Forecasting Value-at-Risk Using the Markov-Switching ARCH Model
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- Robert F. Engle & Simone Manganelli, 2004.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
- Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
- Ang, Andrew & Bekaert, Geert, 2002.
"Regime Switches in Interest Rates,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
- Andrew Ang & Geert Bekaert, 1998. "Regime Switches in Interest Rates," NBER Working Papers 6508, National Bureau of Economic Research, Inc.
- Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993.
"On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks,"
Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
- Lawrence R. Glosten & Ravi Jagannathan & David E. Runkle, 1993. "On the relation between the expected value and the volatility of the nominal excess return on stocks," Staff Report 157, Federal Reserve Bank of Minneapolis.
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- Harvey,Andrew C., 2013.
"Dynamic Models for Volatility and Heavy Tails,"
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Cambridge University Press, number 9781107034723, October.
- Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024, October.
- Baele, Lieven, 2005.
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- L. Baele, 2003. "Volatility Spillover Effects in European Equity Markets," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/189, Ghent University, Faculty of Economics and Business Administration.
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More about this item
Keywords
Value-at-Risk; Switching-regime ARCH models;JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ETS-2004-10-30 (Econometric Time Series)
- NEP-FIN-2004-10-30 (Finance)
- NEP-RMG-2004-10-30 (Risk Management)
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