Reinsurance Revenue Forecasting Using the Singleexponential Smoothing : Application and Residualanalysis

Time series analysis is a crucial tool for predicting how reinsurance revenues will be used. Reinsurance businesses must be aware of the estimated future values of claims in order to minimise the substantial losses brought on by predicted future claims. The potential values of anticipated claims are essential for reinsurance companies to avoid major losses caused by likely future claims. Single exponential smoothing serves as a fundamental component in more advanced forecasting techniques and time series models. It can be used as a benchmark to compare the performance of other forecasting methods and evaluate their accuracy. In this study, we take into account the additive Holt-Winters’ filtering and testing studies as well as the single exponential smoothing model for estimating reinsurance revenues. Using an original time series, the proposed paradigm’s value is demonstrated. The accuracy of the predictions is evaluated using the Ljung-Box test and the corresponding p-value