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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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    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.
    2. Lütkepohl, Helmut & Netšunajev, Aleksei, 2015. "Structural vector autoregressions with heteroskedasticity: A comparison of different volatility models," SFB 649 Discussion Papers 2015-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Justyna Wr'oblewska & {L}ukasz Kwiatkowski, 2024. "Identification of structural shocks in Bayesian VEC models with two-state Markov-switching heteroskedasticity," Papers 2406.03053, arXiv.org, revised Jun 2024.
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    5. Helmut Lütkepohl & Anton Velinov, 2016. "Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions Via Heteroskedasticity," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 377-392, April.
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    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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