Analisis Komparatif Model Statistik Dan Probabilistik Untuk Peramalan Harga Emas Dan Pengukuran Risiko
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Abstract
Pergerakan harga emas yang dipengaruhi oleh volatilitas pasar dan ketidakpastian ekonomi memerlukan metode peramalan serta analisis risiko yang akurat. Penelitian ini bertujuan membandingkan kinerja Autoregressive Integrated Moving Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Extreme Value Theory (EVT), Monte Carlo Simulation berbasis Geometric Brownian Motion (GBM), dan Random Walk dalam peramalan harga emas dan analisis risiko. Data historis harga emas harian periode 1 Maret 2020 hingga 2 April 2026 yang diperoleh dari Investing.com dianalisis melalui tahapan pre-processing, transformasi log return, pemodelan, serta evaluasi menggunakan Mean Absolute Error (MAE), Root Mean Square Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Risiko investasi diukur menggunakan Value-at-Risk (VaR) dan Conditional Value-at-Risk (CVaR). Hasil penelitian menunjukkan bahwa model ARIMA(1,1,1) memberikan akurasi peramalan terbaik dengan nilai MAE 396,14, RMSE 454,31, dan MAPE 9,15%. Adapun GARCH efektif memodelkan volatilitas, EVT mampu mengidentifikasi risiko ekstrem, Monte Carlo menghasilkan distribusi probabilistik pergerakan harga, dan Random Walk digunakan sebagai model pembanding. Hasil penelitian menunjukkan bahwa kombinasi beberapa pendekatan memberikan analisis peramalan dan risiko yang lebih komprehensif dibandingkan penggunaan satu metode tunggal.
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References
R. Gulyás et al., “Optimizing Portfolios Using Monte Carlo and Conditional Value-at-Risk,” Frontiers in Blockchain, vol. 8, 2025. [Online]. Available: https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2025.1665541/pdf
P. Meher and R. K. Mishra, “Risk-adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing,” Australasian Accounting, Business and Finance Journal, vol. 18, no. 5, 2024. [Online]. Available: https://www.uowoajournals.org/aabfj/article/id/1036/
P. Glasserman, Monte Carlo Methods in Financial Engineering. New York, NY, USA: Springer, 2004. [Online]. Available: https://link.springer.com/book/10.1007/9780387216171
P. Boyle, “Options: A Monte Carlo Approach,” Journal of Financial Economics, vol. 4, no. 3, pp. 323–338, 1977. [Online]. Available: https://doi.org/10.1016/0304-405X(77)90005-8
L. Moreira and I. Santos, “Monte Carlo Simulation and Stochastic Optimization in Finance: A Review,” Journal of Economic Surveys, 2026. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1111/joes.12702
L. Zhao et al., “Forecasting Oil and Gold Price Risk Using ARIMA Models,” IEEE, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/9210624
G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time Series Analysis: Forecasting and Control, 5th ed. Hoboken, NJ, USA: Wiley, 2015. [Online]. Available: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118675021
R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed., Melbourne, Australia: OTexts, 2021. [Online]. Available: https://otexts.com/fpp3/
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 Time Series and Forecasting Methods,” International Journal of Forecasting, vol. 36, no. 1, pp. 54–74, 2020. [Online]. Available: https://doi.org/10.1016/j.ijforecast.2019.04.014
R. F. Engle, “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, vol. 50, no. 4, pp. 987–1007, 1982. [Online]. Available: https://www.jstor.org/stable/1912773
T. Bollerslev, “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986. [Online]. Available: https://doi.org/10.1016/0304-4076(86)90063-1
K. Makatjane and T. Tsoku, “Time-Varying Volatility Modeling Using GARCH,” Risks, vol. 10, no. 1, 2022. [Online]. Available: https://www.mdpi.com/2227-7072/10/1/10
Z. Guo et al., “Dynamic Relationships Between Energy Markets Using ARMA-GARCH Models,” Environment, Development and Sustainability, 2026. [Online]. Available: https://link.springer.com/article/10.1007/s10668-026-07431-0
K. Makatjane and N. Moroke, “Extreme Value Modeling in Financial Markets,” Risks, vol. 9, no. 2, 2021. [Online]. Available: https://www.mdpi.com/2227-7072/9/2/18
P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling Extremal Events for Insurance and Finance. Berlin, Germany: Springer, 1997. [Online]. Available: https://link.springer.com/book/10.1007/978-3-642-33483-2
A. J. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management: Concepts, Techniques and Tools, 2nd ed. Princeton, NJ, USA: Princeton University Press, 2015. [Online]. Available: https://press.princeton.edu/books/hardcover/9780691166278/quantitative-risk-management
R. T. Rockafellar and S. Uryasev, “Optimization of Conditional Value-at-Risk,” Journal of Risk, vol. 2, no. 3, pp. 21–41, 2000. [Online]. Available: https://doi.org/10.21314/JOR.2000.038
C. Acerbi and D. Tasche, “On the Coherence of Expected Shortfall,” Journal of Banking & Finance, vol. 26, no. 7, pp. 1487–1503, 2002. [Online]. Available: https://doi.org/10.1016/S0378-4266(02)00283-2
J. Wang et al., “Sample Complexity of Conditional Value-at-Risk Based Learning,” 2023. [Online]. Available: https://openreview.net/forum?id=fclJPWwXcd
E. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance, vol. 25, no. 2, pp. 383–417, 1970. [Online]. Available: https://doi.org/10.2307/2325486
Lo, A. W., & MacKinlay, A. C. (1988). Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test. The Review of Financial Studies, 1(1), 41–66. https://doi.org/10.1093/rfs/1.1.41
D. G. Baur and B. M. Lucey, “Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold,” Financial Review, vol. 45, no. 2, pp. 217–229, 2010. [Online]. Available: https://doi.org/10.1111/j.1540-6288.2010.00244.x
D. G. Baur and T. K. McDermott, “Is Gold a Safe Haven? International Evidence,” Journal of Banking & Finance, vol. 34, no. 8, pp. 1886–1898, 2010. [Online]. Available: https://doi.org/10.1016/j.jbankfin.2009.12.008
Investing.com, “Gold Historical Data.” [Online]. Available: https://www.investing.com/commodities/gold-historical-data. [Accessed: Apr. 2, 2026].
P. Piotrowski, I. Rutyna, D. Baczyński, and M. Kopyt, “Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors,” Energies, vol. 15, no. 24, p. 9657, 2022. [Online]. Available: https://www.mdpi.com/1996-1073/15/24/9657
C. Chen, J. Twycross, and J. M. Garibaldi, “A New Accuracy Measure Based on Bounded Relative Error for Time Series Forecasting,” PLOS ONE, vol. 12, no. 3, 2017. [Online]. Available: https://doi.org/10.1371/journal.pone.0174202
R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006. [Online]. Available: https://doi.org/10.1016/j.ijforecast.2006.03.001
D. Anggelia, Y. F. Riti, and P. W. Siswanto, “Analisis Perbandingan Metode ARIMA dan Least Square untuk Prediksi Harga Emas: Pendekatan Probabilistik dan Statistik,” Jurnal Sistem Informasi dan Informatika (SIMIKA), vol. 7, no. 1, pp. 95–103, 2024. [Online]. Available: https://repositori.ukdc.ac.id/2179/