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A VECM Model of Stockmarket Returns

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  • Nagaratnam J Sreedharan

Abstract

Observations of security prices and other financial time series usually include not only the close (C), but also an open, a high and a low (O,H,L) price for a specified interval. The multivariate vector of values (H,L,O,C) is obviously more informative than just the open or close (O, C) for modelling volatilities and volatility predictions. In this paper we capture the return generation process of security prices by using all the quoted prices (H, L, O, C) via a vector error correction (VECM) model. The results of the empirical models using daily DJI index data for a 11 year period (1990-2000) indicate some interesting stylised facts regarding the market returns. We show, via the return generation process (RGP) proposed, that the "cointegrating returns" exhibit significant explanatory power. Some insights are also provided as to why logarithmic returns tend to be non-normally distrbuted

Suggested Citation

  • Nagaratnam J Sreedharan, 2004. "A VECM Model of Stockmarket Returns," Econometric Society 2004 Australasian Meetings 166, Econometric Society.
    • Handle: RePEc:ecm:ausm04:166
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    References listed on IDEAS

    as
    1. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    2. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    3. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
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    More about this item

    Keywords

    Cointegration (CI); VECM; VAR; return generation process (RGP).;
    All these keywords.

    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

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