International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
EISSN: 2313-3724
Print ISSN: 2313-626X
Volume 4, Issue 4 (April 2017), Pages: 127-132
Title: A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model
Author(s): Azeem Ahmad Khan 1, *, Sarfaraz Javed 2
Affiliation(s):
1Department of Commerce, Gagan College of Management & technology, Aligarh, UP, India
2Department of Management, JIT, Lucknow, UP, India
https://doi.org/10.21833/ijaas.2017.04.018
Abstract:
The purpose of this study is to know that how the National and International market, namely (S&P BSE SENSEX) (NASDAQ) (SSE) (FTSE) can influence the volatility of (S&P BSE BANKEX) return in India and the factors affecting the volatility for the same. However, the previous studies mostly considered the volatility of stock in the Indian capital market. But the present study mainly focuses on the Bankex return volatility. Here the researcher identified and estimated the mean and variance components of the daily Bankex return using Garch (1, 1) model by explaining the volatility structure of the residuals obtained under the best-suited model for the used data series. The method ML - ARCH (Marquardt) - Normal distribution has satisfied the criterion of model selection based on the three assumptions. These Null Hypotheses deals with the problems firstly, no serial correlation, secondly, the presence of no ARCH effect, thirdly, residual are normally distributed. We have chosen daily data period from 03rd May, 2012 to 08th January 2016, nearly 914 working days of all markets for estimating Arch-Garch model. The study shows the significant result of ARCH and GARCH effect. The Bankex Return is also significantly affected by endogenous variable (SENSEX return). The NASDAQ composite and SSE composite Index are also statistically significant, In sum-up, the foreign market return volatility or outside shock can influence the volatility of BANKEX return. However, the FTSE 100 was not found statistically significant, revealing that volatility in FTSE return cannot transmit to S&P BSE BANKEX return in India.
© 2017 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Garch, Bankex, Sensex, NASDAQ, Volatility
Article History: Received 22 December 2016, Received in revised form 25 February 2017, Accepted 29 February 2017
Digital Object Identifier:
https://doi.org/10.21833/ijaas.2017.04.018
Citation:
Khan AA and Javed S (2017). A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model. International Journal of Advanced and Applied Sciences, 4(4): 127-132
http://www.science-gate.com/IJAAS/V4I4/Azeem.html
References:
Aggarwal R, Inclan C, and Leal R (1999). Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis, 34(01): 33-55. https://doi.org/10.2307/2676245 |
||||
Alberg D, Shalit H, and Yosef R (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15): 1201-1208. https://doi.org/10.1080/09603100701604225 |
||||
Birău R, Trivedi J, and Antonescu M (2015). Modeling S & P Bombay stock exchange BANKEX index volatility patterns using GARCH model. Procedia Economics and Finance, 32: 520-525. https://doi.org/10.1016/S2212-5671(15)01427-6 |
||||
Bollerslev T (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3): 307-327. https://doi.org/10.1016/0304-4076(86)90063-1 |
||||
Chand S, Kamal S, and Ali I (2012). Modelling and volatility analysis of share prices using ARCH and GARCH models. World Applied Sciences Journal, 19(1): 77-82. | ||||
Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4): 987-1007. https://doi.org/10.2307/1912773 |
||||
Engle RF and Ng VK (1993). Measuring and testing the impact of news on volatility. The Journal of Finance, 48(5): 1749-1778. https://doi.org/10.1111/j.1540-6261.1993.tb05127.x |
||||
French KR, Schwert GW, and Stambaugh RF (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1): 3-29. https://doi.org/10.1016/0304-405X(87)90026-2 |
||||
Goudarzi H and Ramanarayanan CS (2011). Modeling asymmetric volatility in the Indian stock market. International Journal of Business and Management, 6(3): 221-231. https://doi.org/10.5539/ijbm.v6n3p221 |
||||
Joshi P (2010). Modeling volatility in emerging stock markets of India and China. Journal of Quantitative Economics, 8(1): 86-94. | ||||
Karmakar M (2005). Modeling conditional volatility of the Indian stock markets. Vikalpa, 30(3): 21-38. https://doi.org/10.1177/0256090920050303 |
||||
Kaur H (2004). Time varying volatility in the Indian stock market. Vikalpa, 29(4): 25-42. https://doi.org/10.1177/0256090920040403 |
||||
Kulkarni V and Deo N (2005). Correlation and volatility in an Indian stock market: A random matrix approach. The European Physical Journal B, 60(1): 101 -109. https://doi.org/10.1140/epjb/e2007-00322-1 |
||||
Mandelbrot B (1963). The variation of certain speculative prices. The Journal of Business, 36(4): 394-419. https://doi.org/10.1086/294632 |
||||
Pandey A (2005). Volatility models and their performance in Indian capital markets. Vikalpa, 30(2): 27-46. https://doi.org/10.1177/0256090920050203 |
||||
Prasanna PK and Menon AS (2013). Speed of information adjustment in Indian stock indices. IIMB Management Review, 25(3): 150-159. https://doi.org/10.1016/j.iimb.2013.05.003 |
||||
Schwert GW (1989). Why does stock market volatility change over time?. The Journal of Finance, 44(5): 1115-1153. https://doi.org/10.1111/j.1540-6261.1989.tb02647.x |
||||
Trivedi JC (2013). Performance analysis of BANKEX banks through camel model. Management Dynamics, 13(2): 1-13. |