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Forecasting Implied Volatility Surfaces

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Author Info
Francesco Audrino ()
Dominik Colagelo ()

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Abstract

We propose a new semi-parametric model for the implied volatility surface, which incorporates machine learning algorithms. Given a starting model, a tree-boosting algorithm sequentially minimizes the residuals of observed and estimated implied volatility. To overcome the poor predicting power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the tree boosting. Back testing the out-of-sample appropriateness of our model on a large data set of implied volatilities on S&P 500 options, we provide empirical evidence of its strong predictive potential, as well as comparing it to other standard approaches in the literature.

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File URL: http://www.vwa.unisg.ch/RePEc/usg/dp2007/DP-42-Au.pdf
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Publisher Info
Paper provided by Department of Economics, University of St. Gallen in its series University of St. Gallen Department of Economics working paper series 2007 with number 2007-42.

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Length: 38 pages
Date of creation: Nov 2007
Date of revision:
Handle: RePEc:usg:dp2007:2007-42

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Related research
Keywords: Implied Volatility Implied Volatility Surface Forecasting Tree Boosting Regression Tree Functional Gradient Descent

Other versions of this item:

Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing
G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 13(3), pages 585-625.
  2. Silvia Goncalves & Massimo Guidolin, 2005. "Predictable dynamics in the S&P 500 index options implied volatility surface," Working Papers 2005-010, Federal Reserve Bank of St. Louis. [Downloadable!]
    Other versions:
  3. Nicolas P. B. Bollen & Robert E. Whaley, 2004. "Does Net Buying Pressure Affect the Shape of Implied Volatility Functions?," Journal of Finance, American Finance Association, vol. 59(2), pages 711-753, 04. [Downloadable!] (restricted)
  4. Francesco Audrino, 2005. "The Stability of Factor Models of Interest Rates," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 422-441. [Downloadable!] (restricted)
  5. Gianluca Cassesse & Massimo Guidolin, 2005. "Modelling the MIB30 implied volatility surface. Does market efficiency matter?," Working Papers 2005-008, Federal Reserve Bank of St. Louis. [Downloadable!]
  6. Ser-Huang Poon & Clive W. J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  7. Monfort, A. & Gourieroux, C. & Tenreiro, C., 1995. "Kernel M-Estimators and Functional residual Plots," Papers 9546, Institut National de la Statistique et des Etudes Economiques-.
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This page was last updated on 2008-7-1.

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