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A study on the volatility forecast of the US housing market in the 2008 crisis

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  • Kui-Wai Li

Abstract

This 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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File URL: http://hdl.handle.net/10.1080/09603107.2012.687096
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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 22 (2012)
Issue (Month): 22 (November)
Pages: 1869-1880

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Handle: RePEc:taf:apfiec:v:22:y:2012:i:22:p:1869-1880

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  1. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-84, September.
  2. 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.
  3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
  11. 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.
  12. 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 & Francis Journals, vol. 20(1-2), pages 137-150.
  13. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
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