Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks
Download full text from publisher
References listed on IDEAS
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003.
"Choosing the Best Volatility Models: The Model Confidence Set Approach,"
Oxford Bulletin of Economics and Statistics,
Department of Economics, University of Oxford, vol. 65(s1), pages 839-861, December.
- Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the best volatility models: the model confidence set approach," FRB Atlanta Working Paper 2003-28, Federal Reserve Bank of Atlanta.
- Peter Hansen & Asger Lunde & James M. Nason, 2003. "Choosing the Best Volatility Models:The Model Confidence Set Approach," Working Papers 2003-05, Brown University, Department of Economics.
- Schwert, G William, 1990. "Stock Volatility and the Crash of '87," Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 77-102.
- Asger Lunde & Peter R. Hansen, 2005.
"A forecast comparison of volatility models: does anything beat a GARCH(1,1)?,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
- Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
- Doornik, Jurgen A. & Ooms, Marius, 2008. "Multimodality in GARCH regression models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 432-448.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
- Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
- Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
- 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.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-144, January.
- Cukierman, Alex & Meltzer, Allan H, 1986. "A Theory of Ambiguity, Credibility, and Inflation under Discretion and Asymmetric Information," Econometrica, Econometric Society, vol. 54(5), pages 1099-1128, September.
- Ball, Laurence, 1992.
"Why does high inflation raise inflation uncertainty?,"
Journal of Monetary Economics,
Elsevier, vol. 29(3), pages 371-388, June.
- Laurence Ball, 1990. "Why Does High Inflation Raise Inflation Uncertainty?," NBER Working Papers 3224, National Bureau of Economic Research, Inc.
- Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Papers 2005-W24, Economics Group, Nuffield College, University of Oxford.
- Menelaos Karanasos & J. Kim, "undated". "Alternative GARCH in Mean Models: An Application to the Korean Stock Market," Discussion Papers 00/25, Department of Economics, University of York.
- Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
- Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
More about this item
Keywordsnonlinear GARCH; GARCH-in-Mean-Level effect; country risk; fear of disruption; forecast performance;
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ETS-2005-11-09 (Econometric Time Series)
- NEP-FMK-2005-11-09 (Financial Markets)
- NEP-FOR-2005-11-09 (Forecasting)
StatisticsAccess and download statistics
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpem:0509006. 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: (EconWPA). General contact details of provider: https://econwpa.ub.uni-muenchen.de .
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.