Upper Bounds of Stock Portfolio Investment Risk Using Value at Risk (Case Study: Indonesian Blue-Chip Stocks in 2022)

Document Type : Research Paper

Authors

1 Department of Management, Faculty of Economics and Busniness, Diponegoro University, Semarang, Indonesia

2 Department of Accounting, Faculty of Economics and Business, Diponegoro University, Semarang, Indonesia

3 Department of Accounting, Faculty of Economic and Business, Diponegoro University, Semarang, Indonesia.

4 Data Science Study Program, Faculty of Computer Science, Pembangunan Nasional Veteran Jawa Timur University, Surabaya, Indonesia.

5 Department of Management, Faculty of Economics and Business, Diponegoro University, Semarang, Indonesia

Abstract

In recent years, stocks become the most preferred asset by Indonesian investors. Besides offering large profits, stock investment also has a risk factor that can occur at any time. One way to minimize risk is to form a stock portfolio. This paper aims to measure the upper bounds of the portfolio loss risk formed by several single assets that are mutually dependent. The upper bound value is chosen because the exact value of portfolio loss risk is difficult to obtained by Convolution or Panjer Recursion methods. The main analysis of this research is formed the upper bounds of stock portfolio investment risk using VaR with Cornish Fisher Expansion aproach by utilized comonotonicity and convex order properties. The portofolio contains of 3 single asset (ARTO.JK, ITMG.JK, and MIKA.JK) which collected from IDX Indonesia from 10/25/21 to 10/21/22. The novelty of this research is combined comonotonicity and convex order properties with VaR-CFE to get upper bounds of portolio risk predicition. The result show that at 95% significance level and 1-day holding period, the upper bounds of VaR-CFE prediction for the portfolio is -0.1394. The social impact of this research can be a benchmark to get accurate risk prediction of their portfolio asset.

Keywords

Main Subjects


Achudume, C., & Ugbebor, O. O. (2021). Optimal Portfolio of an Investor in a Financial Market. Journal of Physics: Conference Series, 1734(1), 012050. Retrieved from https://doi.org/10.1088/1742-6596/1734/1/012050 https://iopscience.iop.org/article/10.1088/1742-6596/1734/1/012050
Aghnitama, R. D., Aufa, A. R., & Hersugondo, H. (2021). Market Capitalization dan Profitabilitas Perusahaan dengan FAR, AGE, EPS, dan PBV Sebagai Variabel Kontrol. Jurnal Akuntansi Dan Manajemen, 18(02), 01–11. Retrieved from https://doi.org/10.36406/jam.v18i02.392
Amédée-Manesme, C. -O., Barthélémy, F., & Maillard, D. (2019). Computation of the Corrected Cornish–Fisher Expansion Using the Response Surface Methodology: Application to VaR and CVaR. Annals of Operations Research, 281(1–2), 423–453. Retrieved from https://doi.org/10.1007/s10479-018-2792-4
Ansari, J., & Rüschendorf, L. (2020). Upper Risk Bounds in Internal Factor Models with Constrained Specification Sets. Probability, Uncertainty and Quantitative Risk, 5(1), 3. Retrieved from https://doi.org/10.1186/s41546-020-00045-y
ARAI, T. (2017). Good Deal Bounds With Convex Constraints. International Journal of Theoretical and Applied Finance, 20(02), 1750011. Retrieved from https://doi.org/10.1142/S021902491750011X
Avramov, D., Chordia, T., Jostova, G., & Philipov, A. (2013). Anomalies and Financial Distress. Journal of Financial Economics, 108(1), 139-159. Retrieved from https://doi.org/10.1016/j.jfineco.2012.10.005
Bernard, C., Denuit, M., & Vanduffel, S. (2018). Measuring Portfolio Risk Under Partial Dependence Information. Journal of Risk and Insurance, 85(3), 843–863. https://doi.org/10.1111/jori.12165
Bernard, C., Rüschendorf, L., & Vanduffel, S. (2017). Value-at-Risk Bounds with Variance Constraints. Journal of Risk and Insurance, 84(3), 923–959. Retrieved from https://doi.org/10.1111/jori.12108
Bignozzi, V., Puccetti, G., & Rüschendorf, L. (2015). Reducing Model Risk via Positive and Negative Dependence Assumptions. Insurance: Mathematics and Economics, 61, 17–26. Retrieved from https://doi.org/10.1016/j.insmatheco.2014.11.004
Bouhadjar, M., Zeghdoudi, H., & Remita, M. R. (2016). On Stochastic Orders and their Applications: Policy Limits and Deductibles. Applied Mathematics & Information Sciences, 10(4), 1385–1392. Retrieved from https://doi.org/10.18576/amis/100417
Chen, X., Liu, Q., & Tong, X. T. (2022). Dimension Independent Excess Risk by Stochastic Gradient Descent. Electronic Journal of Statistics, 16(2). https://doi.org/10.1214/22-EJS2055
Cheung, K. C., Denuit, M., & Dhaene, J. (2017). Tail Mutual Exclusivity and Tail-Var Lower Bounds. Scandinavian Actuarial Journal, 2017(1), 88–104. Retrieved from https://doi.org/10.1080/03461238.2015.1084945
Cheung, K. C., & Vanduffel, S. (2013). Bounds for Sums of Random Variables When the Marginal Distributions and the Variance of the Sum Are Given. Scandinavian Actuarial Journal, 2013(2), 103–118. Retrieved from https://doi.org/10.1080/03461238.2011.558186
Christoph, G., Monakhov, M. M., & Ulyanov, V. V. (2020). Second-Order Chebyshev–Edgeworth and Cornish–Fisher Expansions for Distributions of Statistics Constructed from Samples with Random Sizes. Journal of Mathematical Sciences, 244(5), 811–839. Retrieved from https://doi.org/10.1007/s10958-020-04655-x
Cooper, J., Bloxham, N., & Mitic, P. (2021). Incremental Value-at-Risk. Journal of Risk Model Validation, 14(1), 1–38. Retrieved from https://www.risk.net/journal-of-risk-model-validation/7472201/incremental-value-at-risk
Dhaene, J., Linders, D., Schoutens, W., & Vyncke, D. (2014). A Multivariate Dependence Measure for Aggregating Risks. Journal of Computational and Applied Mathematics, 263, 78–87. Retrieved from https://doi.org/10.1016/j.cam.2013.12.010
Embrechts, P., Puccetti, G., & Rüschendorf, L. (2013). Model Uncertainty and VaR Aggregation. Journal of Banking & Finance, 37(8), 2750–2764. Retrieved from https://doi.org/10.1016/j.jbankfin.2013.03.014
Feng, M., Wächter, A., & Staum, J. (2015). Practical Algorithms for Value-at-Risk Portfolio Optimization Problems. Quantitative Finance Letters, 3(1), 1–9. Retrieved from https://doi.org/10.1080/21649502.2014.995214
Fernandez, M., Almaazmi, M. M., & Joseph, R. (2020). Foreign Direct Investment In Indonesia: An Analysis From Investors Perspective. International Journal of Economics and Financial Issues, 10(5), 102–112. Retrieved from https://doi.org/10.32479/ijefi.10330
Florea, A., Păltănea, E., & Bălă, D. (2015). Convex Ordering Properties and Applications. Journal of Mathematical Inequalities, 4, 1245–1257. Retrieved from https://doi.org/10.7153/jmi-09-95
Gao, J., & Zhao, F. (2017). A New Approach of Stochastic Dominance for Ranking Transformations on the Discrete Random Variable. Economics, 11(1). https://doi.org/10.5018/economics-ejournal.ja.2017-14
Hadiyoso, A., Firdaus, M., & Sasongko, H. (2016). Building an Optimal Portfolio on Indonesia Sharia Stock Index (ISSI). Bisnis & Birokrasi Journal, 22(2). Retrieved from https://doi.org/10.20476/jbb.v22i2.5699
Hanbali, H., Dhaene, J., & Linders, D. (2022). Dependence Bounds for the Difference of Stop-Loss Payoffs on the Difference of Two Random Variables. Insurance: Mathematics and Economics, 107, 22–37. Retrieved from https://doi.org/10.1016/j.insmatheco.2022.07.008
Haugh, M., Iyengar, G., & Song, I. (2015). A Generalized Risk Budgeting Approach to Portfolio Construction. Journal of Computational Finance, 21(2), 29–60. Retrieved from  https://ideas.repec.org/a/rsk/journ0/5316546.html
Hersugondo, H., Ghozali, I., Handriani, E., Trimono, T., & Pamungkas, I. D. (2022). Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia. Economies, 10(1), 17. Retrieved from https://doi.org/10.3390/economies10010017
Irsan, M. Y. T., Priscilla, E., & Siswanto, S. (2022). Comparison of Variance Covariance and Historical Simulation Methods to Calculate Value At Risk on Banking Stock Portfolio. Jurnal Matematika, Statistika Dan Komputasi, 19(1), 241–250. Retrieved from https://doi.org/10.20956/j.v19i1.21436
Jagati, P. H. (2019). Descriptive Analysis Of Stock Market Investor. ICTACT Journal on Management Studies, 5(3), 1068–1072. Retrieved from https://ictactjournals.in/IJMS/ArticleDetails?id=3920
Jain, A. K., & Gupta, B. B. (2018). Towards Detection of Phishing Websites on Client-Side Using Machine Learning Based Approach. Telecommunication Systems, 68(4), 687–700. Retrieved from https://doi.org/10.1007/s11235-017-0414-0
Jakobsons, E., Han, X., & Wang, R. (2016). General Convex Order on Risk Aggregation. Scandinavian Actuarial Journal, 2016(8), 713–740. Retrieved from https://doi.org/10.1080/03461238.2015.1012223
Jakobsons, E., & Vanduffel, S. (2015). Dependence Uncertainty Bounds for the Expectile of a Portfolio. Risks, 3(4), 599–623. Retrieved from https://doi.org/10.3390/risks3040599
Juniar, A., Rahmi, Z., Rahmawati, R., & Fadah, I. (2020). Value at Risk in the Formation of Optimal Portfolio on Sharia-Based Stocks. IJRTE, 8(5), 1198–1203. Retrieved from https://www.ijrte.org/portfolio-item/E5750018520/
KSEI Indonesia. (2017). Press Release: Capital Market Investors Reach 1 Million. Retrieved 22 November 2017 from Https://Www.Ksei.Co.Id/Files/Uploads/Press_releases/Press_file/Id-Id/133_berita_pers_jumlah _investor_pasar_modal_tembus_1_juta_20170918134206.Pdf
---------- (2022a). Dominated by Millennials and Gen Z, Stock Investors Reach 4 Million. Retrieved from https://share.google/CB5gx0sYSrhp4XYuv
---------- (2022b). Press Release: Capital Market Investors Reach 10 Million. Retrieved from https://share.google/UXHScwzlEWQVdt0Bk
Kumar, C. D. N., & Srinivasan, S. (2014). PDF of the Random Variable When Its Distribution Function Changes after the Change Points. Applied Mathematical Sciences, 8, 337–343. Retrieved from https://doi.org/10.12988/ams.2014.311627
Lu, Z., Meng, S., Liu, L., & Han, Z. (2018). Optimal Insurance Design under Background Risk with Dependence. Insurance: Mathematics and Economics, 80, 15–28. Retrieved from https://doi.org/10.1016/j.insmatheco.2018.02.006
Maruddani, D. A. I., & Trimono, T. (2021). Valuation of Portfolio Risk and Performance of Several Blue Chip Stocks in Indonesia Using Value-at-Risk Based on n-Dimensional Geometric Brownian Motion. Thailand Statistician, 19(3), 501–510. Retrieved from https://ph02.tci-thaijo.org/index.php/thaistat/article/view/244528
Murata, R., & Hamori, S. (2021). ESG Disclosures and Stock Price Crash Risk. Journal of Risk and Financial Management, 14(70), 1–20. Retrieved from https://www.mdpi.com/1911-8074/14/2/70
Murti, T. H., & Sahara. (2019). The Impact of Investment on Regional Economic Growth in Indonesia. Journal of Economics and Development Policy, 8(2), 163–181 [in Indonesian]. Retrieved from https://journal.ipb.ac.id/index.php/jekp/article/view/32768
Ortega-Jiménez, P., Sordo, M. A., & Suárez-Llorens, A. (2021). Stochastic Comparisons of Some Distances between Random Variables. Mathematics, 9(9), 981. Retrieved from https://doi.org/10.3390/math9090981
Pasaribu, F. (2019). Value at Risk of Momentum Investment Strategy: Indonesia’s Liquid Stocks Portfolio. Jurnal Manajemen Indonesia, 19(1), 30–45. Retrieved from https://journals.telkomuniversity.ac.id/ijm/article/view/1982
Pucetti, G. (2013). Sharp Bounds on the Expected Shortfall for a Sum of Dependent Random Variables. Statistics and Probability Letters, 83, 1227-1232. Retrieved from https://www.sciencedirect.com/science/article/pii/S0167715213000230
Radović, M., Radukić, S., & Njegomir, V. (2018). The Application of the Markowitz’s Model in Efficient Portfolio Forming on the Capital Market in the Republic of Serbia. Economic Themes, 56(1), 17–34. Retrieved from https://doi.org/10.2478/ethemes-2018-0002
Ridha, M. R., & Budi, N. (2020). The Effect of Foreign Direct Investment, Human Development and Macroeconomic Condition on Economic Growth: Evidence from Indonesia. Journal of Indonesian Applied Economics, 8(2), 46–54. Retrieved from https://jiae.ub.ac.id/index.php/jiae/article/view/299
Salsabila, A., & Hasnawati, S. (2018). Value-at-Risk and Expected Returns of Portfolio (Companies Listed on LQ45 Index Period 2013–2016). KnE Social Sciences, 3(10). Retrieved from https://doi.org/10.18502/kss.v3i10.3407
Sholikhah, S. M., Sudarto, & Shaferi, I. (2020). Risk Analysis of Banking Companies Listed on Indonesia Stock Exchange during Covid-19 2020. International Sustainable Competitiveness Advantage 2020., 112–131. Retrieved from https://ejournal.upi.edu/index.php/JRAK/article/view/38535
Sumaji, Y. M. P., Hsu, W.-H. L., & Salim, U. (2017). Analysis of Market Rısk in Stock Investment Usıng Value at Rısk Method (Study on Manufacturıng Companıes in Lq-45 Lısted on Indonesıa Stock Exchange). Asia Pacific Management and Business Application, 6(1), 1–14. Retrieved from https://doi.org/10.21776/ub.apmba.2017.006.01.1
Sun X., Dhaene, J., & Vanmaele M. (2018). Efficient Computation of the Optimal Strikes in the Comonotonic Upper Bound for an Arithmetic Asian Option. International Journal of Applied Mathematics and Statistics, 57(4), 95–106. Retrieved from https://doi.org/10.1016/S0167-6687(99)00051-7
Tarno, T., Maruddani, D. A. I., Rahmawati, R., Hoyyi, A., Trimono, T., & Munawar, M. (2020). ARIMA-GARCH Model and ARIMA-GARCH Ensemble for Value-at-Risk Prediction on Stocks Portfolio. Preprints, 1–14. Retrieved from https://share.google/bKWDXhnB7mOSGAtKz
Tarno, T., Trimono, T., Maruddani, D. A. I., Wilandari, Y., & Utami, R. S. (2022a). Risk Assessment of Stocks Portfolio through Ensemble Arma-Garch and Value at Risk (Case Study: Indf.Jk and Icbp.Jk Stock Price). Media Statistika, 14(2), 125–136. Retrieved from https://doi.org/10.14710/medstat.14.2.125-136
Tarno, T., Trimono, T., Maruddani, D. A. I., Wilandari, Y., & Utami, R. S. (2022b). Risk Assessment Of Stocks Portfolio Through Ensemble Arma-Garch And Value At Risk (Case Study: Indf.Jk And Icbp.Jk Stock Price). Media Statistika, 14(2), 125–136. Retrieved from https://doi.org/10.14710/medstat.14.2.125-136
Trimono, Susilo, A., Handayani, D., & Syuhada, K. (2019). Bounds of Adj-TVaR Prediction for Aggregate Risk. Indonesian Journal of Pure and Applied Mathematics., 1(1), 1–7. Retrieved from https://doi.org/10.15408/inprime.v1i1.12788
Westgaard, S., Frydenberg, S., Andersen Sveinsson, J., & Aaløkken, M. (2020). Performance of Value-At-Risk Averaging in the Nordic Power Futures Market. The Journal of Energy Markets. Retrieved from https://doi.org/10.21314/JEM.2020.207
Xing, G. D., & Li, X. H. (2018). On Bounds of Value-at-Risk and Convex Risk Measure of Portfolio of Weighted Dependent Risks. Chinese Quarterly Journal of Mathematics, 33(4), 421–433. Retrieved from https://dianda.cqvip.com/Qikan/Article/Detail?id=6100251809&from=Qikan_Search_Index
Zanfelicce, R. L., & Rabechini Jr, R. (2021). The Influence of Risk Management on the Project Portfolio Success – Proposal of a Risk Intensity Matrix. Gestão & Produção, 28(2). Retrieved from https://doi.org/10.1590/1806-9649-2020v28e5264
Zhang, Y., & Nadarajah, S. (2018). A Review of Backtesting for Value at Risk. Communications in Statistics - Theory and Methods, 47(15), 3616–3639. Retrieved from https://doi.org/10.1080/03610926.2017.1361984
Zhou, M., Dhaene, J., & Yao, J. (2018). An Approximation Method for Risk Aggregations and Capital Allocation Rules Based on Additive Risk Factor Models. Insurance: Mathematics and Economics, 79, 92–100. Retrieved from https://doi.org/10.1016/j.insmatheco.2018.01.002
Zhou, R., & Palomar, D. P. (2021). Solving High-Order Portfolios via Successive Convex Approximation Algorithms. IEEE Transactions on Signal Processing, 892–904. Retrieved from https://share.google/zqlbjcLYEfcaCOhCg