Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio

Version 1 : Received: 8 October 2020 / Approved: 9 October 2020 / Online: 9 October 2020 (09:04:18 CEST)

How to cite: Tarno, T.; Maruddani, D.A.I.; Rahmawati, R.; Hoyyi, A.; Trimono, T.; Munawar, M. ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio. Preprints 2020, 2020100191 (doi: 10.20944/preprints202010.0191.v1). Tarno, T.; Maruddani, D.A.I.; Rahmawati, R.; Hoyyi, A.; Trimono, T.; Munawar, M. ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio. Preprints 2020, 2020100191 (doi: 10.20944/preprints202010.0191.v1).

Abstract

Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the value at risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroscedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through backtesting test. In this study, the portfolio formed from Astra Agro Lestari Ltd (AALI) and Indofood Ltd (INDF) stocks from 10/02/2012 to 10/01/2019. The results showed that the best model is ARIMA(0,0,[3])-GARCH(1,2) with AIC of -5.604 and MSE 1.874e-07.At confidence level of 95% and 1 day holding period, the VaR of the ARIMA(0,0,[3])-GARCH(1,2) was -0.3464. Based on the backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the violation ratio (VR) is equal to 0.

Subject Areas

stocks portfolio; loss risk; heteroscedastic; VaR; backtesting

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.