreads paid by U.S. and European borrowers, and that OLS estimates of the association between idiosyncratic equity volatility and corporate loan spreads are biased downward by about a factor of 5.

Official source

https://infoscience.epfl.ch/entities/publication/270eb2a3-6d3c-4492-8dc7-22d93d987806

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2011
Working Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/270eb2a3-6d3c-4492-8dc7-22d93d987806

About this result

Related concepts (32)

Instrumental variables estimation

In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results.

Errors-in-variables models

In statistics, errors-in-variables models or measurement error models are regression models that account for measurement errors in the independent variables. In contrast, standard regression models assume that those regressors have been measured exactly, or observed without error; as such, those models account only for errors in the dependent variables, or responses. In the case when some regressors have been measured with errors, estimation based on the standard assumption leads to inconsistent estimates, meaning that the parameter estimates do not tend to the true values even in very large samples.

Ordinary least squares

In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable.

Related publications (2)

Can Equity Volatility Explain the Global Loan Pricing Puzzle?

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