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Volatility forecasting: Intra-day versus inter-day models

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  • Angelidis, Timotheos
  • Degiannakis, Stavros

Abstract

Volatility prediction is the key variable in forecasting the prices of options, value-at-risk and, in general, the risk that investors face. By estimating not only inter-day volatility models that capture the main characteristics of asset returns, but also intra-day models, we were able to investigate their forecasting performance for three European equity indices. A consistent relation is shown between the examined models and the specific purpose of volatility forecasts. Although researchers cannot apply one model for all forecasting purposes, evidence in favor of models that are based on inter-day datasets when their criteria based on daily frequency, such as value-at-risk and forecasts of option prices, are provided.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of International Financial Markets, Institutions and Money.

Volume (Year): 18 (2008)
Issue (Month): 5 (December)
Pages: 449-465

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Handle: RePEc:eee:intfin:v:18:y:2008:i:5:p:449-465

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Web page: http://www.elsevier.com/locate/intfin

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Cited by:
  1. Stavros Degiannakis & Christos Floros & Alexandra Livada, 2012. "Evaluating value-at-risk models before and after the financial crisis of 2008: International evidence," Managerial Finance, Emerald Group Publishing, Emerald Group Publishing, vol. 38(3), pages 436-452, March.
  2. Dimitrios P. Louzis & Spyros Xanthopoulos - Sissinis & Apostolos P. Refenes, 2012. "Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach," Economics Bulletin, AccessEcon, vol. 32(1), pages 981-991.
  3. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
  4. Stavros Degiannakis & Andreas Andrikopoulos & Timotheos Angelidis & Christos Floros, 2013. "Return dispersion, stock market liquidity and aggregate economic activity," Working Papers 166, Bank of Greece.
  5. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
  6. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(4), pages 592-604.
  7. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
  8. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 28-42.

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