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Implied Volatility Forecasting: A Comparison of Different Procedures

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Author Info

  • Hwang, S.
  • Satchell, S. E.

Abstract

The purpose of this paper is to consider how to forecast implied volatility for a selection of UK companies with traded options on their stocks. The authors consider a range of GARCH and log--ARFIMA based models as well as some simple forecasting models. Overall, it is found that a log-ARFIMA model forecasts best of short and long horizons.

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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Accounting and Finance Discussion Papers with number 98-af38.

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Date of creation: Feb 1998
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Handle: RePEc:cam:camafp:98-af38

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Web page: http://www.econ.cam.ac.uk/index.htm

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Cited by:
  1. Soosung Hwang & Steve Satchell, 2005. "GARCH model with cross-sectional volatility: GARCHX models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 203-216.
  2. Cifarelli, giulio, 2002. "The information content of implied volatilities of options on eurodeposit futures traded on the LIFFE: is there long memory?," MPRA Paper 28538, University Library of Munich, Germany.
  3. Adam Clements & Joanne Fuller, 2012. "Forecasting increases in the VIX: A time-varying long volatility hedge for equities," NCER Working Paper Series 88, National Centre for Econometric Research.
  4. Fabio Trojani & Francesco Audrino, 2006. "Estimating and predicting multivariate volatility thresholds in global stock markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 345-369.
  5. Giulio, Cifarelli, 2004. "Yes, implied volatilities are not informationally efficient: an empirical estimate using options on interest rate futures contracts," MPRA Paper 28655, University Library of Munich, Germany.
  6. Darsinos, T. & Satchell, S.E., 2001. "Bayesian Analysis of the Black-Scholes Option Price," Cambridge Working Papers in Economics 0102, Faculty of Economics, University of Cambridge.

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