Prediction of Motor Vehicle Insurance Claims Using ARIMA-GARCH Models
Abstract
Motorized vehicles are one of the means of transportation used by Indonesian people. As of 2021, the Central Statistics Agency (CSA) recorded the growth of motorized vehicles in Indonesia reaching 141,992,573 vehicles. Lack of control over the number of motorized vehicles results in losses for various parties, such as accidents, damage and other unwanted losses. The size of insurance claims has the potential to fluctuate, because it is influenced by several factors, such as policy changes, market conditions and economic conditions. This research aims to predict the size of motor vehicle insurance claims using the ARIMA-GARCH model which is used to predict the size of vehicle insurance claims by dealing with non-stationarity and heteroscedasticity in time series data. Based on research, the best model obtained is the ARIMA (2,1,3) - GARCH (1,0) model which produces seven significant parameters. Meanwhile, based on the MAPE value, it shows that the ARIMA (2,1,3)-GARCH (1,0) model is quite accurate. The results of this research can be taken into consideration in predicting the size of insurance claims in the future.
Keywords
Full Text:
PDFReferences
Adubisi, O. D., David, I. J., James, F. E., Awa, U. E., & Terna, A. J. (2018). A predictive Autoregressive Integrated Moving Average (ARIMA) Model for forecasting inflation rates. Res. J. Bus. Econ. Manag, 1(1), 1-8.
Ajib, M. (2019). Asuransi syariah. Lentera Islam.
Bollerslev, T. (1986). Generalzed Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31(3), 307–327.
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity twith estimates of thevariance of United Kingdom Inflation. Econometrica, 50(4), 987-1008.
E.P.Box, G. et al. (2015). Time Series Analysis: Forecasting and Control. 5th Edition. New Jersey: Wiley.
Montgomery, D.C., Jennings, C. L., dan Kulachi, M. (2008). Introduction to Time Series Analysis and Forecasting. New Jersey: John Wiley & Sons. Inc.
Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods. New York: Pearson
DOI: https://doi.org/10.47194/orics.v5i3.331
Article Metrics
Abstract view : 22 timesPDF - 7 times
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Operations Research: International Conference Series
This work is licensed under a Creative Commons Attribution 4.0 International License.
 Published By:Â
Iora Journal
Jl. Merkuri Timur VI No. 1, RT. 007, RW. 004, Manjahlega, Rancasari, Kota Bandung, Jawa Barat, INDONESIAÂ Phone: +62 85841953112; +62 811
ORICS Indexed By:Â
Â
 Â This work is licensed under a Creative Commons Attribution 4.0 International License.
View My Stats