IDEAS home Printed from https://ideas.repec.org/a/ioe/cuadec/v45y2008i132p217-233.html
   My bibliography  Save this article

Volatilidad de Indices Accionarios: El caso del IPSA

Author

Listed:
  • Rodrigo A. Alfaro
  • Carmen Gloria Silva

Abstract

This paper reviews the traditional ways to measure volatility which are based only on closing prices, and introduces alternative measurements that use additional information of prices during the day: opening, minimum, maximum, and closing prices. Using th

Suggested Citation

  • Rodrigo A. Alfaro & Carmen Gloria Silva, 2008. "Volatilidad de Indices Accionarios: El caso del IPSA," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 45(132), pages 217-233.
  • Handle: RePEc:ioe:cuadec:v:45:y:2008:i:132:p:217-233
    as

    Download full text from publisher

    File URL: http://www.economia.uc.cl/docs/132alfaa.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    2. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    3. Soosung Hwang & Pedro L. Valls Pereira, 2006. "Small sample properties of GARCH estimates and persistence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(6-7), pages 473-494.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    6. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-491, July.
    7. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    8. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Alfaro, Rodrigo & Silva, Carmen Gloria, 2010. "Stock Index Volatility: the case of IPSA," MPRA Paper 25906, University Library of Munich, Germany, revised 31 Mar 2010.
    2. Mr. Leonardo Luna & Mr. Dale F Gray & Jorge Restrepo & Carlos Garcia, 2011. "Incorporating Financial Sector Risk Into Monetary Policy Models: Application to Chile," IMF Working Papers 2011/228, International Monetary Fund.
    3. Dale F. Gray & Carlos J. García & Leonardo Luna & Jorge E. Restrepo, 2011. "Incorporating Financial Sector Risk Into Monetary Policy Models: Application to Chile," Central Banking, Analysis, and Economic Policies Book Series, in: Rodrigo Alfaro (ed.),Financial Stability, Monetary Policy, and Central Banking, edition 1, volume 15, chapter 6, pages 159-197, Central Bank of Chile.
    4. Rodrigo A. Alfaro. & Andrés Sagner & Carmen G. Silva, 2011. "Aplicaciones del Modelo Binomial para el Análisis de Riesgo," Working Papers Central Bank of Chile 631, Central Bank of Chile.

    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. De Nard, Gianluca & Engle, Robert F. & Ledoit, Olivier & Wolf, Michael, 2022. "Large dynamic covariance matrices: Enhancements based on intraday data," Journal of Banking & Finance, Elsevier, vol. 138(C).
    2. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    3. Nelson, Daniel B., 1996. "Asymptotic filtering theory for multivariate ARCH models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 1-47.
    4. Dicle, Mehmet F. & Levendis, John, 2020. "Historic risk and implied volatility," Global Finance Journal, Elsevier, vol. 45(C).
    5. Rodrigo Alfaro & Carmen Gloria Silva, 2008. "Measuring Equity Volatility: the case of Chilean Stock Index," Working Papers Central Bank of Chile 462, Central Bank of Chile.
    6. Benlagha, Noureddine & Chargui, Sana, 2017. "Range-based and GARCH volatility estimation: Evidence from the French asset market," Global Finance Journal, Elsevier, vol. 32(C), pages 149-165.
    7. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    8. Lakshmi Padmakumari & S Maheswaran, 2016. "A Regression Based Approach to Capturing the Level Dependence in the Volatility of Stock Returns," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(12), pages 706-718, December.
    9. Tan, Shay-Kee & Ng, Kok-Haur & Chan, Jennifer So-Kuen & Mohamed, Ibrahim, 2019. "Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 537-551.
    10. Richard Gerlach & Chao Wang, 2016. "Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures," Papers 1612.08488, arXiv.org.
    11. Tomasz Skoczylas, 2015. "Bivariate GARCH models for single asset returns," Working Papers 2015-03, Faculty of Economic Sciences, University of Warsaw.
    12. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    13. Parthajit Kayal & Sumanjay Dutta & Vipul Khandelwal & Rakesh Nigam, 2021. "Information Theoretic Ranking of Extreme Value Returns," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 1-21, March.
    14. Prateek Sharma & Vipul _, 2015. "Forecasting stock index volatility with GARCH models: international evidence," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 32(4), pages 445-463, October.
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Kumar, Dilip & Maheswaran, S., 2014. "A new approach to model and forecast volatility based on extreme value of asset prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 128-140.
    17. Kumar, Dilip, 2015. "Sudden changes in extreme value volatility estimator: Modeling and forecasting with economic significance analysis," Economic Modelling, Elsevier, vol. 49(C), pages 354-371.
    18. Lucio Fiorin, 2022. "Estimation of Historical volatility and Allocation strategies using Variance Swaps," Papers 2208.03164, arXiv.org.
    19. Christensen, Kim & Podolski, Mark, 2005. "Asymptotic theory for range-based estimation of integrated variance of a continuous semi-martingale," Technical Reports 2005,18, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    20. Caporin, Massimiliano & Ranaldo, Angelo & Santucci de Magistris, Paolo, 2013. "On the predictability of stock prices: A case for high and low prices," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5132-5146.

    More about this item

    Keywords

    Volatilidad; modelo binomial; GARCH; VIX; sesgo y eficiencia;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:ioe:cuadec:v:45:y:2008:i:132:p:217-233. 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: Jaime Casassus (email available below). General contact details of provider: https://edirc.repec.org/data/iepuccl.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.