IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v260y2018i1d10.1007_s10479-016-2215-3.html
   My bibliography  Save this article

Multiresolution analysis of S&P500 time series

Author

Listed:
  • Deniz Kenan Kılıç

    (Middle East Technical University)

  • Ömür Uğur

    (Middle East Technical University)

Abstract

Time series analysis is an essential research area for those who are dealing with scientific and engineering problems. The main objective, in general, is to understand the underlying characteristics of selected time series by using the time as well as the frequency domain analysis. Then one can make a prediction for desired system to forecast ahead from the past observations. Time series modeling, frequency domain and some other descriptive statistical data analyses are the primary subjects of this study: indeed, choosing an appropriate model is at the core of any analysis to make a satisfactory prediction. In this study Fourier and wavelet transform methods are used to analyze the complex structure of a financial time series, particularly, S&P500 daily closing prices and return values. Multiresolution analysis is naturally handled by the help of wavelet transforms in order to pinpoint special characteristics of S&P500 data, like periodicity as well as seasonality. Besides, further case study discussions include the modeling of S&P500 process by invoking linear and nonlinear methods with wavelets to address how multiresolution approach improves fitting and forecasting results.

Suggested Citation

  • Deniz Kenan Kılıç & Ömür Uğur, 2018. "Multiresolution analysis of S&P500 time series," Annals of Operations Research, Springer, vol. 260(1), pages 197-216, January.
    • Handle: RePEc:spr:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2215-3
    DOI: 10.1007/s10479-016-2215-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-016-2215-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-016-2215-3?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. Erhan Bayraktar & H. Vincent Poor & K. Ronnie Sircar, 2004. "Estimating The Fractal Dimension Of The S&P 500 Index Using Wavelet Analysis," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(05), pages 615-643.
    2. repec:zbw:bofrdp:2005_001 is not listed on IDEAS
    3. Crowley, Patrick M., 2005. "An intuitive guide to wavelets for economists," Bank of Finland Research Discussion Papers 1/2005, Bank of Finland.
    4. Addo, Peter Martey & Billio, Monica & Guégan, Dominique, 2013. "Nonlinear dynamics and recurrence plots for detecting financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 416-435.
    5. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    6. Chaker Aloui & Duc Khuong Nguyen, 2014. "On the detection of extreme movements and persistent behaviour in Mediterranean stock markets: a wavelet-based approach," Applied Economics, Taylor & Francis Journals, vol. 46(22), pages 2611-2622, August.
    7. H. Wong & Wai-Cheung Ip & Zhongjie Xie & Xueli Lui, 2003. "Modelling and forecasting by wavelets, and the application to exchange rates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 537-553.
    8. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
    9. Francis In & Sangbae Kim, 2006. "The Hedge Ratio and the Empirical Relationship between the Stock and Futures Markets: A New Approach Using Wavelet Analysis," The Journal of Business, University of Chicago Press, vol. 79(2), pages 799-820, March.
    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. 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.
    2. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    3. Joanna Janczura & Rafał Weron, 2012. "Efficient estimation of Markov regime-switching models: An application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 385-407, July.
    4. Ahmad Alrazni Alshammari, Basheer Altarturi, Buerhan Saiti, Latifah Munassar, 2020. "The impact of exchange rate, oil price and gold price on the Kuwaiti stock market: a wavelet analysis," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 17(1), pages 31-54, June.
    5. Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2024. "Tail connectedness of DeFi and CeFi with accessible banking pillars: Unveiling novel insights through wavelet and quantile cross-spectral coherence analyses," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    6. Power, Gabriel J. & Eaves, James & Turvey, Calum & Vedenov, Dmitry, 2017. "Catching the curl: Wavelet thresholding improves forward curve modelling," Economic Modelling, Elsevier, vol. 64(C), pages 312-321.
    7. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2016. "Gold, oil, and stocks: Dynamic correlations," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 186-201.
    8. Mpoha, Salifya & Bonga-Bonga, Lumengo, 2021. "Spillover effects from China and the US to global emerging markets: a dynamic analysis," MPRA Paper 109349, University Library of Munich, Germany.
    9. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    10. Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
    11. 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.
    12. Lakhwinder Pal Singh & Ravi Teja Challa, 2016. "Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 17(2), pages 157-169, June.
    13. Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.
    14. Thomas Conlon & John Cotter & Ramazan Gençay, 2015. "Long-run international diversification," Working Papers 201502, Geary Institute, University College Dublin.
    15. repec:zbw:bofrdp:2006_018 is not listed on IDEAS
    16. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2017. "Do oil price asymmetric effects on the stock market persist in multiple time horizons?," Applied Energy, Elsevier, vol. 185(P2), pages 1799-1808.
    17. Bilgili, Faik & Mugaloglu, Erhan & Koçak, Emrah, 2018. "The impact of oil prices on CO2 emissions in China: A Wavelet coherence approach," MPRA Paper 90170, University Library of Munich, Germany.
    18. Ying Fan & Abdullah Yavas, 2023. "Price Dynamics in Public and Private Commercial Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 67(1), pages 150-190, July.
    19. Crowley, Patrick M. & Lee, Jim, 2005. "Decomposing the co-movement of the business cycle : a time-frequency analysis of growth cycles in the euro area," Research Discussion Papers 12/2005, Bank of Finland.
    20. Ozun, Alper & Cifter, Atilla, 2007. "Modeling Long-Term Memory Effect in Stock Prices: A Comparative Analysis with GPH Test and Daubechies Wavelets," MPRA Paper 2481, University Library of Munich, Germany.
    21. Roger Bowden & Jennifer Zhu, 2010. "Multi-scale variation, path risk and long-term portfolio management," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 783-796.

    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:annopr:v:260:y:2018:i:1:d:10.1007_s10479-016-2215-3. 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.