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Log-volatility enhanced GARCH models for single asset returns

Author

Listed:
  • Tomasz Skoczylas

    (University of Warsaw, Faculty of Economic Sciences)

Abstract

This paper presents an alternative approach to modelling and forecasting single asset return volatility. A new, flexible framework is proposed, one which may be considered a development of single-equation GARCH-type models. In this approach an additional equation is added, which binds logarithms of conditional volatility and observed volatility, as measured by the Garman-Klass variance estimator. It enables more information to be retrieved from data. Proposed models are compared with benchmark GARCH and range-based GARCH (RGARCH) models in terms of prediction accuracy. All models are estimated with the maximum likelihood method, using time series of EUR/PLN, EUR/USD, EUR/GBP spot rates quotations as well as WIG20, Dow Jones industrial and DAX indexes. Results are encouraging, especially for foreasting Value-at-Risk. Log-volatility enhanced models achieved lesser rates of VaR exception, as well as lower coverage test statistics, without being more conservative than their single-equation counterparts, as their forecast error measures are to some degree similar.

Suggested Citation

  • Tomasz Skoczylas, 2015. "Log-volatility enhanced GARCH models for single asset returns," Bank i Kredyt, Narodowy Bank Polski, vol. 46(5), pages 411-432.
    • Handle: RePEc:nbp:nbpbik:v:46:y:2015:i:5:p:411-432
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    References listed on IDEAS

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    More about this item

    Keywords

    GARCH; range-based volatility estimators; observed volatility; Value-at-Risk; volatility forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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