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Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index
( Vol-6,Issue-3,March 2019 )
Author(s):

Nashirah Abu Bakar, Sofian Rosbi

Keywords:

Monte Carlo Simulation, Malaysia Stock Exchange, Volatility, Islamic Finance.

Abstract:

The objective of this study is to evaluate the volatility rate of sharia-company in Malaysia Stock Exchange using Monte Carlo Simulation (MCS). This study collected daily stock price form Thomson Reuters Datastream for calculating monthly return and volatility rate. In validating the findings of volatility rate, this study performed normality diagnostics test, and Monte Carlo Simulation (MCS). Result indicates the distribution of volatility rate is follows normal distribution. In addition, Monte Carlo Simulation also proved the volatility rate is 4.85% and standard deviation is 2.23. Result of process capability shows the value of volatility rate is under statistical control with implementation on Monte Carlo Simulation. The significant of this study is it provides a better understanding for investors regarding the financial environment in Malaysia Stock Exchange. This information will help investors to make proper selection of their investment portfolio.

ijaers doi crossref DOI:

10.22161/ijaers.6.3.2

Paper Statistics:
  • Total View : 92
  • Downloads : 17
  • Page No: 006-012
Cite this Article:
MLA
Nashirah Abu Bakar et al ."Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index". International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),vol 6, no. 3, 2019, pp.006-012 AI Publications, doi:10.22161/ijaers.6.3.2
APA
Nashirah Abu Bakar, Sofian Rosbi(2019).Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index. International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),6(3), 006-012. http://dx.doi.org/10.22161/ijaers.6.3.2
Chicago
Nashirah Abu Bakar, Sofian Rosbi. 2019,"Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index". International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)).6(3):006-012. Doi: 10.22161/ijaers.6.3.2
Harvard
Nashirah Abu Bakar, Sofian Rosbi. 2019,Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index, International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)).6(3), pp:006-012
IEEE
Nashirah Abu Bakar, Sofian Rosbi."Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index", International Journal of Advanced Engineering Research and Science(ISSN : 2349-6495(P) | 2456-1908(O)),vol.6,no. 3, pp.006-012,2019.
Bibtex
@article {nashirahabubakar2019monte,
title={Monte Carlo Simulation for Data Volatility Analysis of Stock Prices in Islamic Finance for Malaysia Composite Index},
author={Nashirah Abu Bakar, Sofian Rosbi},
journal={International Journal of Advanced Engineering Research and Science},
volume={6},
year= {2019},
}
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References:

[1] Abu Bakar, N. and Rosbi, S. (2016). Long Term Performance of Islamic Share Price for Initial Public Offerings (IPOs) in Malaysia: Evidence from Sharia-Compliant Companies Listed on the Malaysian Stock Exchange (2006-2010). International Journal of Management Science and Business Administration, 2 (6), 43-57.
[2] Abu Bakar, N. and Rosbi, S. (2017). Dynamic Forecasting method for Shariah-compliant Share Price of Healthcare sector in Malaysian Stock Exchange. International Journal of Advanced Engineering, Management and Science, 3 (8), 855-863.
[3] Abu Bakar, N. and Rosbi, S. (2018). Efficient Frontier Analysis for Portfolio Investment in Malaysia Stock Market. Science International (Lahore), 30 (5), 723-729.
[4] Abu Bakar, N. Rosbi, S. and Uzaki, K. (2018). Evaluating Forecasting Method Using Autoregressive Integrated Moving Average (ARIMA) Approach for Shariah Compliant Oil and Gas Sector in Malaysia. Journal of mathematics and computing science, 1 (1), 19-33.
[5] Adkins, L. C., Gade, M. N. (2012). Monte Carlo Experiments Using Stata: A Primer with Examples, in Dek Terrell, Daniel Millimet (ed.) 30th Anniversary Edition (Advances in Econometrics, Volume 30) Emerald Group Publishing Limited, pp.429 – 477.
[6] Akhtar, S. and Khan, N.U. (2016). Modeling volatility on the Karachi Stock Exchange Pakistan. Journal of Asia Business Studies, 10 (3), 253 – 275.
[7] Bahlous, M., Mohd. Yusof, R. (2014). International diversification among Islamic investments: is there any benefit. Managerial Finance, 40(6): 613-633.
[8] Coskun, Y. and Ertugrul, H.M. (2016). House price return volatility patterns in Turkey, Istanbul, Ankara and Izmir. Journal of European Real Estate Research, 9 (1), 26-51.
[9] Floros, C. and Enrique Salvador, E (2016). Volatility, trading volume and open interest in futures markets. International Journal of Managerial Finance, 12 (5), 629 – 653.
[10] Ghiani, E., Locci, N., Muscas, C. and Sulis, S. (2004). Uncertainty estimation for DSP‐based power quality measurements. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 23(1), 92-103.
[11] Ismal, R. (2010). Volatility of the returns and expected losses of Islamic bank financing. International Journal of Islamic and Middle Eastern Finance and Management, 3(3), 267 – 279.
[12] Kongsilp, W. and Mateus, C. (2017). Volatility risk and stock return predictability on global financial crises. China Finance Review International, 7 (1), 33-66.
[13] Messis, P. and Zapranis, A. (2014). Herding behaviour and volatility in the Athens Stock Exchange. The Journal of Risk Finance, 15(5), 572-590.
[14] Mohd Thas Thaker, H., Mohamad, A., Mustaffa Kamil, N.K. and Duasa, J. (2018). Information content and informativeness of analysts’ report: evidence from Malaysia. Journal of Financial Reporting and Accounting, 16(4), 742-763.
[15] Prakash, A. and Mohanty, R.P. (2017). DEA and Monte Carlo simulation approach towards green car selection. Benchmarking: An International Journal, 24(5), 234-1252.
[16] Siddikee, M. N. and Begum, N. N. (2016). Volatility of Dhaka Stock Exchange. International Journal of Economics and Finance, 8(5), pp. 220-229.
[17] Watzenig, D., Neumayer, M. and Fox, C. (2011). Accelerated Markov chain Monte Carlo sampling in electrical capacitance tomography. COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, 30(6), 1842-1854.