IDEAS home Printed from https://ideas.repec.org/a/eoq/bjbmjr/art37.html

Modeling and Predicting Stock Market Volatility using ARCH Model: Evidence from Bhutanese Stock Market

Author

Listed:
  • Darjay, Sonam

Abstract

The stock market is widely known for its high degree of volatility, making it more challenging for individuals to accurately model and predict it in real-time. In an attempt to forecast the stock price and its volatility, investors, stock market analysts and academicians have been developing models for quite some time. One such model, the Autoregressive Conditional Heteroskedasticity (ARCH) model, developed by Engle (1982), has found wider applications in time series analysis where the central issue lies with the volatility clustering phenomenon. This paper employs the ARCH model as an econometric model to forecast volatility of the share price of three companies listed on the 2Royal Securities Exchange of Bhutan, namely Sherza Ventures Limited (SVL), Bhutan Insurance Limited (BIL), and Bhutan National Bank Limited (BNB). For this study, we statistically tested the time series share price return data from January 2019 to December 2022 for stationarity using Augmented Dickey-Fuller unit root test and for conditional heteroscedasticity effect using the ARCH Lagrange Multiplier test before modelling the ARCH model. To model the ARCH model, we selected the optimal order of the model, q, using the partial autocorrelation function of the squared residuals. The ARCH (1) model provided the best fit, resulting in accurate forecasts of the volatility of the stock price return. Overall, our ARCH (1) model performed very well with a mean absolute error and root mean squared error of 0.02628 and 0.03139, respectively, for the volatility of SVL return, 0.01523 and 0.01943 for BIL return, and 0.01395 and 0.01666 for BNB return. The study is expected to benefit stock market investors for making a sound investment decision thorough their knowledge about the volatility of return. Also, it would enable the stock market regulators to develop effective regulations and policies to promote market efficiency and investor confidence. Additionally, the study may shed light to future researcher and academicians for potential future research on applications of the ARCH model beyond stock market.

Suggested Citation

  • Darjay, Sonam, 2023. "Modeling and Predicting Stock Market Volatility using ARCH Model: Evidence from Bhutanese Stock Market," Bhutan Journal of Business and Management, Gedu College of Business Studies, Royal University of Bhutan.
    • Handle: RePEc:eoq:bjbmjr:art37
    DOI: 10.17102/bjbm.v6.2
    as

    Download full text from publisher

    File URL: https://bjbm.gcbs.edu.bt/index.php/bjbm/article/view/37
    Download Restriction: no
    File URL: https://libkey.io/10.17102/bjbm.v6.2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Lama, A. & Jha, G.K. & Paul, R.K. & Gurung, B., 2015. "Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(01).
    4. Engle, Robert F, 1983. "Estimates of the Variance of U.S. Inflation Based upon the ARCH Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(3), pages 286-301, August.
    5. Stavros Degiannakis, 2004. "Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model," Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Tirngo Dinku & Gardachew Worku, 2022. "Asymmetric GARCH models on price volatility of agricultural commodities," SN Business & Economics, Springer, vol. 2(11), pages 1-17, November.
    3. Philipp Otto & Osman Dou{g}an & Suleyman Tac{s}p{i}nar & Wolfgang Schmid & Anil K. Bera, 2023. "Spatial and Spatiotemporal Volatility Models: A Review," Papers 2308.13061, arXiv.org.
    4. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    5. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    6. Nico Knuth & Andreas Nastansky, 2025. "Anwendung von Deep Learning in der Prognose der Volatilität des DAX: Ein Vergleich der Prognosegüte von GARCH und LSTM," Statistische Diskussionsbeiträge 59, Universität Potsdam, Wirtschafts- und Sozialwissenschaftliche Fakultät.
    7. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    8. Altaf Muhammad & Zhang Shuguang, 2015. "Impact Of Structural Shifts on Variance Persistence in Asymmetric Garch Models: Evidence From Emerging Asian and European Markets," Romanian Statistical Review, Romanian Statistical Review, vol. 63(1), pages 57-70, March.
    9. Ding, Jing & Jiang, Lei & Liu, Xiaohui & Peng, Liang, 2023. "Nonparametric tests for market timing ability using daily mutual fund returns," Journal of Economic Dynamics and Control, Elsevier, vol. 150(C).
    10. Brooks, Robert, 2007. "Power arch modelling of the volatility of emerging equity markets," Emerging Markets Review, Elsevier, vol. 8(2), pages 124-133, May.
    11. Zou, Yongjie & Li, Honggang, 2014. "Time spans between price maxima and price minima in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 303-309.
    12. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    13. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    14. Charles, Amélie, 2010. "The day-of-the-week effects on the volatility: The role of the asymmetry," European Journal of Operational Research, Elsevier, vol. 202(1), pages 143-152, April.
    15. Bachar Fakhry & Christian Richter, 2018. "Does the Federal Constitutional Court Ruling Mean the German Financial Market is Efficient?," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 4(2), pages 111-125.
    16. Sang Hoon Kang & Seong-Min Yoon, 2010. "Sudden Changes and Persistence in Volatility of Korean Equity Sector Returns," Korean Economic Review, Korean Economic Association, vol. 26, pages 431-451.
    17. Tak Siu & John Lau & Hailiang Yang, 2007. "On Valuing Participating Life Insurance Contracts with Conditional Heteroscedasticity," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 14(3), pages 255-275, September.
    18. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    19. Chuong Luong & Nikolai Dokuchaev, 2018. "Forecasting of Realised Volatility with the Random Forests Algorithm," JRFM, MDPI, vol. 11(4), pages 1-15, October.
    20. Alistair Mees & Berndt Pilgram, 2000. "Non-Linear Markov Modelling Using Canonical Variate Analysis: Forecasting Exchange Rate Volatility," Econometric Society World Congress 2000 Contributed Papers 1162, Econometric Society.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eoq:bjbmjr:art37. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tshewang Phuntsho (email available below). General contact details of provider: https://edirc.repec.org/data/bsrubbt.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.