GARCH (1,1) as the Stochastic Underlying Process for Stock Market Returns : Empirical Evidence from Asian Markets

Authors

  •   Shivani Inder Research Scholar, Department of Commerce, Punjabi University, Patiala, Punjab
  •   J. S. Pasricha Dean Research & Professor, Department of Commerce, Punjabi University, Patiala, Punjab

Keywords:

GARCH (1

, 1), Conditional Variance, Persistence, Volatility, ARCH, Volatility Clustering

G12

, G14, G15, G17.

Abstract

Traditional econometric analysis assumes the financial time series as a random walk process with constant variance. However, returns from financial market variables exhibit the conditionally dependent behaviour which is autoregressive in nature and can be explained by the generally auto regressive conditionally heteroskedastic process. The present study focused on GARCH as the process explaining the behaviour for returns and volatility of indices of the stock markets of 10 Asian countries. This process was estimated by employing the GARCH (1,1) model on stock market returns. The time period considered is from January 2000 to July 2013. The GARCH(1,1) model was found to be a satisfactory model fitting the financial time series.

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Published

2014-12-01

How to Cite

Inder, S., & Pasricha, J. S. (2014). GARCH (1,1) as the Stochastic Underlying Process for Stock Market Returns : Empirical Evidence from Asian Markets. Indian Journal of Research in Capital Markets, 1(1), 27–38. Retrieved from https://indianjournalofmarketing.com/index.php/ijrcm/article/view/102687

Issue

Volume 1, Issue 1, October-December 2014

Section

Articles

References

Achia, T.N.O., Wangombe, A., & Anyika, E. (2013). Time-series modeling of returns from the NSE 20-share index: An empirical study of the impact of political climate on market volatility. Retrieved from : http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/38632

Aggarwal, R., Inclan, C., & Leal, R. (1999). Volatility in emerging stock markets. Journal of Financial and Quantitative Analysis, 34 (1), 33-55.

Andersen, T.G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39 (4), 885-905.

Ashley, R. A., & Patterson, D. M. (2010). A test of the GARCH (1,1) specification for daily stock returns. Macroeconomics Dynamics, 14 (1), 137-144. DOI: http://dx.doi.org/10.1017/S1365100510000015

Bekaert, G., & Harvey, C. R. (1997). Emerging market volatility. Journal of Financial Economics, 43 (1), 29-77.

Bera, A. K., & Higgins, M. L. (1993). ARCH models: Properties, estimation and testing. Journal of Economic Surveys, 7(4), 307-366.

Berkes, I., Horvath, L., & Kokoszka, P.S. (2003). GARCH processes: Structure and estimation. Bernoulli, 9 (2), 201-227.

Bhanja, N., Dar, A. B., & Samantaraya, A. (2013). Exchange rate volatility and export growth: Post reform experience of India. Indian Journal of Finance, 7 (9), 27-35.

Black, F. (1976). Studies of stock market volatility changes. Proceedings of the 1976 Business Meetings of the Business and Economic Statistic Section, American Statistical Association, 177 - 181.

Bollerslev, T. (1986). A generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307-327.

Bonilla, C.A., & Sepulveda, J. (2011) Stock returns in emerging markets and the use of GARCH models. Applied Economics Letters, 18 (14), 1321-1325. DOI:10.1080/13504851.2010.537615

Braun, P.A., Nelson, D. B., & Sunier, A. M. (1995). Good news, bad news, volatility, and betas. The Journal of Finance, 50 (5), 1575-1603. DOI: 10.1111/j.1540-6261.1995.tb05189.x

Brook, C., & Burke, S.P. (2003). Information criteria for GARCH model selection: An application to high frequency data. European Journal of Finance, 9 (6), 557- 580.

Campbell, J.Y., & Hentschel, L. (1992). No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31(3), 281-318. DOI:10.1016/0304-405X(92)90037-X

Emenike, K. O., (2010). Modelling stock returns volatility in Nigeria using GARCH models. MPRA Paper No. 23432. Retrieved from http://mpra.ub.uni-muenchen.de/23432/

Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of the United Kingdom inflation. Econometrica, 50 (4), 987-1007.

Fama, E. (1965).The behavior of stock market prices. Journal of Business, 38 (1), 34 - 105.

Frimpong, J.M., & Oteng-Abayie, E.F. (2006). Modelling and forecasting volatility of returns on the Ghana Stock Exchange using GARCH models. Retrieved from http://mpra.ub.uni-muenchen.de/593/

Fryzlewicz, P., (2007). Lecture notes: Financial time series, ARCH and GARCH models. University of Bristol. Retrieved from http://www.maths.bris.ac.uk/~mapzf/

Giraitis, L., Kokoszka, P., Leipus, R., & Teyssiere, G. (2003). Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics, 112 (2), 265 - 294.

Glosten, L. R., Jagannathan, & Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5),1779 - 1801. DOI: 10.1111/j.1540-6261.1993.tb05128.x

Goyal, A. (2000). Predictability of stock return volatility from GARCH models. Anderson Graduate School of Management, UCLA. Retrieved from http://www.hec.unil.ch/agoyal/docs/Garch.pdf

Hamilton, J.D. (1989). A new approach to the economic analysis of non - stationary time series and the business cycle. Econometrica, 57(2), 357-384.

Hsieh, D. A. (1989). Modelling heteroscedasticity in daily foreign - exchange rates. Journal of Business and Economic Statistics, 7(3), 307-317.

Jayasuriya, S. (2002). Does stock market liberalization affect the volatility of stock returns: Evidence from emerging market economies. Georgetown University Discussion Series.

Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business and Economic Statistics, 8 (2), 225-234.

Lunde, A., & Hansen, P. R. (2001). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Working Papers 2001-2004, Brown University, Department of Economics, Series No. 84, 1-41.

Mandelbrot, B. (1963).The variation of certain speculative prices. Journal of Business, 36 (4), 394-419.

Matei, M. (2009). Assessing volatility forecasting models: Why GARCH models take the Lead ? Romanian Journal of Economic Forecasting, 4(1), 42-65.

McMillan, D., Speight, A., & Apgwilym, O. (2000). Forecasting UK stock market volatility. Applied Financial Economics, 10 (4), 435-448. DOI:10.1080/09603100050031561

Murthy, I. K., Anupama, T., & Deepa, K. (2012). Forecasting gold prices using geometric random walk growth model. Indian Journal of Finance, 6 (9), 36-44.

Nelson, D. (1991).Conditional heteroscedasticity in asset returns: A new approach. Econometrica, 59 (2), 347-370.

Olowe, R.A. (2009). Modelling Naira/Dollar exchange rate volatility: Evidence from GARCH and asymmetric models. International Review of Business Research Papers, 5 (3), 377-398.

Starica, C. (2003). Is GARCH (1,1) as good a model as the accolades of the Nobel Prize would imply? DOI: http://dx.doi.org/10.2139/ssrn.637322

Taylor, S. J. (1994). Modeling stochastic volatility: A review and comparative study. Mathematical Finance, 4 (2),183 - 204.

Teräsvirta, T. (2009). An introduction to Univariate GARCH models. In, Handbook of financial time series (pp. 17 -42). Springer Berlin Heidelberg Publishing House. DOI : 10.1007/978-3-540-71297-8_1

Zakoian, J. - M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931 - 955.