Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa
This paper investigates the relative performance of the asymmetric normal mixture generalized autoregressive conditional heteroskedasticity (NM-GARCH) and the benchmarked GARCH models with the daily stock market returns of the Johannesburg Stock Exchange, South Africa. The predictive performance of the NMGARCH model is compared against a set of the GARCH models with the normal, the Student-t, and the skewed Student-t distributions. The empirical results show that the NM-GARCH outperforms all other competing models according to Christoffersen’s (1998) tail-loss and White’s (2000) reality check tests. This evidence shows that mixture of errors improves the predictive performance of volatility models.
Volume (Year): (2012)
Issue (Month): 2 (June)
|Contact details of provider:|| Postal: |
Phone: 004 021 3188148
Fax: 004 021 3188148
Web page: http://www.ipe.ro/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- David McMillan & Pako Thupayagale, 2008. "Efficiency of the South African equity market," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 4(5), pages 327-330.
- Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992.
"Efficient Tests for an Autoregressive Unit Root,"
NBER Technical Working Papers
0130, National Bureau of Economic Research, Inc.
- Bollerslev, Tim & Ole Mikkelsen, Hans, 1996.
"Modeling and pricing long memory in stock market volatility,"
Journal of Econometrics,
Elsevier, vol. 73(1), pages 151-184, July.
- Tom Doan, . "RATS program to replicate Bollerslev-Mikkelson(1996) FIEGARCH models," Statistical Software Components RTZ00173, Boston College Department of Economics.
- Keith Jefferis & Pako Thupayagale, 2008. "Long Memory In Southern African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 384-398, 09.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Keith Jefferis & Graham Smith, 2005. "The Changing Efficiency Of African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 73(1), pages 54-67, 03.
- Serena Ng & Pierre Perron, 2001.
"LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power,"
Econometric Society, vol. 69(6), pages 1519-1554, November.
- Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
- Cifter, Atilla, 2011. "Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2356-2367.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, . "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
- Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
- Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
- repec:cor:louvrp:2414 is not listed on IDEAS
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Cifter, Atilla & Ozun, Alper, 2007.
"The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey,"
2489, University Library of Munich, Germany.
- Atilla Çifter & Alper Özün, 2007. "The Predictive Performance of Asymmetric Normal Mixture GARCH in Risk Management: Evidence from Turkey," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 1(1), pages 7-34.
- Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
- Appiah-Kusi, Joe & Menyah, Kojo, 2003. "Return predictability in African stock markets," Review of Financial Economics, Elsevier, vol. 12(3), pages 247-270.
- Leonardo Souza & Alvaro Veiga & Marcelo C. Medeiros, 2002. "Evaluating the performance of GARCH models using White´s Reality Check," Textos para discussão 453, Department of Economics PUC-Rio (Brazil).
- Cathy W. S. Chen & Mike K. P. So & Edward M. H. Lin, 2009. "Volatility forecasting with double Markov switching GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 681-697.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Carol Alexandra & Emese Lazar, 2003. "Symmetric Normal Mixture GARCH," ICMA Centre Discussion Papers in Finance icma-dp2003-09, Henley Business School, Reading University.
- Carol Alexander & Emese Lazar, 2009. "Modelling Regime-Specific Stock Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(6), pages 761-797, December.
- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
- Gonzalez-Rivera, Gloria & Lee, Tae-Hwy & Mishra, Santosh, 2004. "Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood," International Journal of Forecasting, Elsevier, vol. 20(4), pages 629-645.
- LAURENT, Sébastien & VIOLANTE, Francesco, . "Volatility forecasts evaluation and comparison," CORE Discussion Papers RP -2414, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling commodity value at risk with higher order neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 20(7), pages 585-600.
When requesting a correction, please mention this item's handle: RePEc:rjr:romjef:v::y:2012:i:2:p:127-142. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Corina Saman)
If references are entirely missing, you can add them using this form.