Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico
Journal of Public Policy and Administration
Volume 2, Issue 3, September 2018, Pages: 32-39
Received: Oct. 8, 2018; Accepted: Oct. 29, 2018; Published: Nov. 27, 2018
Views 737      Downloads 84
Vina Javed Khan, Department of Commerce, University of the Punjab, Gujranwala, Pakistan
Abdul Qadeer, Department of Finance, National University of Modern Languages, Lahore, Pakistan
Bezon Kumar, Department of Economics, Varendra University, Rajshahi, Bangladesh
Article Tools
Follow on us
Understanding and modeling the volatility measurements is important for forecasting the risk and for evaluating asset allocation decisions of stock market. The study have used the daily frequency data from January 1, 2002 to September 30, 2016 as an in-sample period to perform empirical analyses for modeling and predicting the volatility dynamics of Mexican stock market (IPC). To facilitate the variance forecast, the competing models are ARCH (p, q), GARCH (p, q), and its variations i.e. Glosten Jagnnathon Runkle GARCH, GARCH in Mean, Exponential GARCH, and Quadratic GARCH. The results of residual diagnostics suggested that stock market of Mexico is characterized by heteroskedasticity, multicolinearity, non-normality, and serial correlation. Volatility measurements by ARCH and GARCH signify that the current conditional variance of Mexico is determined by its past price behavior and previous day volatility. Today’s volatility does impact the current stock returns as indicated by GARCH-M. Results of EGARCH explained that any large size news produces high volatility as compared to small size news. Effects of bad news are greater on the volatility of the Mexican stock market than good news. GJR GARCH described the asymmetric behavior of returns and variance in the politically conflicted regime during 2006-2012. Moreover, QGARCH effect is not linear. Findings have the implications for individuals and corporate investors about retaining their risky stocks.
Volatility, ARCH Family, Mexican Stock Market
To cite this article
Vina Javed Khan, Abdul Qadeer, Bezon Kumar, Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico, Journal of Public Policy and Administration. Vol. 2, No. 3, 2018, pp. 32-39. doi: 10.11648/j.jppa.20180203.13
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abu-Mostafa, Y. S., and Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence 6 (3), 205-213.
Sánchez A, V. D. (2002). Frontiers of research in BSS/ICA. Neurocomputing 49 (1-4), 7-23.
Figlewski, S. (1997). Forecasting volatility. Financial markets, institutions and instruments 6 (1), 1-88.
Markowitz, H. (1952). Portfolio selection. The journal of finance 7 (1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance 19 (3), 425-442.
Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The Review of Economics and Statistics47 (1), 13-37.
Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica 34 (4), 768-783.
Black, F. (1976). Studies of stock price volatility changes. Proceedings of the 1976 Meeting of the Business and Economic Statistics Section, American Statistical Association 171-181.
Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business 36 (4), 394-419.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (4), 987-1007.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics 3 1(3), 307-327.
Taylor, S. J. (l986). Modeling financial time series. Chichester, UK: John Wiley and Sons.
Nelson, D. B. (1991). Conditional heteroscedasticity in asset returns: A new approach. Econometrica59 (2), 347-370.
Glosten, L. R., Jagannathan, R., and Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance 48 (5), 1779-1801.
Engle, R. F., and Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance 48 (5), 1749-1778.
Engle, R. F. (1990). Stock volatility and the crash of 87: Discussion. The Review of Financial Studies 3 (1), 103-106.
Brooks, C., and Burke, S. P. (1998). Forecasting exchange rate volatility using conditional variance models selected by information criteria. Economics Letters 61 (3), 273-278.
Brailsford, T. J., and Faff, R. W. (1996). An evaluation of volatility forecasting techniques. Journal of Banking and Finance 20 (3), 419-438.
McMillan, D., Speight, A., and Apgwilym, O. (2000). Forecasting UK stock market volatility. Applied Financial Economics 10 (4), 435-448.
Franses, P. H., and Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting 15 (1), 1-9.
Wei, W. (2002). Forecasting stock market volatility with non-linear GARCH models: a case for China. Applied Economics Letters 9 (3), 163-166.
Pandey, A. (2002). Extreme-value volatility estimators and their empirical performance in Indian capital markets. NSE Working Paper No. 52.
Miron, D., and Tudor, C. (2010). Asymmetric conditional volatility models: Empirical estimation and comparison of forecasting accuracy. Romanian Journal of Economic Forecasting 9 (3), 74-92.
Claessen, H., and Mittnik, S. (2002). Forecasting stock market volatility and the informational efficiency of the DAX-index options market. The European Journal of Finance 8 (3), 302-321.
Su, C. (2010). Application of EGARCH model to estimate financial volatility of daily returns: The empirical case of China (Unpublished master’s thesis). University of Gothenburg, Swedan.
Oskooe, S. A. P., and Shamsavari, A. (2011). Asymmetric effects in emerging stock markets-The case of Iran stock market. International Journal of Economics and Finance 3 (6), 16-24.
Mishra, P. K. (2010). A GARCH model approach to capital market volatility: The case of India. Indian Journal of Economics and Business9 (3), 631-641.
Abdalla, S. Z. S., and Suliman, Z. (2012). Modelling stock returns volatility: Empirical evidence from Saudi stock exchange. International Research Journal of Finance and Economics 85, 166-179.
Ahmed, A. E. M., and Suliman, S. Z. (2011). Modeling stock market volatility using GARCH models evidence from Sudan. International Journal of Business and Social Science 2 (23).
Chowdhury, A., and Ratan, S. (2012). Estimating Dhaka stock market volatility: A comparison between standard and asymmetric GARCH models. ABAC Journal 32 (2), 63-70.
Arshad, I., Rani, H., and Shaikh, A. W. (2012). Volatility modeling of Karachi stock exchange. Sindh University Research Journal-SURJ (Science Series) 44 (1), 125-130.
Wong, Y. C., and Kok, K. L. (2005). A comparison forecasting models for ASEAN equity markets. Sunway Academic Journal 2, 1-12.
Kuen, T. Y., and Hoong, T. S. (1992). Forecasting volatility in the Singapore stock market. Asia Pacific Journal of Management 9 (1), 1-13.
AbdElaal, M. A. (2011). Modeling and forecasting time varying stock return volatility in the Egyptian stock market. International Research Journal of Finance and Economics78 (2011), 96-113.
Alberg, D., Shalit, H., and Yosef, R. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics 18 (15), 1201-1208.
Vortilinos, D. (2017). Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH. Research in international business and finance 39, 824-839.
Hufbauer, G. C., and Schott, J. J. (1993). NAFTA: An assessment. Washington, D. C.: Institute for International Economics.
O'neill, J. (2011). The Growth Map: Economic opportunity in the BRICs and beyond. New York: Penguin.
Poon, S. H. (2005). A practical guide to forecasting financial market volatility. Chi Chester, UK: John Wiley and Sons.
Engle, R. F., Lilien, D., and Robins, R. (1987). Estimating time varying risk premier in the term structure: The Arch-M model. Econometrica 55 (2), 391-407.
Enders, W. (2008). Applied econometric time series. USA: John Wiley and Sons.
Negroponte, D. V. (Ed.). (2013). The end of nostalgia: Mexico confronts the challenges of global competition. Washington, DC: Brookings Institution Press.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186