Volume 2, Issue 3, September 2018, Page: 32-39
Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico
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
Received: Oct. 8, 2018;       Accepted: Oct. 29, 2018;       Published: Nov. 27, 2018
DOI: 10.11648/j.jppa.20180203.13      View  20      Downloads  13
Abstract
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.
Keywords
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
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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