Portfolio Optimization for Rupiah Exchange Rate using Multidimensional Geometric Brownian Motion Model
Abstract
Exchange rate fluctuations are critical in ensuring economic stability and shaping foreign investment, while foreign currencies serve as asset and wealth diversification instruments. This study aims to predict foreign exchange rates with a multidimensional geometric Brownian motion model and determine the optimal portfolio fund allocation with the Markowitz model using the Moore-Pendrose method. The multidimensional GBM model was employed for its ability to capture the volatility and interdependence among multiple currencies, making it more suitable for multi-asset portfolios than univariate models. The Markowitz model was used to determine the optimal asset allocation that achieves a specified expected return with minimal risk, while the Moore-Penrose method was applied to address matrix inversion challenges in high-dimensional data. Using data from 2023 to April 2024 on the Indonesian rupiah against the Singapore Dollar (SGD), Chinese Yuan (CNY), and Euro (EUR), this study finds that the multidimensional GBM model effectively forecasts exchange rate movements, as indicated by MAPE values below 10% for each currency. "The optimal portfolio yields a risk of 0.28% and an expected return of 0.009%, with asset allocations of 90.3% in SGD, 8.2% in CNY, and 1.5% in EUR. The dominance of SGD in the optimal portfolio suggests it was the most favorable investment option against the rupiah during the study period. This reflects Singapore's strong economic fundamentals and strategic position as a global financial hub. These findings provide valuable insights for investors and financial analysts seeking to manage currency risk and enhance returns through data-driven diversification strategies.
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Agustini, W. F., Affianti, I. R., & Putri, E. R. M. (2018). Stock Price Prediction Using Geometric Brownian Motion. Journal of Physics: Conference Series, 974(1), 9–12. https://doi.org/10.1088/1742-6596/974/1/012047
Aksaraylı, M., & Pala, O. (2018). A Polynomial Goal Programming Model for Portfolio Optimization Based on Entropy And Higher Moments. Expert Systems with Applications, 94(1), 185–192. https://doi.org/10.1016/j.eswa.2017.10.056
Brătian, V., Acu, A. M., Oprean‐stan, C., Dinga, E., & Ionescu, G. M. (2021). Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion. Mathematics, 9(22), 11–20. https://doi.org/10.3390/math9222983
Carissa, N., & Khoirudin, R. (2020). The Factors Affecting The Rupiah Exchange Rate in Indonesia. Jurnal Ekonomi Pembangunan, 18(1), 37–46. https://doi.org/10.29259/jep.v18i1.9826
Deng, G.-F., Lin, W.-T., & Lo, C.-C. (2012). Markowitz-Based Portfolio Selection With Cardinality Constraints Using Improved Particle Swarm Optimization. Expert Systems with Applications, 39(4), 4558–4566. https://doi.org/10.1016/j.eswa.2011.09.129
Eris, I., Putro, T. S., & Kornita, S. E. (2017). Pengaruh Tingkat Suku Bunga Bi Rate, Jumlah Uang Beredar dan Neraca Pembayaran Terhadap Nilai Tukar Rupiah Tahun 2006-2015. Jurnal Online Mahasiswa Fakultas Ekonomi Universitas Riau, 4(1), 393–404.
Germansah, G., Tjahjana, R. H., & Herdiana, R. (2023). Geometric Brownian Motion in Analyzing Seasonality of Gold Derivative Prices. Eduvest - Journal of Universal Studies, 3(8), 1558–1572. https://doi.org/10.59188/eduvest.v3i8.892
Grechuk, B., & Zabarankin, M. (2014). Inverse Portfolio Problem With Mean-Deviation Model. European Journal of Operational Research, 234(2), 481–490. https://doi.org/10.1016/j.ejor.2013.04.056
Huang, X., & Yang, T. (2020). How Does Background Risk Affect Portfolio Choice: An Analysis Based On Uncertain Mean-Variance Model With Background Risk. Journal of Banking & Finance, 111(3), 2039–2054. https://doi.org/10.1016/j.jbankfin.2019.105726
Ibrahim, S. N. I., Misiran, M., & Laham, M. F. (2021). Geometric Fractional Brownian Motion Model For Commodity Market Simulation. Alexandria Engineering Journal, 60(1), 955–962. https://doi.org/10.1016/j.aej.2020.10.023
Kalayci, C. B., Ertenlice, O., & Akbay, M. A. (2019). A Comprehensive Review Of Deterministic Models And Applications For Mean-Variance Portfolio Optimization. Expert Systems with Applications, 125(1), 345–368. https://doi.org/10.1016/j.eswa.2019.02.011
Kloeden, P. ., & Platen, E. (1992). Numerical Solution of Stochastic Differential Equation. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-662-12616-5
Li, B., Sun, Y., Aw, G., & Teo, K. L. (2019). Uncertain Portfolio Optimization Problem Under A Minimax Risk Measure. In Applied Mathematical Modelling, 76(1), 274–281. https://doi.org/10.1016/j.apm.2019.06.019
Li, B., & Zhang, R. (2021). A New Mean-Variance-Entropy Model For Uncertain Portfolio Optimization With Liquidity Diversification. Chaos, Solitons & Fractals, 146(1), 989–1003. https://doi.org/10.1016/j.chaos.2021.110842
Liu, S.-T. (2011). The Mean-Absolute Deviation Portfolio Selection Problem With Interval-Valued Returns. Journal of Computational and Applied Mathematics, 235(14), 4149–4157. https://doi.org/10.1016/j.cam.2011.03.008
Lv, S., Wu, Z., & Yu, Z. (2016). Continuous-Time Mean-Variance Portfolio Selection With The Random Horizon in an Incomplete Market. Automatica, 69(1), 176–180. https://doi.org/10.1016/j.automatica.2016.02.017
Ramos, A. L., Mazzinghy, D. B., Barbosa, V. da S. B., Oliveira, M. M., & da Silva, G. R. (2019). Evaluation Of An Iron Ore Price Forecast Using A Geometric Brownian Motion Model. Revista Escola de Minas, 72(1), 9–15. https://doi.org/10.1590/0370-44672018720140
Ramos, H. P., Righi, M. B., Guedes, P. C., & Müller, F. M. (2023). A Comparison Of Risk Measures For Portfolio Optimization With Cardinality Constraints. Expert Systems with Applications, 228(1), 6–12. https://doi.org/10.1016/j.eswa.2023.120412
Reddy, K., & Clinton, V. (2016). Simulating Stock Prices Using Geometric Brownian Motion: Evidence From Australian Companies. Australasian Accounting, Business and Finance Journal, 10(3), 23–47. https://doi.org/10.14453/aabfj.v10i3.3
Silaban, S., Aadilah, H., & Matondang, K. (2023). Influence of Rupiah Exchange Rate on Indonesia’s Economic Growth: Literature Study. Journal of Business Management and Economic Development, 1(2), 123–131. https://doi.org/10.59653/jbmed.v1i02.48
Suganthi, K., & Jayalalitha, G. (2019). Geometric Brownian Motion in Stock Prices. Journal of Physics: Conference Series, 1377(1). https://doi.org/10.1088/1742-6596/1377/1/012016
DOI: https://doi.org/10.31764/jtam.v9i2.29953
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