IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/25906.html
   My bibliography  Save this paper

Stock Index Volatility: the case of IPSA

Author

Listed:
  • Alfaro, Rodrigo
  • Silva, Carmen Gloria

Abstract

This paper introduces alternative measurements that use additional information of prices during the day: opening, minimum, maximum, and closing prices. Using the binomial model as the distribution of the stock price we prove that these alternative measurements are more efficient than the traditional ones that rely only in closing price. Following Garman and Klass (1980) we compute the relative efficiency of these measurements showing that are 3 to 4 times more efficient than using closing prices. Using daily data of the Chilean stock market index we show that a discrete-time approximation of the stock price seems to be more accurate than the continuous-time model. Also, we prove that there is a high correlation between intraday volatility measurements and implied ones obtained from options market (VIX). For that we propose the use of intraday information to estimate volatility for the cases where the stock markets do not have an associated option market.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:25906
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/25906/1/MPRA_paper_25906.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    4. 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.
    5. 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.
    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. Daniel Oda, 2013. "Introducing Liquidity Risk in the Contingent-Claim Analysis for the Banks," Working Papers Central Bank of Chile 681, 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. 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.
    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. Claudiu Vinte & Marcel Ausloos, 2022. "The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator," Papers 2205.00104, arXiv.org.
    4. Elsayed, Ahmed H. & Asutay, Mehmet & ElAlaoui, Abdelkader O. & Bin Jusoh, Hashim, 2024. "Volatility spillover across spot and futures markets: Evidence from dual financial system," Research in International Business and Finance, Elsevier, vol. 71(C).
    5. Igor Kliakhandler, 2007. "Execution edge of pit traders and intraday price ranges of soft commodities," Applied Financial Economics, Taylor & Francis Journals, vol. 17(5), pages 343-350.
    6. Garvey, John & Gallagher, Liam A., 2013. "The economics of data: Using simple model-free volatility in a high-frequency world," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 370-379.
    7. Hiroyuki Kawakatsu, 2021. "Information in daily data volatility measurements," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1642-1656, April.
    8. Thierry Chauveau & Sylvain Friederich & Jérôme Héricourt & Emmanuel Jurczenko & Catherine Lubochinsky & Bertrand Maillet & Christophe Moussu & Bogdan Négréa & Hélène Raymond-Feingold, 2004. "La volatilité des marchés augmente-t-elle ?," Revue d'Économie Financière, Programme National Persée, vol. 74(1), pages 17-44.
    9. Alia Afzal & Philipp Sibbertsen, 2021. "Modeling fractional cointegration between high and low stock prices in Asian countries," Empirical Economics, Springer, vol. 60(2), pages 661-682, February.
    10. Sanjay Sehgal & Tarunika Jain Agrawal, 2019. "Impact of Commodity Transaction Tax on Market Liquidity, Volatility, and Government Revenues: An Empirical Study for India," Vikalpa: The Journal for Decision Makers, , vol. 44(1), pages 12-29, March.
    11. Nelson, Daniel B., 1996. "Asymptotic filtering theory for multivariate ARCH models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 1-47.
    12. Lapinova, S. & Saichev, A. & Tarakanova, M., 2013. "Efficiency and probabilistic properties of bridge volatility estimator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1439-1451.
    13. Min Zhang & Adam W. Kolkiewicz & Tony S. Wirjanto & Xindan Li, 2013. "The Impacts of Financial Crisis on Sovereign Credit Risk Analysis in Asia and Europe," Working Paper series 62_13, Rimini Centre for Economic Analysis.
    14. G'abor Petneh'azi & J'ozsef G'all, 2018. "Exploring the predictability of range-based volatility estimators using RNNs," Papers 1803.07152, arXiv.org.
    15. Richard Gerlach & Declan Walpole & Chao Wang, 2017. "Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility," Quantitative Finance, Taylor & Francis Journals, vol. 17(2), pages 199-215, February.
    16. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    17. Joel Ong & Dorien Herremans, 2024. "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers 2406.08742, arXiv.org.
    18. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2016. "Volatility spillovers across stock index futures in Asian markets: Evidence from range volatility estimators," Finance Research Letters, Elsevier, vol. 17(C), pages 158-166.
    19. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    20. Gkillas, Konstantinos & Vortelinos, Dimitrios I. & Babalos, Vassilios & Wohar, Mark E., 2021. "Day-of-the-week effect and spread determinants: Some international evidence from equity markets," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 268-288.

    More about this item

    Keywords

    Volatility; Binomial Model; VIX; Bias and Efficiency.;
    All these keywords.

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:25906. 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.