IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v42y2017icp1367-1371.html
   My bibliography  Save this article

Application of VIX and entropy indicators for portfolio rotation strategies

Author

Listed:
  • Jadhao, Gaurav
  • Chandra, Abhijeet

Abstract

In our study, we use sample entropy and approximate entropy indicators − derived from the India Volatility Index (India VIX) − to explore the feasibility of style, size and time horizon-based portfolio rotation strategies. We show that these two entropy-based indicators are significantly and strongly related to portfolio rotation strategy based on style and size than the trading strategies based on signals derived from percentage change in India VIX. Finally, the comparative portfolio performances show that the trading strategies based on sample entropy outperform those based on VIX change. These results provide evidence of the prospect for new investment and diversification strategies in the otherwise less-studied emerging markets.

Suggested Citation

  • Jadhao, Gaurav & Chandra, Abhijeet, 2017. "Application of VIX and entropy indicators for portfolio rotation strategies," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1367-1371.
  • Handle: RePEc:eee:riibaf:v:42:y:2017:i:c:p:1367-1371
    DOI: 10.1016/j.ribaf.2017.07.074
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531916304731
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ribaf.2017.07.074?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. Steve Pincus, 2008. "Approximate Entropy as an Irregularity Measure for Financial Data," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 329-362.
    2. James Kozyra & Camillo Lento, 2011. "Using VIX data to enhance technical trading signals," Applied Economics Letters, Taylor & Francis Journals, vol. 18(14), pages 1367-1370.
    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. Vera Ivanyuk, 2022. "Proposed Model of a Dynamic Investment Portfolio with an Adaptive Strategy," Mathematics, MDPI, vol. 10(23), pages 1-19, November.

    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. Radhika Prosad Datta, 2023. "Regularity in forex returns during financial distress: Evidence from India," Papers 2308.04181, arXiv.org.
    2. Muhammad Sheraz & Imran Nasir, 2021. "Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach," Risks, MDPI, vol. 9(5), pages 1-20, May.
    3. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    4. Imlak Shaikh & Puja Padhi, 2014. "The forecasting performance of implied volatility index: evidence from India VIX," Economic Change and Restructuring, Springer, vol. 47(4), pages 251-274, November.
    5. Ataei, Masoud & Chen, Shengyuan & Yang, Zijiang & Peyghami, M. Reza, 2021. "Theory and applications of financial chaos index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    6. Day, Min-Yuh & Ni, Yensen & Huang, Paoyu, 2019. "Trading as sharp movements in oil prices and technical trading signals emitted with big data concerns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 349-372.
    7. Efremidze, Levan & Stanley, Darrol J. & Kownatzki, Clemens, 2021. "Entropy trading strategies reveal inefficiencies in Japanese stock market," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 464-477.
    8. Camillo Lento & Nikola Gradojevic, 2022. "The Profitability of Technical Analysis during the COVID-19 Market Meltdown," JRFM, MDPI, vol. 15(5), pages 1-19, April.
    9. David E. Allen & Michael McAleer & Robert Powell & Abhay K. Singh, 2013. "A Non-Parametric and Entropy Based Analysis of the Relationship between the VIX and S&P 500," JRFM, MDPI, vol. 6(1), pages 1-25, October.
    10. Wine, Trevor, 2020. "A Matrix-Based Regularity Measure for Symbolic Sequences," OSF Preprints vpg8h, Center for Open Science.
    11. Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    12. Darrol J. Stanley & Levan Efremidze & Jannie Rossouw, 2017. "Entropy Risk Factor Model of Exchange Rate Prediction," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 8(3), pages 51-56, July.
    13. Nazarova Jekaterina, 2015. "Investment Planning in the Context of Business Cycle Volatility," Economics and Business, Sciendo, vol. 27(1), pages 53-57, August.
    14. Stephan Schwill, 2018. "Entropy Analysis of Financial Time Series," Papers 1807.09423, arXiv.org.
    15. Tzu‐Pu Chang, 2021. "Buy Low and Sell High: The 52‐Week Price Range and Predictability of Returns," International Review of Finance, International Review of Finance Ltd., vol. 21(1), pages 336-344, March.

    More about this item

    Keywords

    India VIX; Sample entropy; Approximate entropy; Portfolio management; Trading strategy;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:eee:riibaf:v:42:y:2017:i:c:p:1367-1371. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .

    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.