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The Jump component of S&P 500 volatility and the VIX index

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  • Ralf Becker
  • Adam Clements

    ()

  • Andrew McClelland

Abstract

Much research has investigated the differences between option implied volatilities and econometric model-based forecasts in terms of forecast accuracy and relative informational content. Implied volatility is a market determined forecast, in contrast to model-based forecasts that employ some degree of smoothing to generate forecasts. Therefore, implied volatility has the potential to reflect information that a model-based forecast could not. Specifically, this paper considers two issues relating to the informational content of the S&P 500 VIX implied volatility index. First, whether it subsumes information on how historical jump activity contributed to the price volatility, followed by whether the VIX reflects any incremental information relative to model based forecasts pertaining to future jumps. It is found that the VIX index both subsumes information relating to past jump contributions to volatility and reflects incremental information pertaining to future jump activity, relative to modelbased forecasts. This is an issue that has not been examined previously in the literature and expands our understanding of how option markets form their volatility forecasts.

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File URL: http://www.ncer.edu.au/papers/documents/WpNo24Mar08.pdf
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Bibliographic Info

Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 24.

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Length: 17 pages
Date of creation: 17 Mar 2008
Date of revision:
Handle: RePEc:qut:auncer:2008-13

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Keywords: Implied volatility; VIX; volatility forecasts; informational efficiency; jumps;

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Cited by:
  1. Lin, Yueh-Neng, 2013. "VIX option pricing and CBOE VIX Term Structure: A new methodology for volatility derivatives valuation," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4432-4446.
  2. Hammoudeh, S.M. & Liu, T. & Chang, C-L. & McAleer, M.J., 2011. "Risk Spillovers in Oil-Related CDS, Stock and Credit Markets," Econometric Institute Research Papers EI 2011-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  3. Lee, Bong Soo & Ryu, Doojin, 2013. "Stock returns and implied volatility: A new VAR approach," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy, vol. 7(3), pages 1-20.
  4. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4943-4957.
  5. Saldías, Martín, 2013. "Systemic risk analysis using forward-looking Distance-to-Default series," Journal of Financial Stability, Elsevier, vol. 9(4), pages 498-517.
  6. Alvaro Cartea & Dimitrios Karyampas, 2009. "The relationship between the volatility of returns and the number of jumps in financial markets," Business Economics Working Papers wb097508, Universidad Carlos III, Departamento de Economía de la Empresa.
  7. Boudt, Kris & Petitjean, Mikael, 2014. "Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks," Journal of Financial Markets, Elsevier, vol. 17(C), pages 121-149.
  8. Kaeck, Andreas & Alexander, Carol, 2013. "Continuous-time VIX dynamics: On the role of stochastic volatility of volatility," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 46-56.
  9. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.

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