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Estimation and evaluation of asset pricing models with habit formation using Philippine data

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  • Raymund Abara

Abstract

This study tests the habit-formation model, an extension of the consumption-based capital asset pricing model (C-CAPM). Using Philippine stock market data, seasonally adjusted and non-seasonally adjusted consumption datasets, the study tracks the performance of these resulting models in terms of forecast performance both in-sample and out-of-sample. Several statistical measures such as the Diebold-Mariano test and the success ratio test are used to compare these habit models against the standard power utility/C-CAPM, the random walk with drift model, and the traditional static CAPM. Based on the criteria set by this study, only the external habit model performs better than all the other models.

Suggested Citation

  • Raymund Abara, 2006. "Estimation and evaluation of asset pricing models with habit formation using Philippine data," Applied Economics Letters, Taylor & Francis Journals, vol. 13(8), pages 493-497.
  • Handle: RePEc:taf:apeclt:v:13:y:2006:i:8:p:493-497
    DOI: 10.1080/13504850500400611
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    References listed on IDEAS

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    1. Shigeyuki Hamori & Toshifumi Tokunaga, 1999. "Habit formation and durability and consumption: some evidence from income quintile groups in Japan," Applied Economics Letters, Taylor & Francis Journals, vol. 6(6), pages 397-402.
    2. Atsushi Maki & Tadashi Sonoda, 2002. "A solution to the equity premium and riskfree rate puzzles: an empirical investigation using Japanese data," Applied Financial Economics, Taylor & Francis Journals, vol. 12(8), pages 601-612.
    3. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    4. Ooms, Marius & Franses, Philip Hans, 1997. "On Periodic Correlations between Estimated Seasonal and Nonseasonal Components in German and U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 470-481, October.
    5. Ghysels, Eric, 1997. "Seasonal Adjustment and Other Data Transformations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 410-418, October.
    6. Yiuman Tse & Tatyana Zabotina, 2002. "Smooth transition in aggregate consumption," Applied Economics Letters, Taylor & Francis Journals, vol. 9(7), pages 415-418.
    7. Tony Wirjanto, 1997. "Aggregate consumption behaviour with time-nonseparable preferences and liquidity constraints," Applied Financial Economics, Taylor & Francis Journals, vol. 7(1), pages 107-114.
    8. Abel, Andrew B., 1999. "Risk premia and term premia in general equilibrium," Journal of Monetary Economics, Elsevier, vol. 43(1), pages 3-33, February.
    9. 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.
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    Cited by:

    1. Fusaro, Marc Anthony & Dutkowsky, Donald H., 2011. "What explains consumption in the very short-run? Evidence from checking account data," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 542-552.

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