IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/45993.html
   My bibliography  Save this paper

Impact of calendar effects in the volatility of vale shares

Author

Listed:
  • Lúcio Godeiro, Lucas

Abstract

The paper aims to estimate the impact of calendar effects in volatility of the preferred and ordinary shares of Vale. The data researched were the stocks prices Vale between January 2, 1995 and October 26, 2011. The Stochastic Volatility Model was the Model and the Structural Model was the estimation method used. The results indicate that the privatization and the public offer of the stocks of Vale changed the behavior of volatility of the shares. The calendar effects have effect in volatility. The calendar effects had a greater explanatory power over the ordinary shares.

Suggested Citation

  • Lúcio Godeiro, Lucas, 2011. "Impact of calendar effects in the volatility of vale shares," MPRA Paper 45993, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:45993
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/45993/1/MPRA_paper_45993.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Harvey, Andrew C & Koopman, Siem Jan, 1992. "Diagnostic Checking of Unobserved-Components Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 377-389, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Broto Carmen & Ruiz Esther, 2009. "Testing for Conditional Heteroscedasticity in the Components of Inflation," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-30, May.
    2. Marczak, Martyna & Proietti, Tommaso, 2016. "Outlier detection in structural time series models: The indicator saturation approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 180-202.
    3. Matteo Barigozzi & Marc Hallin, 2016. "Generalized dynamic factor models and volatilities: recovering the market volatility shocks," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 33-60, February.
    4. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    5. Alexander Tsyplakov, 2011. "An introduction to state space modeling (in Russian)," Quantile, Quantile, issue 9, pages 1-24, July.
    6. Carlos Trucíos & João H. G. Mazzeu & Marc Hallin & Luiz K. Hotta & Pedro L. Valls Pereira & Mauricio Zevallos, 2022. "Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(1), pages 40-52, December.
    7. Li Wei & Ming-Chih Lee & Wan-Hsiu Cheng & Chia-Hsien Tang & Jing-Wun You, 2023. "Evaluating the Efficiency of Financial Assets as Hedges against Bitcoin Risk during the COVID-19 Pandemic," Mathematics, MDPI, vol. 11(13), pages 1-19, June.
    8. Philipp Adämmer & Martin T. Bohl, 2018. "Price discovery dynamics in European agricultural markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(5), pages 549-562, May.
    9. Michael ARTIS & Massimiliano MARCELLINO & Tommaso PROIETTI, 2002. "Dating the Euro Area Business Cycle," Economics Working Papers ECO2002/24, European University Institute.
    10. Sentana, Enrique, 1995. "Risk and return in the Spanish stock market," LSE Research Online Documents on Economics 119179, London School of Economics and Political Science, LSE Library.
    11. Gabriele Fiorentini & Enrique Sentana, 2016. "Neglected serial correlation tests in UCARIMA models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 121-178, March.
    12. Ringlund, Guro Bornes & Rosendahl, Knut Einar & Skjerpen, Terje, 2008. "Does oilrig activity react to oil price changes An empirical investigation," Energy Economics, Elsevier, vol. 30(2), pages 371-396, March.
    13. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    14. Christian Pierdzioch & Andrea Schertler, 2007. "Sources of Predictability of European Stock Markets for High-technology Firms," The European Journal of Finance, Taylor & Francis Journals, vol. 13(1), pages 1-27.
    15. Barros, Pedro Pita & Nunes, Luis C., 2010. "The impact of pharmaceutical policy measures: An endogenous structural-break approach," Social Science & Medicine, Elsevier, vol. 71(3), pages 440-450, August.
    16. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    17. Proietti, Tommaso, 2003. "Forecasting the US unemployment rate," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 451-476, March.
    18. Barnett, William A. & de Peretti, Philippe, 2009. "Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability," Macroeconomic Dynamics, Cambridge University Press, vol. 13(S2), pages 317-334, September.
    19. Nauro Campos & Corrado Macchiarelli, 2020. "Brexit and the Euro," LEQS – LSE 'Europe in Question' Discussion Paper Series 154, European Institute, LSE.
    20. Kosuke Sasakura & Takeshi Aoki & Masayoshi Komatsu & Takeshi Watanabe, 2020. "A Temperature-Risk and Energy-Saving Evaluation Model for Supporting Energy-Saving Measures for Data Center Server Rooms," Energies, MDPI, vol. 13(19), pages 1-22, October.

    More about this item

    Keywords

    Stochastic Volatility Model; Kalman Filter; Vale.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:45993. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.