Multi-Period Corporate Short-Term Credit Risk Assessment - a State-Dependent Stochastic Liquidity Balance Model

In recent decades, literatures on credit risk measurement evolved dramatically. According to modeling techniques, they can be roughly grouped into two major categories, quot;accounting-based modelsquot; and quot;market-based modelsquot;. However, among the above models, few of them develop representative liquidity measure from corporate financial data to evaluate short-term credit risk and further build up a stochastic model based on the liquidity measure. In addition, we can hardly find a model that can generate probability of insolvency and expected liquidity deficiency endogenously and concurrently. Basing upon two significant characteristics of liquidity balance per unit asset (later denoted as LB/A) - quot;mean-reversionquot; and quot;allowing positive and negative valuesquot;, and the concept of varying coefficient model, the study constructs a quot;time-dependent stochastic liquidity balance modelquot; to assess multi-period corporate short-term credit risk. It considers the impacts of industrial economic state changes on the structure of a firm's LB/A process (i.e. the parameters of the liquidity balance model) through incorporating information generated from a stochastic industrial economic state model. The liquidity balance model can simulate many LB/A paths and then the LB/A distributions of each future period. With LB/A distribution and the criteria of insolvency (when LB/A is less than zero), we can obtain both the probability of a company's liquidity crisis and the expected liquidity deficiency in future periods. In addition, for outside investors or creditors, this liquidity balance model is readily for them to perform a firm's multi-period short-term credit risk analysis by using only publicly available information of corporate finance and the industrial economic state (i.e. the industrial cyclicality information). The empirical results of this study show preliminarily supports for the effectiveness of the model