Peramalan Volatilitas IHSG dan Estimasi Value-at-Risk Menggunakan Model Student APARCH

Authors

  • Najibullah Najibullah Politeknik Kutaraja, Banda Aceh, Aceh
  • Ricky Ariansyah Politeknik Kutaraja, Banda Aceh, Aceh
  • Fitrian Rizky Politeknik Kutaraja, Banda Aceh, Aceh

DOI:

https://doi.org/10.61393/heiema.v2i1.105

Keywords:

Value-at-risk, APARCH, Student-t Distribution

Abstract

This study aim to find the best model to predict daily volatility of IHSG using APARCH model under student-t distribution, estimate value-at-risk measure based on the model and compare it with the value-at-risk measure estimated from GARCH. This study limits the search only on APARCH model class. The sample are IHSG dataset which is derived from yahoo finance, starting from Jan 1st, 2015 to Dec 31th, 2021. The result suggest that value-at-risk measure estimated using student APARCH are more adept to negatif shock than that of GARCH. Nevertheless, the result of value-at-risk back-testing do not show any significant differences between those two.

References

Aryati, T., & Walansendouw, Y. C. (2013). Analisis Pengaruh Diversifikasi Arumningtyas, F., Prahutama, A., & Kartikasari, P. (2021). Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns. Jurnal Varian, 5(1), 71–80. https://doi.org/10.30812/varian.v5i1.1474

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307–327. https://doi.org/10.1016/03044076(86)90063-1

Christoffersen, P. F. (1998). Evaluating Interval Forecasts. International Economic Review, 39(4), 841. https://doi.org/10.2307/2527341

Ding, Z., Granger, C. W. J., & Engle, R. F. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1(1), 83–106. https://doi.org/10.1016/0927-5398(93)90006-D

Duffie, D., & Pan, J. (1997). An Overview of Value at Risk. The Journal of Derivatives, 4(3), 7–49. https://doi.org/10.3905/jod.1997.407971

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987. https://doi.org/10.2307/1912773

Francq, C., & Thieu, L. Q. (2019). QML Inference For Volatility Models With Covariates. Econometric Theory, 35(1), 37–72. https://doi.org/10.1017/S0266466617000512

Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x

Hafizah, S. Z., Kusnandar, D., & Martha, S. (2020). Model Generalized Autoregressive Conditional Heteroscedasticity in Mean Untuk Meramalkan Volatilitas Return Saham. Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, 9(1).

Huang, Y. C., & Lin, B.-J. (2004). Value-at-Risk Analysis for Taiwan Stock Index Futures: Fat Tails and Conditional Asymmetries in Return Innovations. Review of Quantitative Finance and Accounting, 22(2), 79–95. https://doi.org/10.1023/B:REQU.0000015851.78720.a9

Hung, J.-C., Lee, M.-C., & Liu, H.-C. (2008). Estimation of value-at-risk for energy commodities via fat-tailed GARCH models. Energy Economics, 30(3), 1173–1191. https://doi.org/10.1016/j.eneco.2007.11.004

Husein, I., & Lubis, A. I. D. (2022). Egarch Model Prediction for Sale Stock Price. Jurnal Varian, 6(1), 49–60. https://doi.org/10.30812/varian.v6i1.1975

Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73–84. https://doi.org/10.3905/jod.1995.407942

Maulana, Y. (2020). Analisis Volatilitas Return Saham PT Antam (Persero) Tbk dan PT Adaro Energy Tbk Dengan Garch, Egarch Dan GJR. Jurnal Akuntansi dan Pajak, 20(2), 197–200. https://doi.org/10.29040/jap.v20i2.859

Megawati, S. M., Kusnandar, D., & others. (2020). Pemodelan dan peramalan volatilitas saham menggunakan model integrated generalized autoregressive conditional heteroscedasticity. Bimaster: Buletin Ilmiah Matematika, Statistika dan Terapannya, 9(1).

Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347. https://doi.org/10.2307/2938260

Ridha, R., & Wibowo, A. (2020). Analisis Volatilitas Return indeks saham sektor barang konsumsi di Indonesia: Aplikasi metode Treshold-garch (TGARCH). VARIANCE: Journal of Statistics and Its Applications, 2(1), 35–43. https://doi.org/10.30598/variancevol2iss1page35-43

Sidadolog, J. H., Sumarjaya, I. W., & Tastrawati, N. K. T. (2020). Peramalan Volatilitas Return Saham Menggunakan Metode Asymmetric Power Arch (APARCH). E-Jurnal Matematika, 9(3), 157–164. https://doi.org/10.24843/MTK.2020.v09.i03.p293

Tsay, R. S. (2010). Analysis of Financial Time Series: Tsay/Financial Time Series 3E. John Wiley & Sons, Inc. https://doi.org/10.1002/9780470644560

Warsito, O. L. D., & Robiyanto, R. (2020). Analisis Volatilitas Cryptocurrency, Emas, Dollar, Dan Indeks Harga Saham (IHSG). International Journal of Social Science and Business, 4(1). https://doi.org/10.23887/ijssb.v4i1.23887

Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955. https://doi.org/10.1016/01651889(94)90039-6

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Published

2023-01-26

How to Cite

Najibullah, N., Ariansyah, R., & Rizky, . F. . (2023). Peramalan Volatilitas IHSG dan Estimasi Value-at-Risk Menggunakan Model Student APARCH . HEI EMA : Jurnal Riset Hukum, Ekonomi Islam, Ekonomi, Manajemen Dan Akuntansi, 2(1), 70–82. https://doi.org/10.61393/heiema.v2i1.105

Issue

Vol. 2 No. 1 (2023): Januari

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Articles

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Copyright (c) 2023 Najibullah Najibullah, Ricky Ariansyah, Fitrian Rizky

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