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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
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    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.

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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.434681.de/dp1350.pdf
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    Bibliographic Info

    Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1350.

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    Length: 29 p.
    Date of creation: 2013
    Date of revision:
    Handle: RePEc:diw:diwwpp:dp1350

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

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    1. 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.
    2. Nicolaas Groenewold, 2004. "Fundamental share prices and aggregate real output," Applied Financial Economics, Taylor & Francis Journals, vol. 14(9), pages 651-661.
    3. Markku Lanne & Helmut Luetkepohl & Katarzyna Maciejowska, 2009. "Structural Vector Autoregressions with Markov Switching," Economics Working Papers ECO2009/06, European University Institute.
    4. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, September.
    5. Helmut Herwartz & Helmut Luetkepohl, 2011. "Structural Vector Autoregressions with Markov Switching: Combining Conventional with Statistical Identification of Shocks," Economics Working Papers ECO2011/11, European University Institute.
    6. Saikkonen, Pentti & Lütkepohl, Helmut, 2001. "Testing for the cointegrating rank of a VAR process with structural shifts," SFB 373 Discussion Papers 1998,82, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    7. 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.
    8. 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.
    9. Rapach, David E., 2001. "Macro shocks and real stock prices," Journal of Economics and Business, Elsevier, vol. 53(1), pages 5-26.
    10. 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, 09.
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