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All Journal International Journal of Global Operations Research International Journal of Quantitative Research and Modeling
Febrianty, Popy
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics Physics

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Extreme Risk Analysis on Financial Derivatives in Indonesia Using Extreme Value-at-Risk Based on Generalized Pareto Distribution (GPD) Febrianty, Popy
International Journal of Global Operations Research Vol. 6 No. 1 (2025): International Journal of Global Operations Research (IJGOR)
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i1.358

Abstract

Develop a Value at Risk (VaR) model based on Generalized Pareto Distribution (GPD) to analyze extreme risks in financial derivative portfolios in Indonesia. The GPD approach is chosen because it can describe the tail distribution of price data that exceeds a certain threshold. Price data (in US dollars/USD) of financial derivatives from the Indonesian market are collected from 2011 to 2022 taken from the International Financial Statistics (IMF Data). Furthermore, the data is analyzed to determine the threshold, then the GPD model is applied to extract the tail distribution. The calculation of GPD-based VaR is carried out to provide a more accurate estimate of the potential for extreme losses. This study is expected to contribute to the management of extreme risks in the derivatives market in Indonesia, as well as provide guidance for investors and financial institutions in making more informed investment decisions.
Stock Investment Portfolio Optimization Using Mean-Variance Model Based on Stock Price Prediction with Long-Short Term Memory Febrianty, Popy; Napitupulu, Herlina; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.1002

Abstract

Stock investment in the technology sector in Indonesia offers high potential returns. However, like any other investment instruments, the associated risks cannot be overlooked. Therefore, an appropriate portfolio optimization strategy is needed to enable investors to achieve optimal returns while managing risk. In this study, the author combines stock price prediction approaches with portfolio optimization methods to construct an efficient portfolio. The Long-Short Term Memory (LSTM) model is used to predict daily closing stock prices, with model performance evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics. An optimal LSTM model is obtained with a batch size hyperparameter of 16 for ISAT, MTDL, MLPT, and EDGE stocks, and a batch size of 32 for DCII stock. For all stocks, the average prediction error from the actual values falls within the range of 1.53% ≤ MAPE ≤ 3.52%. The optimal portfolio is constructed using the Mean-Variance risk aversion model to maximize expected returns while considering risk. The resulting optimal portfolio composition consists of a weight allocation of 19.7% for ISAT stock, 36.8% for MTDL stock, 34.8% for MLPT stock, 3.6% for EDGE stock, and 15% for DCII stock. This portfolio yields an expected portfolio return of 0.001249 and a portfolio variance of 0.000311.
Co-Authors Herlina Napitupulu Sukono .