skip to main content


*Di Asih I Maruddani orcid scopus  -  Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro, Indonesia
Trimono Trimono  -  Data Science Study Program, UPN “Veteran” East Java, Indonesia, Indonesia
Mas'ad Mas'ad  -  School of Sociology and Social Policy, University Park, The University of Nottingham, Nottingham, United Kingdom, United Kingdom
Open Access Copyright (c) 2022 MEDIA STATISTIKA under

Citation Format:
Currently, financial assets become an alternative choice for investors in Indonesia to get maximum profits. The Indonesia Stock Exchange is the official capital market in Indonesia which is a place for trading financial assets. Stocks are listed as the most preferred financial asset by investors. In reality, stock investment is not a risk-free investment. The main risk that investors should face is the loss risk. This kind of risk can occur at any time. From that problem, this study aims to do risk assessment on the Indonesian stock market. The evaluation will be started with stock price index prediction using the Stochastic model (Geometric Brownian Motion Model and Jump Diffusion). Then, the result from that processes will be used to get loss risk prediction through the Adjusted Expected Shortfall model. By using the historical price of JKSE index from 01/08/21 to 31/08/22, Jump Diffusion is the best model to predict the JKSE index with MAPE value is 1.08%. Then, at the 95% confidence level and 1-day holding period, the expected loss risk using Adjusted Expected Shortfall model on 09/01/2022 is -0.02978.
Fulltext View|Download
Keywords: Stock Investment, Risk, Stochastic Model, Adjusted Expected Shortfall

Article Metrics:

  1. A. Aibai., Y. Peng., P. Shen., and H. Xu. Can Local Policy Uncertainty Curtail Corporate Speculation on Financial Assets? International Review of Financial Analysis, 83(102287),1-10, (2022)
  2. A. Hussain, F. Choukairi, and K. Dunn. Predicting Survival in Thermal Injury: A Systematic Review of Methodology of Composite Prediction Models. Burn. 39(5), 835-850, (2013)
  3. A. I. L. Wibowo, A.D. Putra, M.S. Dewi, and D.O. Radianto. Differences In Intrinsic Value With Stock Market Prices Using The Price Earning Ratio (PER) Approach As An Investment Decision Making Indicator (Case Study Of Manufacturing Companies In Indonesia Period 2016 - 2017), Aptisi Transaction on Technopreneurship, 1(1), 82-92, (2019)
  4. A. K. Tiwari. Modelling the dynamics of Bitcoin and Litecoin: GARCH Versus Stochastic Volatility Models. Applied Economics, 51(27), 4073-4082, (2019)
  5. A. Simsek. Speculation and Risk Sharing with New Financial Assets. The Quarterly Journal of Economics, 128(3), 1365-1396, (2013)
  6. A.A. Salisu and U.B. Ndako. Modelling stock price–exchange rate nexus in OECD countries: A new perspective. Economic Modelling, 74,105-123, (2018)
  7. A.B. Omar, et al. Forest and Deep Neural Network Models Before and During the COVID-19 Period. Frontiers in Environmental Science, Frontiers in Envirnment Science, 10, 1-10, 2022
  8. A.Nayak, M.M. Pai, and R. M. Pai. Prediction Models for Indian Stock Market. Procedia Computer Science, 89, 441-449, (2016)
  9. Angelaccio, M. Forecasting Public Electricity Consumption with ARIMA Model: A Case Study from Italian Municipalities Energy Data. Procedings of International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 1-3. (2019)
  10. B. Saiti, O.I. Bacha, M. Masih. The diversification benefits from Islamic investment during the financial turmoil: The case for the US-based equity investors. Borsa Istanbul Review, 14(4), 196-211, (2014)
  11. B. Zhang., J. C. C. Chan., and J. L. Cross. Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts. International Journal of Forecasting, 36(4), 1318-1328, (2020)
  12. Basson, L. M., Kilbourn, P. J., and Walters. Forecast accuracy in demand planning: A fast-moving consumer goods case study. Journal of Transport and Supply Chain Management. 13(427): 1-9, (2019)
  13. C. Setiawan and H. Oktariza. Syariah and Conventional Stocks Performance of Public Companies Listed on Indonesia Stock Exchange. Journal of Accounting, Finance and Economics, 3(1), 51-64, 2013
  14. C. W. Chiu., H. Mumtaz, and G. Pinter. Forecasting with VAR models: Fat tails and stochastic volatility. International Journal of Forecasting. 33(4), 1124-1143 (2017)
  15. C.T. Guloksuz. Geometric Brownian motion approach to modelling stock prices. FORCE, 2(1), 53-63, (2021)
  16. D. Jadhav, T.V. Ramanathan, and U.V. Naik-Nimbakar. Modified Expected Shortfall: A New Robust Coherent Risk Measure. Journal of Risk, 16(1), 69-83, (2013)
  17. D. Purnamasari. The Effect of Changes in Return on Assets, Return on Equity, and Economic Value Added to the Stock Price Changes and Its Impact on Earnings Per Share. Research Journal of Finance and Accounting, 6(6), 80-89, (2015)
  18. D.A.I. Maruddani and Trimono. Prediksi harga saham PT. Astra agro lestari TBK dengan jump diffusion model. JRAMB. 3(1), 1-11, 2017
  19. G. Liu., J. Zhang., H. Wu., and Y. Peng. Financial Asset Allocations and R&D Activities: Evidence from China’s Listed Companies. Emerging Markets Finance and Trade, 55(3), 531-544, (2019)
  20. H. Gao., H. Wen., and X, Wang. Pandemic effect on corporate financial asset holdings: Precautionary or return-chasing?. Research in International Business and Finance, 62, 1-13, (2022)
  21. H. Ma and Y. Li. Stock Price Jump-diffusion Process Model Based on Fractional Brownian Motion Theory. Advances in Social Science, Education and Humanities Research, 285, 374-378. (2019)
  22. Hersugondo et al. Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia, Economies, 10(17), 1-13, (2022)
  23. Huang, Y. Portfolio optimization based on jump-diffusion stochastic differential equation. Alexandria Engineering Journal. 59 (4): 2503-2512, (2020)
  24. IDX Indonesia. Stock Index. (accesed on 10/06/2022)
  25. J. C. C. Chan. The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling. Journal of Business & Economic Statistics, 35(1), 17-28 (2017)
  26. K. Reddy and V. Clinton. Simulating Stock Prices Using Geometric Brownian Motion: Evidence from Australian Companies. Australasian Accounting, Business and Finance Journal. 10(3), 23-47, (2016)
  27. KSEI. Statistik Pasar Modal Indonesia April 2017. Jakarta: KSEI Publisher. (2017)
  28. KSEI. Statistik Pasar Modal Indonesia April 2022. Jakarta: KSEI Publisher. (2022)
  29. L. Cui, K.E. Meyer, and H. W. Hu. What drives firms’ intent to seek strategic assets by foreign direct investment? A study of emerging economy firms, Journal of World Business, 49(4), 488-501, (2014)
  30. L. M. Basson., P. J. Kilbourn., and J. Walters. Forecast accuracy in demand planning: A fast-moving consumer goods case study. Journal of Transport and Supply Chain Management. 13(427): 1-9 (2019)
  31. Li, Z., Zhang, W. G., Liu, Y. J., and Zhang, Y. Pricing discrete barrier options under jump-diffusion model with liquidity risk. International Review of Economics and Finance. 59: 347-368, (2019)
  32. M. Angelaccio. Forecasting Public Electricity Consumption with ARIMA Model: A Case Study from Italian Municipalities Energy Data. Procedings of International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 1-3 (2019)
  33. M.R. Islam and N. Nguyen. Comparison of Financial Models for Stock Price Prediction. Risk and Financial Management, 13(181), 1-19,2020
  34. O. Antwi. Stochastic Modeling of Stock Price Behavior on Ghana Stock Exchange. International Journal of Systems Science and Applied Mathematics, 2(6), 116-125, (2017)
  35. O. Romanchenko, O. Shemetkova, V. Piatanova, and D. Kornienko. Approach of Estimation of the Fair Value of Assets on a Cryptocurrency Market. International Conference on Digital Science, 245-253 (2018)
  36. P. Bolton, H. Chen, and N. Wang. Market timing, investment, and risk management. Journal of Financial Economics. 109(1), 40-62, (2013)
  37. Q. Ji, B.Y Liu, W.L. Zhao, and Y. Fan. Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS. International Review of Financial Analysis, 68(101238), 1-14, 2020
  38. R. Li., T. Han, and X. Song. Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization. Applied Soft Computing, 124(109089), 1-13, (2022)
  39. Trimono, D.A.I Maruddani, D. Ispriyanti. Pemodelan Harga Saham Dengan Geometric Brownian Motion Dan Value At Risk Pt Ciputra Development Tbk. Jurnal Gaussian. 6(2), 261-270, (2017)
  40. Trimono, et al. Bounds of Adj-TVaR Prediction for Aggregate Risk. Inprime Journal, 1(1), 1-17, (2019)
  41. Z. Fathali, Z. Kodia, and L.B. Said. Stock Market Prediction of NIFTY 50 Index Applying Machine Learning Techniques. Applied Artificial Intelligence, 36(1), 2022

Last update:

No citation recorded.

Last update: 2024-06-20 17:25:25

No citation recorded.