IDEAS home Printed from https://ideas.repec.org/p/sek/iefpro/4807778.html
   My bibliography  Save this paper

Canaries in the coal mine. The tale of two signals: the VIX and the MOVE Indexes

Author

Listed:
  • Bruce Budd

    (Dar Al-Hekma University)

Abstract

The purpose of this paper is to explore derived signals of the historical implied volatility measures between the U.S. equities market and the U.S. bond market using the VIX and MOVE Indices 2010-2015 respectively. This paper further examines the co-movement and dynamics (i.e. changes) within and between these markets. This empirical analysis finds implied volatility of the treasury market MOVE Index can forecast the implied volatility of the equities market (VIX), though not always reliably. The signals between the VIX and MOVE Indexes in the last ten years has changed and the gap between these markets has widened. A relationship not witnessed since the early days before the 2008 Global Financial Crisis. The contributing factor to this widening gap is the greater volatility experienced by the MOVE Index compared to its VIX counterpart heightened by the record-low global interest rates and lack of liquidity in the bond market. The implications of this research are important for strategic forecasting policy decision-makers and analysts alike.

Suggested Citation

  • Bruce Budd, 2017. "Canaries in the coal mine. The tale of two signals: the VIX and the MOVE Indexes," Proceedings of Economics and Finance Conferences 4807778, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:4807778
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/8th-economics-finance-conference-london/table-of-content/detail?cid=48&iid=002&rid=7778
    File Function: First version, 2017
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    2. repec:cup:cbooks:9781107034662 is not listed on IDEAS
    3. Becker, Ralf & Clements, Adam E. & White, Scott I., 2006. "On the informational efficiency of S&P500 implied volatility," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 139-153, August.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Brooks,Chris, 2014. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781107661455, December.
    6. Franck Moraux & Patrick Navatte & Christophe Villa, 1999. "The Predictive Power of the French Market Volatility Index: A Multi Horizons Study," Review of Finance, European Finance Association, vol. 2(3), pages 303-320.
    7. Wagner, Niklas & Szimayer, Alexander, 2004. "Local and spillover shocks in implied market volatility: evidence for the U.S. and Germany," Research in International Business and Finance, Elsevier, vol. 18(3), pages 237-251, September.
    8. Ederington, Louis H. & Guan, Wei, 2013. "The cross-sectional relation between conditional heteroskedasticity, the implied volatility smile, and the variance risk premium," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3388-3400.
    9. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    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. Xuan Vinh Vo & Kevin Daly, 2008. "Volatility amongst firms in the Dow Jones Eurostoxx50 Index," Applied Financial Economics, Taylor & Francis Journals, vol. 18(7), pages 569-582.
    2. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    3. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    4. Sayantan Khanra & Sanjay Dhir, 2017. "Creating Value in Small-cap Firms by Mitigating Risks of Market Volatility," Vision, , vol. 21(4), pages 350-355, December.
    5. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    6. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    7. Dotsis, George & Psychoyios, Dimitris & Skiadopoulos, George, 2007. "An empirical comparison of continuous-time models of implied volatility indices," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3584-3603, December.
    8. 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.
    9. Anupam Dutta & Debojyoti Das, 2022. "Forecasting realized volatility: New evidence from time‐varying jumps in VIX," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2165-2189, December.
    10. Bahram Adrangi & Arjun Chatrath & Joseph Macri & Kambiz Raffiee, 2019. "Dynamic Responses of Major Equity Markets to the US Fear Index," JRFM, MDPI, vol. 12(4), pages 1-23, September.
    11. Kozarski, R., 2013. "Pricing and hedging in the VIX derivative market," Other publications TiSEM 221fefe0-241e-4914-b6bd-c, Tilburg University, School of Economics and Management.
    12. Ghulam, Yaseen & Doering, Jana, 2018. "Spillover effects among financial institutions within Germany and the United Kingdom," Research in International Business and Finance, Elsevier, vol. 44(C), pages 49-63.
    13. Eugenie Hol & Siem Jan Koopman, 2000. "Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility," Tinbergen Institute Discussion Papers 00-104/4, Tinbergen Institute.
    14. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, vol. 37(C), pages 242-251.
    15. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, April.
    16. Bentes, Sónia R., 2015. "A comparative analysis of the predictive power of implied volatility indices and GARCH forecasted volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 105-112.
    17. Bentes, Sonia R & Menezes, Rui, 2012. "On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility," MPRA Paper 42193, University Library of Munich, Germany.
    18. Bekiros, Stelios & Jlassi, Mouna & Naoui, Kamel & Uddin, Gazi Salah, 2017. "The asymmetric relationship between returns and implied volatility: Evidence from global stock markets," Journal of Financial Stability, Elsevier, vol. 30(C), pages 156-174.
    19. Tim Bollerslev & Hao Zhou, 2003. "Volatility puzzles: a unified framework for gauging return-volatility regressions," Finance and Economics Discussion Series 2003-40, Board of Governors of the Federal Reserve System (U.S.).
    20. Yoo, Eun Gyu & Yoon, Sun-Joong, 2020. "CBOE VIX and Jump-GARCH option pricing models," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 839-859.

    More about this item

    Keywords

    VIX Index; MOVE Index; Implied Volatility.;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

    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:sek:iefpro:4807778. 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: Klara Cermakova (email available below). General contact details of provider: https://iises.net/ .

    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.