Value at Risk Estimation of Portfolio Affected by the BDS Movement: A Copula Approach

Binarvian Sofawi, Dhoriva Urwatul Wutsqa

Abstract

This study aims to estimate value at risk (VaR) as a measure of the maximum potential loss in an investment portfolio through the application of a Copula approach to stocks affected by the Boycott, Divestment, and Sanctions (BDS) Movement. The data used are the daily return data of MAPI from PT Mitra Adiperkasa, Tbk, FAST from PT Fast Food Indonesia, Tbk, and UNVR from PT Unilever Indonesia, Tbk obtained from the closing stock prices. The returns used are daily simple returns from March 1, 2019, to February 29, 2024, consisting of 1,130 days. The model used in this study is the ARMA-GARCH Copula model. Autoregressive moving average (ARMA) is used due to the involvement of time influence in estimation, while generalized autoregressive conditional heteroskedasticity (GARCH) is used to address the high volatility in stocks. The selection of the best copula model using maximum likelihood estimation (MLE) involves five copulas: gaussian copula, t-Student copula, Clayton copula, Frank copula, and Gumbel copula. The results of the analysis show that Clayton copula is the best model, with VaR of the portfolio of stocks affected by the Boycott, Divestment, and Sanctions (BDS) movement at the 99%, 95%, and 90% confidence levels are 3.45%, 2.11%, and 1.55%, respectively. These findings suggest that lower tail dependence plays an important role in portfolio risk, indicating the potential for simultaneous extreme losses. Therefore, investors are encouraged to consider copula-based risk measurement methods and diversification strategies to minimize potential portfolio losses.


Keywords: ARMA, copula, GARCH, returns, value at risk

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References

Bursa Efek Indonesia, “Saham,” PT Bursa Efek Indonesia. Accessed: Jun. 27, 2024. [Online]. Available: https://www.idx.co.id/id/produk/saham/

Z. Puspitaningtyas, Prediksi risiko investasi saham. Yogyakarta: Pandiva Buku, 2015.

A. Inrawan et al., Portofolio dan investasi. Bandung: Widina Bhakti Persada, 2022.

S. P. Sapkota, “Impact of stock market-specific and macro-economic variables on stock return,” Contemporary Research: An Interdisciplinary Academic Journal, vol. 3, no. 1, pp. 56–66, 2019, [Online]. Available: www.craiaj.com

Palestinian BDS National Committee, “What is BDS?,” BDS Movement. Accessed: Aug. 29, 2024. [Online]. Available: https://bdsmovement.net/what-is-bds

I. Nurasiah, N. Permata, Suaryo, and S. Auliana, “Koreksi harga saham produk terafiliasi dengan israel sebagai akibat dari gerakan boikot, divestasi dan sanksi (BDS) di bursa efek Indonesia (BEI) periode 2023,” Jurnal Ekonomi Keuangan dan Kebijakan Publik), vol. 5, no. 2, pp. 55–61, 2023, doi: https://doi.org/10.30743/jekkp.v5i2.8586.

T. Bollerslev, R. F. Engle, and D. B. Nelson, “ARCH models,” Handbook of Econometrics, vol. 4, pp. 2961–2984, 1994.

S. Makridakis, S. C. Wheelwright, and R. J. Hyndman, Forecasting: methods and applications, 3rd ed. New Jersey: John Wiley & Sons, Inc, 1997.

J. D. Cryer, Time series analysis. PWS Publisher, 1986.

R. S. Tsay, Analysis of financial time series, 2nd ed. New Jersey: John Wiley & Sons, Inc, 2005.

K. Byun and S. Song, “Value at risk of portfolios using copulas,” Commun. Stat. Appl. Methods, vol. 28, no. 1, pp. 59–79, 2021, doi: https://doi.org/10.29220/CSAM.2021.28.1.059.

N. K. Bob, “Value at risk estimation: a GARCH-EVT-Copula approach,” 2013. [Online]. Available: www.math.su.se

R. B. Nelsen, An introduction to copulas, 2nd ed. Springer, 2006.

P. Jorion, Value at risk: the new benchmark for managing financial risk, 2nd ed. New York: McGrew-Hill, 2000.

T. B. Raju, V. S. Sengar, R. Jayaraj, and N. Kulshrestha, “Study of volatility of new ship building prices in LNG shipping,” International Journal of e-Navigation and Maritime Economy, vol. 5, pp. 61–73, 2016, doi: https://doi.org/10.1016/j.enavi.2016.12.005.

DOI: https://doi.org/10.13057/ijas.v9i1.95849

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