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On Modelling Speculative Prices: The Empirical Literature

Author

Listed:
  • Elena Andreou
  • Nikitas Pittis
  • Aris Spanos

Abstract

Traditionally, financial theory and in particular asset pricing models have assumed (implicitly or explicitly) a certain probabilistic structure for speculative prices. The probabilistic structure is usually defined in terms of specific statistical models and relates to the dependence, heterogeneity and the distribution of such prices. The primary objective of this paper is to trace the development of various statistical models proposed since Bachelier (1900), in an attempt to assess how well these models capture the empirical regularities exhibited by data on speculative prices.

Suggested Citation

  • Elena Andreou & Nikitas Pittis & Aris Spanos, 2001. "On Modelling Speculative Prices: The Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 15(2), pages 187-220, April.
    • Handle: RePEc:bla:jecsur:v:15:y:2001:i:2:p:187-220
    DOI: 10.1111/1467-6419.00136
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    Cited by:

    1. Martins-Filho Carlos & Yao Feng, 2006. "Estimation of Value-at-Risk and Expected Shortfall based on Nonlinear Models of Return Dynamics and Extreme Value Theory," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-43, May.
    2. Dora Almeida & Andreia Dionísio & Muhammad Enamul Haque & Paulo Ferreira, 2022. "A Giant Falls: The Impact of Evergrande on Asian Stock Indexes," JRFM, MDPI, vol. 15(8), pages 1-14, July.
    3. Wally Tzara, 2018. "The Evolution of Security Prices Is Not Stochastic but Governed by a Physicomathematical Law," Papers 1807.10114, arXiv.org, revised Jul 2019.
    4. Scheicher, Martin, 2008. "How has CDO market pricing changed during the turmoil? Evidence from CDS index tranches," Working Paper Series 910, European Central Bank.
    5. Natália Costa & César Silva & Paulo Ferreira, 2019. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies," IJFS, MDPI, vol. 7(3), pages 1-12, September.
    6. Kostyantyn MALYSHENKO & Vadim MALYSHENKO & Elena Yu. PONOMAREVA & Marina ANASHKINA, 2019. "Analysis of the stock market anomalies in the context of changing the information paradigm," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 239-270, June.
    7. Paulo Ferreira & Luís Carlos Loures, 2020. "An Econophysics Study of the S&P Global Clean Energy Index," Sustainability, MDPI, vol. 12(2), pages 1-9, January.
    8. Wahbeeah Mohti & Andreia Dionísio & Paulo Ferreira & Isabel Vieira, 2019. "Frontier markets’ efficiency: mutual information and detrended fluctuation analyses," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 551-572, September.
    9. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    10. Sabino da Silva, Fernando A.B. & Ziegelmann, Flavio A. & Caldeira, João F., 2023. "A pairs trading strategy based on mixed copulas," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 16-34.
    11. Ekaterini Tsouma, 2007. "Stock return dynamics and stock market interdependencies," Applied Financial Economics, Taylor & Francis Journals, vol. 17(10), pages 805-825.

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