Abstract
In this paper we consider the corporate default problem. One of the well-known approaches is to model the dynamics of the assets of the firm, and compute the probability that the assets fall below a threshold (which is related to the firm’s liabilities). When modeling the asset value dynamics as a jump-diffusion process (the most realistic model), a serious computational problem arises. In this paper we propose a fast method for computing the default probability. The new method achieves significant acceleration over the available approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M. Odom and R. Sharda, “A neural network model for bankruptcy prediction” Proc of IEEE Int. Conf. on Neural Networks, pp. 163–168, 1990.
R. Merton, “On the pricing of corporate debt: the risk structure of interest rates”, J. Finance, Vol. 29, pp. 449–470, 1974.
F. Longstaff and E. Schwartz, “A simple approach to valuing risk fixed and floating rate debt”, J. Finance, Vol. 50, pp. 789–819, 1995.
R. Merton, “Option pricing when the underlying stock returns are discontinuous”, J. Financial Economics, Vol. 3, pp. 125–144, 1976
F. Knight, Essentials of Brownian Motion and Diffusion, American Mathematical Society, Providence, RI, 1981.
M. Domine, “First passage time distribution of a Wiener Process with drift concerning two elastic barriers”, J. Appl. Prob. Vol. 33, pp. 164–175, 1996
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atiya, A. (2000). Fast Algorithms for Computing Corporate Default Probabilities. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_33
Download citation
DOI: https://doi.org/10.1007/3-540-44491-2_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41450-6
Online ISBN: 978-3-540-44491-6
eBook Packages: Springer Book Archive