Time Series Modelling and Forecasting for a System of Credit and Debit in the Cameroon’s NSIF

Claver, Jimbo Henri; Séraphin, Ngongo Isidore; Jacques, Alemoka Jean; Nicolas, Andjiga Gabriel; Magloire, Etoua Remy; Suzuki, Takeru

doi:10.22606/jaam.2020.52001

Cited by 2 publications

References 2 publications

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Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

Isidore¹,

Magloire²,

Claver³

et al. 2021

JAAM

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The financial crisis that is currently shaking the world, particularly the successive failures of the major banks have brought the issue of banking risks, including credit risk, back to the forefront. This risk must now be managed by more sophisticated methods. In this paper we present two methods that allow us to establish two functions, namely Fisher discriminant analysis and logistic regression; these two functions allow us to evaluate the risk of non-repayment incurred by a bank in the light of our data. It emerges that Fisher discriminant analysis is more effective or efficient than logistic regression for the evaluation of the risk of non-repayment of credit. Discriminant analysis and logistic regression are two methods of credit risk management here the problem we are trying to solve is how to help banks choose the most efficient method between the latter two.

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Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

Isidore¹,

Magloire²,

Claver³

et al. 2021

JAAM

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Predicting Exchange Rates Volatility Using Hybrid ARIMA-GARCH Model

Dinga,

Claver,

Kum

et al. 2024

Advances in Computational Intelligence and Robotics

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For the last few decades, the Autoregressive Integrated Moving Average (ARIMA) model has been a popular linear model in predicting time series. Recent research has shown that the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model can be a promising alternative to the traditional linear models. A time series can be considered to comprise a linear and non-linear part. This chapter aims to highlight in a relevant way the need to properly define the dynamics of the linear and non-linear parts of the time series as far as the time series prediction is concerned. Using the GARCH model and the combined ARIMA-GARCH model in the analysis and prediction of exchange rates, this study shows that the combined ARIMA-GARCH-T model, which defines the dynamics of the linear and non-linear parts of the exchange rate time series, has a higher predictive power when compared to the GARCH-T model.

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Time Series Modelling and Forecasting for a System of Credit and Debit in the Cameroon’s NSIF

Cited by 2 publications

References 2 publications

Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

Discriminant Analysis and Logistic Regression Applying To Credit Risk Management

Predicting Exchange Rates Volatility Using Hybrid ARIMA-GARCH Model

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