Francesco Corielli

Risk Shifting Through Nonfinancial Contracts: Effects on Loan Spreads and Capital Structure of Project Finance Deals

We study capital structure negotiation and cost of debt financing between sponsors and lenders using a sample of more than 1,000 project finance loans worth around US

Parametrix Approximation of Diffusion Transition Densities

195 billion closed between 1998 and 2003. We find that lenders: (i) rely on the network of nonfinancial contracts as a mechanism to control agency costs and project risks, (ii) are reluctant to price credit more cheaply if sponsors are involved as project counterparties in the relevant contracts, and finally (iii) do not appreciate sponsor involvement as a contractual counterparty of the special purpose vehicle when determining the level of leverage. Copyright (c) 2010 The Ohio State University.

Project Finance Collateralised Debt Obligations: An Empirical Analysis of Spread Determinants

A new analytical approximation tool, derived from the classical PDE theory, is introduced in order to build approximate transition densities of diffusions. The tool is useful for approximate pricing and hedging of financial derivatives and for maximum likelihood and method of moments estimates of diffusion parameters. The approximation is uniform with respect to time and space variables. Moreover, easily computable error bounds are available in any dimension.

A note on the decidability of de Finetti's coherence

Credit rating is the most important variable in determining tranche spread at issue on collateralised debt obligations (CDOs) issues backed by project finance (PF) loans. Factors that are important for pricing in the case of corporate bonds, such as market liquidity and weighted average maturity, are also relevant for determining spreads for these securities. Furthermore, the nature of the underlying assets has a substantial impact on CDO pricing: Primary market spread is significantly higher when the underlying PF loans bear a higher level of market risk and when the proportion of projects still under construction in the securitised portfolio is larger.

“Laws of Large Numbers” in Continuum Economies

To check the de Finetti coherence of a putative probability assigned to a classA of events, we must know the possible combinations of truth values (constituents) of any finite class of events inA. Even for a very simple, finite,A this can be impossible. In this case the notion of DF coherence cannot be applied to some or all the putative probabilities on this class of events.

Factor Based Index Tracking

In continuum economies we have a continuum (usually [0, 1]) of agents i each with an allocation x(i) of something. If x(i) is the realization of a random variable X(i) then x(i) as a function of i can be seen as the empirical path of a stochastic process. It is usually asked that the random variables X(i) be independent and identically distributed according to a distribution function F, that the “sample” distribution function of x(i) be “almost surely”, or in some other sense, equal to the coordinate distribution function F, that the integral of x(i) or X(i) (this is not usually clear) over a set be equal to the common expectation of the X(i)’s multiplied by the “length” of the set. This paper considers problems and possible solutions connected with these requirements.