Determination of Stock Investment Risk Using the Multivariate Time Series Approach


  • Asri Rahmawati Graduate School, IPB University, IPB Campus Dramaga, Bogor 16680, Indonesia
  • Retno Budiarti Department of Mathematics, Faculty of Mathematics and Natural Sciences, IPB University, IPB Campus Dramaga, Bogor 16680, Indonesia
  • Hadi Sumarno Department of Mathematics, Faculty of Mathematics and Natural Sciences, IPB University, IPB Campus Dramaga, Bogor 16680, Indonesia


risk, value at risk, invesment, transfer function, ARIMA


Stock investment is putting money in stocks carried out in the long term with the hope of getting profits in the future. Investors make investments to get returns. Return is the result obtained from investment activities within a certain period. The higher the profits, the greater the risks faced by investors. To overcome the chance, it is possible to estimate the return in the future and calculate the risk that investors will likely obtain. The return includes time series data so that estimates can be made to determine its future value. One model that can be used to estimate stock returns is the transfer function. The transfer function combines ARIMA and regression analysis that can be applied to time series related to other variables. This study found that the daily stock return of PT. Cisadane Sawit Raya Tbk (CSRA) has a cross-correlation with Jakarta Composite Indeks (JKSE) during the daily stock return of PT. Salim Ivomas Pratama Tbk (SIMP) has a cross-correlation with JKSE and the exchange rate. The transfer function estimation results show that the RMSE value for CSRA is 0.24%. This value is greater than the RMSE value for SIMP, which is 0.01%. Meanwhile, from the results of risk testing on stock assets, it is found that the greater the level of trust used, the greater the risk of loss.


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How to Cite

Asri Rahmawati, Retno Budiarti, & Hadi Sumarno. (2023). Determination of Stock Investment Risk Using the Multivariate Time Series Approach. International Journal of Sciences: Basic and Applied Research (IJSBAR), 67(1), 76–88. Retrieved from