The incompatibility of achieving a fully specified linear model with assuming that residual dependent-variable variance is random

The residual dependent-variable variance in experiments is not “random error”, as it is often assumed to be, but merely “unaccounted for variance”, because what is random is inexplicable in terms of any possible set of independent-variables and this is something that ultimately is only empirically determinable. So, if there is any unaccounted for dependent-variable variance, an experiment’s set of independent-variables is certainly under-specified and perhaps mis-specified because of the confounding of variables included in this set by causally relevant variables not included in the set. Thus, the proper first empirical test of any linear model is whether it leaves any residual dependent-variable variance, and if it does then none of its independent variables can yet logically justifiably be claimed to predict or causally explain any of the dependent-variable variance whatsoever.     

An analytic derivation of the cost of deposit insurance and loan guarantees An application of modern option pricing theory

It is not uncommon in the arrangement of a loan to include as part of the financial package a guarantee of the loan by a third party. Examples are guarantees by a parent company of loans made to its subsidiaries or government guarantees of loans made to private corporations. Also included would be guarantees of bank deposits by the Federal Deposit Insurance Corporation. As with other forms of insurance, the issuing of a guarantee imposes a liability or cost on the guarantor. In this paper, a formula is derived to evaluate this cost. The method used is to demonstrate an isomorphic correspondence between loan guarantees and common stock put options, and then to use the well developed theory of option pricing to derive the formula.     

A general equilibrium model of changing risk premia: theory and tests

We derive and test a dynamic discrete-time model of asset returns.Both the risks of individual securities and equilibrium riskpremia change predictably in the model, but these changes canbe attributed to movements in the returns and prices of onlytwo well-diversified portfolios. Any other components of returnsshould be unpredictable. Using the generalized method of moments,the model is estimated and tested on portfolios of equities.We find the data supportive of the model's restrictions, evenwhen instruments designed to capture the January effect areemployed.