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Improving the Forecasting of Dynamic Conditional Correlation: a Volatility Dependent Approach

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  • E. Otranto

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Abstract

Forecasting volatility in a multivariate framework has received many contributions in the recent literature, but problems in estimation are still frequently encountered when dealing with a large set of time series. The Dynamic Conditional Correlation (DCC) modeling is probably the most used approach; it has the advantage of separating the estimation of the volatility of each time series (with great flexibility, using single univariate models) and the correlation part (with the strong constraint imposing the same dynamics to all the correlations). We propose a modification to the DCC model, providing different dynamics for each correlation, simply hypothesizing a dependence on the volatility structure of each time series. This new model implies adding only two parameters with respect to the original DCC model. Its performance is evaluated in terms of out-of-sample forecasts with respect to the DCC models and other multivariate GARCH models. The results on four data sets seem to favor the new model.

Suggested Citation

  • E. Otranto, 2009. "Improving the Forecasting of Dynamic Conditional Correlation: a Volatility Dependent Approach," Working Paper CRENoS 200917, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
  • Handle: RePEc:cns:cnscwp:200917
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    More about this item

    Keywords

    dynamic conditional correlation; garch distance; multivariate;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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