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In Rememberance: World Trade Center (WTC)

A Solvency Based Multi-period Corporate Short-term Credit Risk Model

by Hsien-hsing Liao of National Taiwan University, and
Tsung-kang Chen of National Taiwan University

May 9, 2005

Abstract: In current short-term credit risk literature, liquidity crisis prediction model is the main research area. Within this research field, two major models can be classified--- "classical statistical models" and "stochastic intensity models". However few of them can obtain probability of insolvency and expected ratio of liquidity gap at the same time. In addition, within the above two frameworks, few studies apply stochastic solvency ratio models to predict corporate liquidity crisis. Basing upon the two significant characteristics of solvency ratio --"mean-reversion" and "non-negative value" and the concept of varying coefficient model, the study develops a multi-period corporate short-term credit risk model by constructing an "time-dependent stochastic solvency ratio model". It considers the impacts of industrial economic state changes on the structure of a firm's solvency ratio process (i.e. the parameters of the solvency ratio model) through incorporating information generated from a stochastic model of industrial economic state. The solvency ratio model can simulate many solvency ratio paths and then the solvency ratio distributions of each future period. With the information of solvency ratio distribution and the criteria of insolvency (when solvency ratio is less than one), we can obtain both the probability of a company's short-term credit risk and the expected ratio of liquidity gap in future periods. To perform a multi-period firm's short-term credit risk analysis, this solvency ratio model needs only publicly available information of corporate finance and the industrial economic state (i.e. the industrial cyclicality information).

JEL Classification: G3.

Keywords: Stochastic solvency ratio, Multi-period.

Previously titled: A Solvency Based Multi-period Corporate Liquidity Crisis Prediction Model

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