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Non-linear Volatility and Dynamics of the Tunisian Stock Market

Received: 1 December 2013     Published: 30 December 2013
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Abstract

Measuring and controlling risk is one of the most attractive issues in finance. With the persistence of uncontrolled and erratic stocks movements, volatility is perceived as a barometer of daily fluctuations. An objective measure of this variable seems then needed to control risks and cover those that are considered the most important. Non-linear autoregressive modeling is our first evaluation approach. In particular, we test the presence of “persistence” of conditional variance and the presence of a degree of a leverage effect. In order to resolve for the problem of “asymmetry” in volatility, the retained specifications point to the importance of stocks reactions in response to news. Effects of shocks on volatility highlight also the need to study the “long term” behavior of conditional variance of stocks returns and articulate the presence of long memory and dependence of time series in the long run. We note that the integrated fractional autoregressive model allows for representing time series that show long-term conditional variance thanks to fractional integration parameters. In order to stop at the dynamics that manage time series, a comparative study of the results of the different models will allow for better understanding volatility structure over the Tunisia stock market, with the aim of accurately predicting fluctuation risks.

Published in International Journal of Economics, Finance and Management Sciences (Volume 2, Issue 1)
DOI 10.11648/j.ijefm.20140201.14
Page(s) 22-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Volatility, Asymmetry, Clustering, Stylized Facts, Leverage Effect

References
[1] R. Engle (1982), "Autoregressive conditional heteroscedasticity with estimates of the variance", Econometrica, vol 50 (4), pp 987-1007
[2] T. Bollerslev (1986), "A conditionally heteroscedastic time series model of security prices and rates of return data", Review of economics and statistics, vol 59, pp 542-547
[3] D. Nelson (1990), "ARCH models as diffusion approximations", Journal of econometrics vol 35, pp 7-38
[4] J.M. Zakoin (1994), "Threshold Heteroscedastic Models", Journal of Economic Dynamics and control, Vol 18, pp 931-955.
[5] R. Engle and G. Rivera (2006), "Semiparametric ARCH models", Journal of business and economic statistics, vol 9 (4), pp 345-359
[6] L Hentschell (1995), "All in the family: Nesting symmetric and asymmetric GARCH models", Journal of financial economics vol 39, pp 71-104.
[7] P. Hansen and A. Lunde (2005), "A Forecast comparaison of volatility models: does anything beat a GARCH (1.1)", Journal of Applied Econometrics; vol 20, pp 873-889.
[8] S. Ling and M. MacAleer (2000), "Testing GARCH versus EGARCH, Statistics and finance: An inference", Imperial college Press London, pp 226-242
[9] R. Engle and V. K. Ng (1993), "Measuring and testing the impact of News on volatility", Journal of Finance, vol 48, pp 1749-1778.
[10] T. Bollerslev and H.O Mikkeslen (1996), "Modeling and pricing memory in stock market volatility", Journal of Econometrics, vol 73, pp 151-184.
[11] B.Anderson and A. Lund (2002), An empirical investigation on Continuous time return equity models, Journal of Finance, vol 57, 1239-1284
[12] N.S. Hyung and C. Granger (2006), A source of long memory in volatility, working paper series
[13] P. G. Patev and N. k. kanaryan (2004) "Modelling and forcasting the volatility in central European stock market", working paper series
[14] W. G. Dean and R. W. Faff (2001), "Asymmetric covariance, volatility and the impact of news", working paper series
[15] G. Beakeart and G. Wu (2000), "Asymmetric volatility and Risk in Equity Markets", Review of Financial Studies, vol 13, pp 1-42.
[16] T. Bollerslev and J. M. Wooldrige (1992), "Quasi Maximum likelihood estimation and Inference in Dynamic models with Time-Varying Covariance", Econometric reviews, vol 11(2), pp 143-172.2.
[17] G. Beakeart and G. Wu (2000), "Asymmetric volatility and Risk in Equity Markets", Review of Financial Studies, vol 13, pp 1 -42.
[18] K. Bea and R. Nelson (2004), "Why are stock returns and volatility negatively correlated?" Journal of financial economics, vol 24, pp 124- 151.
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  • APA Style

    Kalai Lamia, Jilani Faouzi. (2013). Non-linear Volatility and Dynamics of the Tunisian Stock Market. International Journal of Economics, Finance and Management Sciences, 2(1), 22-32. https://doi.org/10.11648/j.ijefm.20140201.14

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    ACS Style

    Kalai Lamia; Jilani Faouzi. Non-linear Volatility and Dynamics of the Tunisian Stock Market. Int. J. Econ. Finance Manag. Sci. 2013, 2(1), 22-32. doi: 10.11648/j.ijefm.20140201.14

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    AMA Style

    Kalai Lamia, Jilani Faouzi. Non-linear Volatility and Dynamics of the Tunisian Stock Market. Int J Econ Finance Manag Sci. 2013;2(1):22-32. doi: 10.11648/j.ijefm.20140201.14

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  • @article{10.11648/j.ijefm.20140201.14,
      author = {Kalai Lamia and Jilani Faouzi},
      title = {Non-linear Volatility and Dynamics of the Tunisian Stock Market},
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {2},
      number = {1},
      pages = {22-32},
      doi = {10.11648/j.ijefm.20140201.14},
      url = {https://doi.org/10.11648/j.ijefm.20140201.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20140201.14},
      abstract = {Measuring and controlling risk is one of the most attractive issues in finance. With the persistence of uncontrolled and erratic stocks movements, volatility is perceived as a barometer of daily fluctuations. An objective measure of this variable seems then needed to control risks and cover those that are considered the most important. Non-linear autoregressive modeling is our first evaluation approach. In particular, we test the presence of “persistence” of conditional variance and the presence of a degree of a leverage effect. In order to resolve for the problem of “asymmetry” in volatility, the retained specifications point to the importance of stocks reactions in response to news. Effects of shocks on volatility highlight also the need to study the “long term” behavior of conditional variance of stocks returns and articulate the presence of long memory and dependence of time series in the long run. We note that the integrated fractional autoregressive model allows for representing time series that show long-term conditional variance thanks to fractional integration parameters. In order to stop at the dynamics that manage time series, a comparative study of the results of the different models will allow for better understanding volatility structure over the Tunisia stock market, with the aim of accurately predicting fluctuation risks.},
     year = {2013}
    }
    

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    T1  - Non-linear Volatility and Dynamics of the Tunisian Stock Market
    AU  - Kalai Lamia
    AU  - Jilani Faouzi
    Y1  - 2013/12/30
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    DO  - 10.11648/j.ijefm.20140201.14
    T2  - International Journal of Economics, Finance and Management Sciences
    JF  - International Journal of Economics, Finance and Management Sciences
    JO  - International Journal of Economics, Finance and Management Sciences
    SP  - 22
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    PB  - Science Publishing Group
    SN  - 2326-9561
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    AB  - Measuring and controlling risk is one of the most attractive issues in finance. With the persistence of uncontrolled and erratic stocks movements, volatility is perceived as a barometer of daily fluctuations. An objective measure of this variable seems then needed to control risks and cover those that are considered the most important. Non-linear autoregressive modeling is our first evaluation approach. In particular, we test the presence of “persistence” of conditional variance and the presence of a degree of a leverage effect. In order to resolve for the problem of “asymmetry” in volatility, the retained specifications point to the importance of stocks reactions in response to news. Effects of shocks on volatility highlight also the need to study the “long term” behavior of conditional variance of stocks returns and articulate the presence of long memory and dependence of time series in the long run. We note that the integrated fractional autoregressive model allows for representing time series that show long-term conditional variance thanks to fractional integration parameters. In order to stop at the dynamics that manage time series, a comparative study of the results of the different models will allow for better understanding volatility structure over the Tunisia stock market, with the aim of accurately predicting fluctuation risks.
    VL  - 2
    IS  - 1
    ER  - 

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Author Information
  • Graduate Institute of Business and Accounting of Bizerta, Faculty of economic Sciences and Management of Tunisia

  • Graduate Institute of Business and Accounting of Bizerta, Faculty of economic Sciences and Management of Tunisia

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