A study on the volatility forecast of the US housing market in the 2008 crisis
AbstractThis article provides the in-sample estimation and evaluates the out of-sample conditional mean and volatility forecast performance of the conventional Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) and the benchmark RiskMetrics model on the US real estate finance data for the pre-crisis and post-crisis periods in 2008. The empirical results show that the RiskMetrics model performed satisfactorily in the in-sample estimation but poorly in the out-of-sample forecast. For the post-crisis out-of-sample forecasts, all models naturally performed poorly in conditional mean and volatility forecast.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 41033.
Date of creation: 2011
Date of revision:
Publication status: Published in Applied Financial Economics 22.22(2012): pp. 1869-1880
financial crisis; volatility forecast; US real estate finance;
Other versions of this item:
- Kui-Wai Li, 2012. "A study on the volatility forecast of the US housing market in the 2008 crisis," Applied Financial Economics, Taylor and Francis Journals, vol. 22(22), pages 1869-1880, November.
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
- L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
- R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
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.:
- Douglas Wong & Kui-Wai Li, 2010. "Comparing the performance of relative stock return differential and real exchange rate in two financial crises," Applied Financial Economics, Taylor and Francis Journals, vol. 20(1-2), pages 137-150.
- 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,"
157, Federal Reserve Bank of Minneapolis.
- 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.
- West, K.D., 1994.
"Asymptotic Inference About Predictive Ability,"
9417, Wisconsin Madison - Social Systems.
- Engle, Robert F & Ng, Victor K, 1993.
" Measuring and Testing the Impact of News on Volatility,"
Journal of Finance,
American Finance Association, vol. 48(5), pages 1749-78, December.
- Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc.
- John B. Taylor & John C. Williams, 2008.
"A black swan in the money market,"
Working Paper Series
2008-04, Federal Reserve Bank of San Francisco.
- John B. Taylor & John C. Williams, 2009. "A black swan in the money market," Proceedings, Federal Reserve Bank of San Francisco, issue Jan.
- John C. Williams & John B. Taylor, 2009. "A Black Swan in the Money Market," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 58-83, January.
- West, Kenneth D, 2001. "Tests for Forecast Encompassing When Forecasts Depend on Estimated Regression Parameters," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 29-33, January.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- 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.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
- French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
- Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
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