IDEAS home Printed from https://ideas.repec.org/a/spr/metcap/v1y1999i4d10.1023_a1010010825105.html
   My bibliography  Save this article

Statistical Analysis of Financial Volatility by Wavelet Shrinkage

Author

Listed:
  • Enrico Capobianco

    (Technical University of Denmark)

Abstract

We analyze the Nikkei daily stock index and verify how wavelets can help in identifying, estimating and predicting its volatility features. While we study the conditional mean and variance dynamics, by utilizing statistical parametric inference techniques, we also decompose the observed signal with a data de-noising procedure. We thus investigate how wavelets discriminate among information at different resolution levels and we attempt to understand whether the de-noised data lead to a better identification of the underlying volatility process. We find that the wavelet data pre-processing strategy, by reducing the measurement error of the observed data, is useful for improving the volatility prediction power.

Suggested Citation

  • Enrico Capobianco, 1999. "Statistical Analysis of Financial Volatility by Wavelet Shrinkage," Methodology and Computing in Applied Probability, Springer, vol. 1(4), pages 423-443, December.
    • Handle: RePEc:spr:metcap:v:1:y:1999:i:4:d:10.1023_a:1010010825105
    DOI: 10.1023/A:1010010825105
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1010010825105
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1023/A:1010010825105?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev, 1997. "Answering the Critics: Yes, ARCH Models Do Provide Good Volatility Forecasts," NBER Working Papers 6023, National Bureau of Economic Research, Inc.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    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. Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
    2. Antoniou, Antonios & Vorlow, Constantinos E., 2004. "Recurrence quantification analysis of wavelet pre-filtered index returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 257-262.
    3. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.
    4. Antoniou, Antonios & Vorlow, Constantinos E., 2005. "Price clustering and discreteness: is there chaos behind the noise?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 389-403.
    5. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    6. Capobianco, Enrico, 2003. "Independent Multiresolution Component Analysis and Matching Pursuit," Computational Statistics & Data Analysis, Elsevier, vol. 42(3), pages 385-402, March.

    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. Benoit Perron, 2003. "Semiparametric Weak-Instrument Regressions with an Application to the Risk-Return Tradeoff," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 424-443, May.
    2. Capobianco, Enrico, 2003. "Empirical volatility analysis: feature detection and signal extraction with function dictionaries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 495-518.
    3. Boldanov, Rustam & Degiannakis, Stavros & Filis, George, 2016. "Time-varying correlation between oil and stock market volatilities: Evidence from oil-importing and oil-exporting countries," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 209-220.
    4. Klaassen, F.J.G.M., 1998. "Improving Garch Volatility Forecasts," Other publications TiSEM f5bcd096-7744-4137-aabc-6, Tilburg University, School of Economics and Management.
    5. Eskandar A. Tooma, 2003. "Modeling and Forecasting Egyptian Stock Market Volatility Before and After Price Limits," Working Papers 0310, Economic Research Forum, revised Apr 2003.
    6. Kevin Daly, 2011. "An Overview of the Determinants of Financial Volatility: An Explanation of Measuring Techniques," Modern Applied Science, Canadian Center of Science and Education, vol. 5(5), pages 1-46, October.
    7. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    8. Lee, Gabriel S. & Boss, Michael & Klisz, Chris, 2001. "Empirical Performance of the Czech and Hungarian Index Options under Jump," Economics Series 91, Institute for Advanced Studies.
    9. Rosenow, Bernd, 2008. "Determining the optimal dimensionality of multivariate volatility models with tools from random matrix theory," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 279-302, January.
    10. St'ephane Chr'etien & Juan-Pablo Ortega, 2011. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Papers 1101.5475, arXiv.org.
    11. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    12. Beran, Jan & Feng, Yuanhua, 1999. "Local Polynomial Estimation with a FARIMA-GARCH Error Process," CoFE Discussion Papers 99/08, University of Konstanz, Center of Finance and Econometrics (CoFE).
    13. Corbet, Shaen & Larkin, Charles & McMullan, Caroline, 2020. "The impact of industrial incidents on stock market volatility," Research in International Business and Finance, Elsevier, vol. 52(C).
    14. Cho, Guedae & Kim, MinKyoung & Koo, Won W., 2003. "Relative Agricultural Price Changes In Different Time Horizons," 2003 Annual meeting, July 27-30, Montreal, Canada 22249, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    15. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    16. Umar, Muhammad & Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Furqan, Mehreen, 2023. "Asymmetric volatility structure of equity returns: Evidence from an emerging market," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 330-336.
    17. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    18. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    19. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    20. Tomanova, Lucie, 2013. "Exchange Rate Volatility and the Foreign Trade in CEEC," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 267, Ekonomik Yaklasim Association.

    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:spr:metcap:v:1:y:1999:i:4:d:10.1023_a:1010010825105. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.