Modelling Default Probability

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Extra Credit Project
Modelling Default Probability
The business of lending and extending credit facilities to borrowers is associated with risks (credit risks), which the lender needs to be aware of. The significant risk is for the borrower to default, failing to honour their part of the agreement; the borrower loses the money because the client did not pay the borrowed loan. Therefore, credit and lending institutions have a well-elaborated way of trying to cut their losses, for instance, coming up with a model that will enable them to determine the probability of the client defaulting before issuing the loan.
The use of software that considers variables like the number of dependents under the client's care, monthly income, age, number of credit lines that the client is access to, among others, is used to determine the repayment ability of the client. For instance, there is a high likelihood of a client defaulting if the number of dependent ants under care is high and the monthly income is low. The borrowed money will be used up, but their income stream is small, making it difficult for the client to repay the debt and meet the daily needs.
Cooperate uses major models to determine the client's probability of default, namely: structural and reduced-form models. Model assets are viewed as a stochastic process. The structural model assumes the default caused by a trigger; for instance, the depreciation of assets below some anticipated value might result in the client default. On the other hand, the reduced form model assumes default is caused by default intensity or default rate but not triggers. The factors that can result in default can be either internal (firm characteristics) or external factors like (recession, unemployment, and GDP growth) (Ross, 2019). The reduced form model uses statistical relations to predict the outcome using historical data and econometrics models.
The advantages of using the structural model are that: tends to be forward-oriented and gives room for monitoring and evaluation because of the ongoing updating of equity market data. However, its shortcomings include being best used in highly efficient and fluid markets, subjective inputs, and, lastly, challenging to customize to operate in private institutions. On the other hand, reduced model forms use historical data and econometrics models to predict the client's behavior (Blümke, 2018). The use of the reduced form model comes up with coefficients that can predict the probability of default. The model can be a linear regression model
or logistical model
The reduced form model will help analyze and develop coefficients for a model in the form of /. The use of logit regression model will be used for this assignment because of the binary nature of the outcome. It is either the client defaults or not (0,1). However, the binary nature of variables will make use of logs and probability to develop an econometric model to predict the rate of default for a client.
Data and Data Analysis
The assignment makes use of a historical dataset that is huge and needs to be cleaned before being worked. The raw data set had 150,000 entries; of this, only 112,738 were vali...