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Are news important to predict the Value-at-Risk?

Author

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  • Mauro Bernardi
  • Leopoldo Catania
  • Lea Petrella

Abstract

In this paper, we investigate the impact of news to predict extreme financial returns using high-frequency data. We consider several model specifications differing for the dynamic property of the underlying stochastic process as well as for the innovation process. Since news are essentially qualitative measures, they are firstly transformed into quantitative measures which are subsequently introduced as exogenous regressors into the conditional volatility dynamics. Three basic sentiment indexes are constructed starting from three lists of words defined by historical market news response and by a discriminant analysis. Models are evaluated in terms of their predictive accuracy to forecast out-of-sample Value-at-Risk of the STOXX Europe 600 sectors at different confidence levels using several statistic tests and the model confidence set procedure of Hansen, Lunde, Nason [(2011). “The Model Confidence Set”. Econometrica, 79, pp. 453–497]. Moreover, since Hansen's procedure usually delivers a set of models having the same VaR predictive ability, we propose a new forecasting combination technique that dynamically weights the VaR predictions obtained by the models belonging to the optimal final set. Our results confirm that the inclusion of exogenous information as well as the right specification of the returns' conditional distribution significantly decreases the number of actual versus expected VaR violations towards one, and this is especially true for higher confidence levels.

Suggested Citation

  • Mauro Bernardi & Leopoldo Catania & Lea Petrella, 2017. "Are news important to predict the Value-at-Risk?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 535-572, May.
    • Handle: RePEc:taf:eurjfi:v:23:y:2017:i:6:p:535-572
    DOI: 10.1080/1351847X.2015.1106959
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    Citations

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    Cited by:

    1. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    2. Owusu Junior, Peterson & Tiwari, Aviral Kumar & Tweneboah, George & Asafo-Adjei, Emmanuel, 2022. "GAS and GARCH based value-at-risk modeling of precious metals," Resources Policy, Elsevier, vol. 75(C).
    3. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    4. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    5. Laporta, Alessandro G. & Merlo, Luca & Petrella, Lea, 2018. "Selection of Value at Risk Models for Energy Commodities," Energy Economics, Elsevier, vol. 74(C), pages 628-643.
    6. Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024. "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, vol. 129(C).
    7. Yong Ma & Lu Yan & Dongtao Pan, 2024. "The power of news data in forecasting tail risk: evidence from China," Empirical Economics, Springer, vol. 67(6), pages 2607-2642, December.

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