Value at Risk Prediction for the GJR-GARCH Aggregation Model

  • Nurhayati Nurhayati Universitas Almuslim
  • Wiwin Apriani Universitas Almuslim
  • Ariestha Widyastuty Bustan Universitas Pasifik Morotai
DOI: https://doi.org/10.30598/pijmathvol1iss1pp01-06
Keywords: Volatilitas, VaR, GARCH, GJR-GARCH, aggregation, asymmetric

Abstract

Volatility is the level of risk faced due to price fluctuations. The greater the volatility brings, the greater the risk. We need a measure such as Value at Risk (VaR) and volatility modeling to overcome this. The most frequently used volatility model in the financial sector is GARCH. However, this model is still unable to accommodate the asymmetric nature, so the GJR-GARCH model was developed. In addition, this study also used aggregation returns with two assets in them. This study aimed to determine the VaR prediction for the GJR-GARCH(1.1) aggregation model and its comparison with the GARCH(1.1) aggregation model. The results obtained indicate that the prediction of volatility using the GJR-GARCH(1.1) aggregation model is more accurate than the GACRH(1.1) aggregation model because it has a correct VaR value that is close to the given confidence level.

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Published
2022-05-01
How to Cite
Nurhayati, N., Apriani, W., & Bustan, A. (2022). Value at Risk Prediction for the GJR-GARCH Aggregation Model. Pattimura International Journal of Mathematics (PIJMath), 1(1), 01-06. https://doi.org/10.30598/pijmathvol1iss1pp01-06
Issue
Vol 1 No 1 (2022): Pattimura International Journal of Mathematics (PIJMath)
Section
Articles

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