Can fear beat hope? A story of GARCH-in-Mean-Level effects for Emerging Market Country Risks
Upon winning the 2002 presidential elections, event that considerably increased the Brazilian country risk levels and volatility, Lula celebrated by declaring: “hope has beaten fear”. Extending Une and Portugal (2004), the aim of this paper is twofold: to empirically test the interrelations between country risk conditional mean (“hope”) and conditional variance (“fear”) and cast light on the role of country risk stability in the conduction of macroeconomic policies in developing small open economies. We compare the forecasting performance of various alternative GARCH-in-Mean-Level models for n-step conditional volatility point forecasts of the Brazilian country risk estimated for the period May 1994 - February 2005. The results support the idea that both hope and fear play important roles in the Brazilian case and confirms that hope and fear act in the same direction.
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- Schwert, G.W., 1989.
"Stock Volatility And The Crash Of '87,"
89-01, Rochester, Business - General.
- 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.
- 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.
- 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.
- Jurgen A. Doornik & Marius Ooms, 2003.
"Multimodality in the GARCH Regression Model,"
2003-W20, Economics Group, Nuffield College, University of Oxford.
- Jurgen Doornik & Marius Ooms, 2005.
"Outlier Detection in GARCH Models,"
Economics Series Working Papers
2005-W24, University of Oxford, Department of Economics.
- Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Economics Papers 2005-W24, Economics Group, Nuffield College, University of Oxford.
- Jurgen A. Doornik & Marius Ooms, 2005. "Outlier Detection in GARCH Models," Tinbergen Institute Discussion Papers 05-092/4, Tinbergen Institute.
- 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.
- Laurence Ball, 1990.
"Why Does High Inflation Raise Inflation Uncertainty?,"
NBER Working Papers
3224, National Bureau of Economic Research, Inc.
- Ball, Laurence, 1992. "Why does high inflation raise inflation uncertainty?," Journal of Monetary Economics, Elsevier, vol. 29(3), pages 371-388, 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 254-59, April.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Peter Hansen & Asger Lunde & James M. Nason, 2003.
"Choosing the Best Volatility Models:The Model Confidence Set Approach,"
2003-05, Brown University, Department of Economics.
- 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," Working Paper 2003-28, Federal Reserve Bank of Atlanta.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
- Menelaos Karanasos & J. Kim, . "Alternative GARCH in Mean Models: An Application to the Korean Stock Market," Discussion Papers 00/25, Department of Economics, University of York.
- 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.
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