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Can Stock Price Fundamentals Properly be Captured?: Using Markov Switching in Heteroskedasticity Models to Test Identification Schemes

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  • Anton Velinov

Abstract

Structural identification schemes are of essential importance to vector autoregressive (VAR) analysis. This paper tests a commonly used structural parameter identification scheme to assess whether it can properly capture fundamental and non-fundamental shocks to stock prices. In particular, five related structural models, which are widely used in the literature on assessing stock price determinants are considered. They are either specified in vector error correction (VEC) or in VAR form. Restrictions on the long-run effects matrix are used to identify the structural parameters. These identifying restrictions are tested by means of a Markov switching in heteroskedasticity model. It is found that for two of the five models considered, the long-run identification scheme appropriately classifies shocks as being either fundamental or non-fundamental. A series of robustness tests are performed, which largely confirm the initial findings.

Suggested Citation

  • Anton Velinov, 2013. "Can Stock Price Fundamentals Properly be Captured?: Using Markov Switching in Heteroskedasticity Models to Test Identification Schemes," Discussion Papers of DIW Berlin 1350, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1350
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    References listed on IDEAS

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    1. Rapach, David E., 2001. "Macro shocks and real stock prices," Journal of Economics and Business, Elsevier, vol. 53(1), pages 5-26.
    2. Lee, Bong-Soo, 1998. "Permanent, Temporary, and Non-Fundamental Components of Stock Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(01), pages 1-32, March.
    3. Laopodis, Nikiforos T., 2009. "Are fundamentals still relevant for European economies in the post-Euro period?," Economic Modelling, Elsevier, vol. 26(5), pages 835-850, September.
    4. Saikkonen, Pentti & Lutkepohl, Helmut, 2000. "Testing for the Cointegrating Rank of a VAR Process with Structural Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 451-464, October.
    5. Lanne, Markku & Lütkepohl, Helmut & Maciejowska, Katarzyna, 2010. "Structural vector autoregressions with Markov switching," Journal of Economic Dynamics and Control, Elsevier, vol. 34(2), pages 121-131, February.
    6. Binswanger, Mathias, 2000. "Stock market booms and real economic activity: Is this time different?," International Review of Economics & Finance, Elsevier, vol. 9(4), pages 387-415, October.
    7. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    8. Bénédicte Vidaillet & V. D'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    9. Herwartz, Helmut & Lütkepohl, Helmut, 2014. "Structural vector autoregressions with Markov switching: Combining conventional with statistical identification of shocks," Journal of Econometrics, Elsevier, vol. 183(1), pages 104-116.
    10. Nicolaas Groenewold, 2004. "Fundamental share prices and aggregate real output," Applied Financial Economics, Taylor & Francis Journals, vol. 14(9), pages 651-661.
    11. Zacharias Psaradakis & Nicola Spagnolo, 2006. "Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 753-766, September.
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    Cited by:

    1. Lütkepohl, Helmut & Netšunajev, Aleksei, 2017. "Structural vector autoregressions with heteroskedasticity: A review of different volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 2-18.

    More about this item

    Keywords

    Markov switching model; vector autoregression; vector error correction; heteroskedasticity; stock prices;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models

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