Strategi Optimasi Portofolio Saham Sektoral Berbasis Spillover dan Hedging Terintegrasi Menggunakan Pendekatan Mean-Value-at-Risk

Rizki Apriva Hidayana

Sari


Model optimisasi berperan penting dalam pembentukan portofolio saham sektoral dengan tujuan memaksimalkan return dan meminimalkan risiko. Korelasi antar aset, yang sering diabaikan, dapat memengaruhi efektivitas diversifikasi. Oleh karena itu, analisis volatilitas spillover digunakan untuk mengukur hubungan antar sektor dan dampaknya terhadap risiko portofolio. Penelitian ini bertujuan membangun model portofolio dengan pendekatan Mean-Value-at-Risk (Mean-VaR) serta strategi hedging yang disesuaikan dengan toleransi risiko investor. Korelasi antar sektor diukur menggunakan analisis spillover berbasis kausalitas Granger. Pemilihan saham didasarkan pada rasio antara rata-rata return terhadap risiko, dengan fokus pada saham berisiko rendah dan return tinggi. Model ini diimplementasikan pada saham sektoral di pasar modal Indonesia menggunakan Python, dan kinerjanya dinilai melalui Sharpe ratio. Hasil akhir berupa vektor bobot portofolio optimal dan strategi alokasi modal efisien yang mempertimbangkan dampak spillover dan strategi lindung nilai (hedging).

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Abuselidze, G., & Slobodianyk, A. (2019, September). Investment of the Financial Instruments And Their Influence On The Exchange Stock Market Development. In Economic Science For Rural Development Conference Proceedings (No. 52).

Ahlawat, S. (2024). Sector Investing Risks in Different Market Conditions. Wilmott. https://doi.org/10.54946/wilm.12024

Alonso, M. N. i. (2025). Conformal Portfolio Optimization. https://doi.org/10.2139/ssrn.5011129

Balcı, N. (2024). Volatility spillover effects between stock markets during the crisis periods: diagonal bekk approach. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi. https://doi.org/10.30794/pausbed.1462608

Basile, I. G., Ferrari, P., & Abate, G. (2019). The impact of sectorial and geographical segmentation on risk-based asset allocation techniques. Investment Management & Financial Innovations, 16(3), 260–274. https://doi.org/10.21511/IMFI.16(3).2019.24

Cebrián, F. J., García-Abadillo, M. T., & Negrut, L. (2016). A straightforward analysis of sector portfolios in the US stock market. Applied Econometrics and International Development, 16(1), 105–114. https://ideas.repec.org/a/eaa/aeinde/v16y2016i1_9.html

Diwekar, U. M. (2020)., Introduction to applied optimization (Vol. 22). Springer Nature.

Erceg, Ž., & Mularifović, F. (2019). Integrated MCDM model for processes optimization in supply chain management in wood company. Operational research in engineering sciences: Theory and applications, 2(1), 37-50.

Gaivoronski, A. A., & Pflug, G. (2005)., ‘Value-at-risk in portfolio optimization: properties and computational approach’, Journal of risk, 7(2), 1-31.

Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. McGraw-hill.

Hu, Y. (2024). Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation. Highlights in Business, Economics and Management, 41, 214–220. https://doi.org/10.54097/farx3k44

Lin, B., Wesseh Jr, P. K., & Appiah, M. O. (2014). Oil price fluctuation, volatility spillover and the Ghanaian equity market: Implication for portfolio management and hedging effectiveness. Energy Economics, 42, 172-182.

Liu, F. (2024). Risk Management in Derivatives Markets: Integrating Advanced Hedging Strategies with Empirical Analysis. SHS Web of Conferences, 188, 01008. https://doi.org/10.1051/shsconf/202418801008

Mendonça, G. H., Ferreira, F. G., Cardoso, R. T., & Martins, F. V. (2020)., ‘Multi-attribute decision making applied to financial portfolio optimization problem’, Expert Systems with Applications, 158, p(1-9).

Narani, R., & Rikumahu, B. (2019)., ‘Analisis Volatility Spillover Harga Emas Dan Harga Bitcoin Tahun 2013-2018’, eProceedings of Management, 6(2).

Niteshbhai, F. S., Sultornsanee, S., & Angkawisittpan, N. (2024). Hybrid Approaches to Portfolio Optimization: Deep Neural Networks and Statistical Methods. 202–207. https://doi.org/10.1109/icpei61831.2024.10748751

Rao, S. S. (2020)., Engineering Optimization: Theory and Practice. John Wiley & Sons.

Roy, S., & Gupta, A. (2020). Safety investment optimization in process industry: A risk-based approach. Journal of loss prevention in the process industries, 63, 104022.

Sari, L. K., Palupiningrum, A. W., & Nuraisyah, A. (2024). Dampak Spillover Antara Harga Komoditas dan Dinamika Pasar Keuangan. Jurnal Aplikasi Bisnis Dan Manajemen. https://doi.org/10.17358/jabm.10.2.585

Vercellis, C. (2011)., Business intelligence: data mining and optimization for decision making. John Wiley & Sons.

Xu, W. (2024). Spillover effects between economic indicators. Highlights in Business, Economics and Management, 44, 235–243. https://doi.org/10.54097/crsx6d43

Zourmba, B. T., Claver, J. H., Cyrille Audrey, N. T., Tchoua, P., & Nguefack-Tsagué, G. (2024). Selection and Analysis of Optimized Portfolio Sectors of Johannesburg Stock Markets. Advances in Computational Intelligence and Robotics Book Series, 295–338. https://doi.org/10.4018/979-8-3693-6215-0.ch012


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