Which Model Performs Better While Forecasting Stock Market Volatility? Answer for Dhaka Stock Exchange (DSE)
- 1 Department of Economics, University of Dhaka, Dhaka, Bangladesh
- 2 Department of Economics, University of Dhaka, Dhaka, Bangladesh
- 3 Department of Accounting & Information Systems, University of Dhaka, Dhaka, Bangladesh
- 4 Department of Development Studies, University of Dhaka, Dhaka, Bangladesh
Abstract
An efficient and well behaved capital market can be regarded as a prerequisite for the sustainable financial development for an economy. For making the stock market efficient and reducing uncertainty, volatility measure is necessary for the policy makers. The main objective of this paper is to examine relative ability of various models to forecast future volatility and to devise appropriate volatility model for capturing variability in stock returns of Dhaka Stock Exchange (DSE). By exploiting daily data spanning from 27 th November, 2001 to 31 st July, 2013, it was found that, from volatility persistency perspective MA(2)-GARCH(2, 1) is better due to both in sample and out of sample accuracy. In contrast, from capturing asymmetric effect perspective MA(2)-EGARCH(1, 3) is better. Thus, there was no clear winner and hence the decision should depend on the purpose of the concerned people.
- Rajni, M. and Mahendra, R. (2007) Measuring Stock Market Volatility in an Emerging Economy. International Research Journal of Finance & Economics, 8, 126-133.
- Zuliu, H. (1995) Stock Market Volatility and Corporate Investment. IMF Working Paper, 95-102.
- Levine, R. and Zervous, S. (1996) Stock Market Development and Long-Run Growth. World Bank Economic Review, 10, 323-339. https://doi.org/10.1093/wber/10.2.323
- Olowe, R.A. (2009) Stock Return, Volatility & the Global Financial Crisis in an Emerging Market: The Nigerian Case. International Review of Business Research Papers, 5, 426-447.
- Olowe, R.A. (2009) The Impact of the Announcement of the 2005 Capital Requirement for Insurance Companies on the Nigerian Stock Market. The Nigerian Journal of Risk and Insurance, 6, 43-69.
- Chowdhury, A.R. (1994) Statistical Properties of Daily Returns from the Dhaka Stock Exchange. The Bangladesh Development Studies, 22, 61-76.
- Basher, S.A., Hassan, M.K. and Islam, A.M. (2007) Time-Varying Volatility and Equity Returns in Bangladesh Stock Market. Applied Financial Economics, 17, 1393-1407. https://doi.org/10.1080/09603100600771034
- Rayhan, M.A., Sarker, S.A. and Sayem, S.M. (2011) The Volatility of Dhaka Stock Exchange (DSE) Returns: Evidence and Implications. ASA University Review, 5, 97-99.
- Rahman, M.M., Huq, M.M. and Rahman, M.S. (2012) In Sample and Out of Sample Forecasting Performance under Fat Tail and Skewed Distribution. Proceeding Book of International Conference on Statistical Data Mining for Bioinformatics, Health, Agricultural and Environment, Department of Statistics, University of Rajshahi, December 2012, 462-472.
- Islam, M., Ali, L.E. and Afroz, N. (2012) Forecasting Volatility of Dhaka Stock Exchange: Linear Vs Non-Linear Models. International Journal of Science and Engineering, 3, 4-8.
- Huq, M.M., Rahman, M.M., Rahman, M.S., Shahin, M.M. and Ali, M. (2013) Analysis of Volatility and Forecasting General Index of Dhaka Stock Exchange. American Journal of Economics, 3, 229-242.
- Alam, M.Z., Siddikee, M.N. and Masukujjaman, M. (2013) Forecasting Volatility of Stock Indices with ARCH Model. International Journal of Financial Research, 4, 126-143. https://doi.org/10.5430/ijfr.v4n2p126
- Aziz, M.S. and Uddin, M.N. (2014) Volatility Estimation in the Dhaka Stock Exchange (DSE) Returns by Garch Models. Asian Business Review, 4, 41-49. https://doi.org/10.18034/abr.v4i1.72