[Forum SIS] Bocconi DEC Seminar Abel Rodriguez - September 21st

[Marco Bonetti]

Dear Colleagues,

The DEC Department of Bocconi University is pleased to invite you to  
the seminar:

"Statistical inference in structural credit risk models: Likelihood  
and Bayesian approaches"

held by

Abel Rodriguez
(Department of Applied Mathematics and Statistics,
University of California, Santa Cruz)

Room 3-E4-SR03
Via Roentgen 1
1pm

Monday, September 21st

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Abstract

Structural credit risk models have become widely popular as a tool to  
asses the creditworthiness of
corporations because they allow us to price risk based on fundamentals  
such as the firm's asset prices,
and because they provide a direct link between equity and debt markets.
However, the practical application of these models is complicated by  
the fact that the firms's asset
prices are generally not directly observable and must be estimated,  
along with other model parameters,
from available high frequency market data such as equity and debt  
prices.
With limited exceptions, most of the "calibration" approaches  
typically used in recent empirical studies
that use credit risk models are ad-hoc procedures that try to  
reconstruct the volatility of assets from the
equity volatility in order to obtain asset prices.
As an alternative, this talk discusses the use of formal statistical  
inference procedures in the context of
structural credit risk models.
We discuss both frequentist (maximum likelihood) and Bayesian  
approaches.
However, we argue that, since parameter uncertainty can have a big  
influence on spreads, Bayesian
approaches are more appropriate in this context and allow us to  
partially explain the "credit spread puzzle".

This is joint work with Samuel Malone (Universidad de los Andes) and  
Enrique ter Horst (Euromed Management).
======

You are all invited.
Sincerely,

Marco Bonetti



---
Marco Bonetti
Department of Decision Sciences
Bocconi University
Via Guglielmo Roentgen 1
20136 Milan, Italy
Tel +39 02 58365670
Fax +39 02 58365634

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