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GARCH-based Volatility Forecasts for Market Volatility Indices

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Abstract

Volatility forecasting is one of the main issues in the financial econometrics literature. Volatility measures may be derived from statistical models for conditional variance, or from option prices. In recent times, indices have been suggested which summarize the implied volatility of widely traded market index options. One such index is the so-called VXN, an average of 30-day ahead implied volatilities of the options written on the NASDAQ-100 Index. In this paper we show how forecasts obtained with traditional GARCH-type models can be used to forecast the volatility index VXN.

Suggested Citation

  • Massimiliano Cecconi & Giampiero M. Gallo & Marco J. Lombardi, 2002. "GARCH-based Volatility Forecasts for Market Volatility Indices," Econometrics Working Papers Archive wp2002_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
  • Handle: RePEc:fir:econom:wp2002_06
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    Cited by:

    1. Ryan SULEIMANN, 2003. "The Contagion Effect Between the Volatilities of the NASDAQ-100 and the IT.CA :A Univariate and A Bivariate Switching Approach," Econometrics 0307002, EconWPA, revised 18 Jul 2003.
    2. Ryan SULEIMANN, 2003. "New Technology Stock Market Indexes Contagion: A VAR-dccMVGARCH Approach," Econometrics 0307003, EconWPA, revised 18 Jul 2003.
    3. Ryan SULEIMANN, 2003. "Should Stock Market Indexes Time Varying Correlations Be Taken Into Account? A Conditional Variance Multivariate Approach," Econometrics 0307004, EconWPA, revised 18 Jul 2003.

    More about this item

    Keywords

    Volatility modelling; Volatility forecasting; GARCH models; VXN; Implied volatility.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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