Loan Delinquency Modeling

ARX has developed a model using the Markov chain principle which predicts transition probabilities for various loan delinquency buckets. Since an event of default for any borrower occurs only after the borrower has incurred various delinquencies, it is therefore essential to monitor the migration of loans through various stages of delinquency.

A Markov chain is a random process with the property that the next state depends only on the current state. Markov chains are useful tools for statistical as well as stochastic modeling in almost all fields of modern applied mathematics.

Markov chains are used in a variety of different phenomena, including forecasting asset price fluctuations and market crashes. Dynamic macroeconomics heavily uses Markov chains. An example is using Markov chains to exogenously model equity prices in a general equilibrium setting. ARX uses Markov chains to estimate the probability of loan default and the probability of loan delinquency. ARX will provide clients with a white paper on our methodology upon request.