IDEAS home Printed from https://ideas.repec.org/a/eee/jfinec/v147y2023i1p221-242.html
   My bibliography  Save this article

What are the events that shake our world? Measuring and hedging global COVOL

Author

Listed:
  • Engle, Robert F.
  • Campos-Martins, Susana

Abstract

Some events impact volatilities of most assets, asset classes, sectors and countries, causing serious damage to investment portfolios. The magnitude of such shocks is defined as global COVOL which is an abbreviation for global common volatility, a broad measure of all types of global financial risk. This paper introduces a statistical formulation of such events as common volatility innovations in both a multivariate volatility and an asset pricing context. Simulations verify the statistical performance of a simple but novel estimator and of a test to detect global COVOL. Two empirical examples show the events that have had the biggest impact on financial markets. The results are useful for portfolio optimization and risk forecasting.

Suggested Citation

  • Engle, Robert F. & Campos-Martins, Susana, 2023. "What are the events that shake our world? Measuring and hedging global COVOL," Journal of Financial Economics, Elsevier, vol. 147(1), pages 221-242.
    • Handle: RePEc:eee:jfinec:v:147:y:2023:i:1:p:221-242
    DOI: 10.1016/j.jfineco.2022.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304405X22002070
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jfineco.2022.09.009?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. Trzcinka, Charles A, 1986. "On the Number of Factors in the Arbitrage Pricing Model," Journal of Finance, American Finance Association, vol. 41(2), pages 347-368, June.
    2. Connor, Gregory & Korajczyk, Robert A, 1993. "A Test for the Number of Factors in an Approximate Factor Model," Journal of Finance, American Finance Association, vol. 48(4), pages 1263-1291, September.
    3. Bollerslev, Tim & Marrone, James & Xu, Lai & Zhou, Hao, 2014. "Stock Return Predictability and Variance Risk Premia: Statistical Inference and International Evidence," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(3), pages 633-661, June.
    4. Emil N. Siriwardane, 2019. "Limited Investment Capital and Credit Spreads," Journal of Finance, American Finance Association, vol. 74(5), pages 2303-2347, October.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March.
    7. Herskovic, Bernard & Kelly, Bryan & Lustig, Hanno & Van Nieuwerburgh, Stijn, 2016. "The common factor in idiosyncratic volatility: Quantitative asset pricing implications," Journal of Financial Economics, Elsevier, vol. 119(2), pages 249-283.
    8. Connor, Gregory & Korajczyk, Robert A. & Linton, Oliver, 2006. "The common and specific components of dynamic volatility," Journal of Econometrics, Elsevier, vol. 132(1), pages 231-255, May.
    9. Andrew Ang & Robert J. Hodrick & Yuhang Xing & Xiaoyan Zhang, 2006. "The Cross‐Section of Volatility and Expected Returns," Journal of Finance, American Finance Association, vol. 61(1), pages 259-299, February.
    10. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Ambrocio, Gene & Hasan, Iftekhar & Li, Xiang, 2023. "Global political ties and the global financial cycle," IWH Discussion Papers 23/2023, Halle Institute for Economic Research (IWH).
    2. Lang, Chunlin & Xu, Danyang & Corbet, Shaen & Hu, Yang & Goodell, John W., 2024. "Global financial risk and market connectedness: An empirical analysis of COVOL and major financial markets," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. Kumar, Pawan & Singh, Vipul Kumar & Rao, Sandeep, 2023. "Does the substitution effect lead to feedback effect linkage between ethanol, crude oil, and soft agricultural commodities?," Energy Economics, Elsevier, vol. 119(C).
    4. Klein, Tony, 2024. "Investor behavior in times of conflict: A natural experiment on the interplay of geopolitical risk and defense stocks," Journal of Economic Behavior & Organization, Elsevier, vol. 222(C), pages 294-313.
    5. Xu, Danyang & Hu, Yang & Corbet, Shaen & Goodell, John W., 2023. "Volatility connectedness between global COVOL and major international volatility indices," Finance Research Letters, Elsevier, vol. 56(C).
    6. Chen, Bin-xia & Sun, Yan-lin, 2024. "Financial market connectedness between the U.S. and China: A new perspective based on non-linear causality networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    7. Lang, Chunlin & Hu, Yang & Goodell, John W. & Hou, Yang (Greg), 2024. "Connectedness and co-movement between dirty energy, clean energy and global COVOL," Finance Research Letters, Elsevier, vol. 63(C).
    8. Zhou, Dong-hai & Liu, Xiao-xing, 2024. "Does systemic risk in the fund markets predict future economic downturns?," International Review of Financial Analysis, Elsevier, vol. 92(C).
    9. Zhang, Jiaming & Guo, Songlin & Dou, Bin & Xie, Bingyuan, 2023. "Evidence of the internationalization of China's crude oil futures: Asymmetric linkages to global financial risks," Energy Economics, Elsevier, vol. 127(PA).
    10. Oxley, Les & Hu, Yang & Corbet, Shaen & Goodell, John W., 2023. "Role of precious metals in global risk dynamics: Exploring their impact from a connectedness approach," Finance Research Letters, Elsevier, vol. 58(PD).
    11. Campos-Martins, Susana & Hendry, David F., 2024. "Common volatility shocks driven by the global carbon transition," Journal of Econometrics, Elsevier, vol. 239(1).
    12. Zhou, Dong-hai & Liu, Xiao-xing, 2023. "Do world stock markets “jump” together? A measure of high-frequency volatility risk spillover networks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).

    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. Nektarios Aslanidis & Charlotte Christiansen & Neophytos Lambertides & Christos S. Savva, 2019. "Idiosyncratic volatility puzzle: influence of macro-finance factors," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 381-401, February.
    2. Dew-Becker, Ian & Giglio, Stefano & Kelly, Bryan, 2021. "Hedging macroeconomic and financial uncertainty and volatility," Journal of Financial Economics, Elsevier, vol. 142(1), pages 23-45.
    3. Carolin Pflueger & Emil Siriwardane & Adi Sunderam, 2019. "Financial Market Risk Perceptions and the Macroeconomy," NBER Working Papers 26290, National Bureau of Economic Research, Inc.
    4. Xinyu Song, 2019. "Large Volatility Matrix Prediction with High-Frequency Data," Papers 1907.01196, arXiv.org, revised Sep 2019.
    5. Mensah, Jones Odei & Premaratne, Gamini, 2018. "Dependence patterns among Asian banking sector stocks: A copula approach," Research in International Business and Finance, Elsevier, vol. 45(C), pages 357-388.
    6. Sebastien Valeyre, 2020. "Refined model of the covariance/correlation matrix between securities," Papers 2001.08911, arXiv.org.
    7. Siwen Zhou, 2021. "Exploring the driving forces of the Bitcoin currency exchange rate dynamics: an EGARCH approach," Empirical Economics, Springer, vol. 60(2), pages 557-606, February.
    8. Carolin Pflueger & Emil Siriwardane & Adi Sunderam, 2018. "A Measure of Risk Appetite for the Macroeconomy," NBER Working Papers 24529, National Bureau of Economic Research, Inc.
    9. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    10. Gregory Connor & Lisa R. Goldberg & Robert A. Korajczyk, 2010. "Portfolio Risk Analysis," Economics Books, Princeton University Press, edition 1, number 9224.
    11. Kaminska, Iryna & Roberts-Sklar, Matt, 2018. "Volatility in equity markets and monetary policy rate uncertainty," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 68-83.
    12. Elyas Elyasiani & Iftekhar Hasan & Elena Kalotychou & Panos K. Pouliasis & Sotiris K. Staikouras, 2020. "Banks’ equity performance and the term structure of interest rates," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 29(2), pages 43-64, May.
    13. Gorodnichenko, Yuriy & Ng, Serena, 2017. "Level and volatility factors in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 91(C), pages 52-68.
    14. Qiu, Rui & Liu, Jing & Li, Yan, 2023. "Long-term adjusted volatility: Powerful capability in forecasting stock market returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    15. Byun, Sung Je, 2016. "The usefulness of cross-sectional dispersion for forecasting aggregate stock price volatility," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 162-180.
    16. Segal, Gill & Shaliastovich, Ivan, 2023. "Uncertainty, risk, and capital growth," SAFE Working Paper Series 388, Leibniz Institute for Financial Research SAFE.
    17. Eric Renault & Thijs Van Der & Bas J M Werker, 2023. "Arbitrage Pricing Theory for Idiosyncratic Variance Factors," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1403-1442.
    18. Lee, Kiryoung & Joen, Yoontae & Kim, Minki, 2022. "Which uncertainty measures matter for the cross-section of stock returns?#," Finance Research Letters, Elsevier, vol. 46(PB).
    19. Lee, Kiryoung, 2022. "Which uncertainty measures matter for the cross-section of corporate bond returns? Evidence from the U.S. during 1973–2020," Finance Research Letters, Elsevier, vol. 48(C).
    20. Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022. "Volatility forecasting with machine learning and intraday commonality," Papers 2202.08962, arXiv.org, revised Feb 2023.

    More about this item

    Keywords

    Global events; Volatility comovements; Multiplicative factor models; Geopolitical risk; Portfolio optimization;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:jfinec:v:147:y:2023:i:1:p:221-242. 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/inca/505576 .

    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.