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Predicting volatility and correlations with Financial Conditions Indexes

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  • Opschoor, Anne
  • van Dijk, Dick
  • van der Wel, Michel

Abstract

We model the impact of financial conditions on asset market volatilities and correlations. We extend the Spline-GARCH model for volatility and DCC model for correlation to allow for inclusion of indexes that measure financial conditions. In our empirical application we consider daily stock returns of US deposit banks during the period 1994–2011, and proxy financial conditions by the Bloomberg Financial Conditions Index (FCI) which comprises the money, bond, and equity markets. We find that worse financial conditions are associated with both higher volatility and higher correlations between stock returns, especially during crises. Moreover, inclusion of the FCI in volatility and correlation modeling improves Value-at-Risk estimates, particularly at short horizons.

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  • Opschoor, Anne & van Dijk, Dick & van der Wel, Michel, 2014. "Predicting volatility and correlations with Financial Conditions Indexes," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 435-447.
    • Handle: RePEc:eee:empfin:v:29:y:2014:i:c:p:435-447
    DOI: 10.1016/j.jempfin.2014.10.003
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    2. Lubberink, Martien, 2014. "Are banks’ below-par own debt repurchases a cause for prudential concern?," MPRA Paper 59475, University Library of Munich, Germany.
    3. P. Evans & David G. McMillan & Fiona J. McMillan, 2017. "Time-varying correlations and interrelations: Firm-level-based sector evidence," Journal of Asset Management, Palgrave Macmillan, vol. 18(3), pages 209-221, May.
    4. Wang, Bo & Li, Haoran, 2021. "Downside risk, financial conditions and systemic risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
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    7. Salisu, Afees A. & Demirer, Riza & Gupta, Rangan, 2022. "Financial turbulence, systemic risk and the predictability of stock market volatility," Global Finance Journal, Elsevier, vol. 52(C).
    8. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    9. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    10. Christian Conrad & Melanie Schienle, 2020. "Testing for an Omitted Multiplicative Long-Term Component in GARCH Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 229-242, April.
    11. Bitetto, Alessandro & Cerchiello, Paola & Mertzanis, Charilaos, 2023. "On the efficient synthesis of short financial time series: A Dynamic Factor Model approach," Finance Research Letters, Elsevier, vol. 53(C).
    12. Giovanni Bonaccolto & Massimiliano Caporin, 2016. "The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective," JRFM, MDPI, vol. 9(3), pages 1-25, July.
    13. Riza Demirer & Rangan Gupta & He Li & Yu You, 2021. "Financial Vulnerability and Volatility in Emerging Stock Markets: Evidence from GARCH-MIDAS Models," Working Papers 202112, University of Pretoria, Department of Economics.
    14. Hummaira Jabeen, 2023. "US-Financial Conditions and Macro-economy of Emerging Markets," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 9(1), pages 51-63, March.
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    More about this item

    Keywords

    Dynamic correlations; Volatility modeling; Financial Conditions Indexes; Bank holding companies;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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