Generating Volatility Forecasts from Value at Risk Estimates

@article{Taylor2005GeneratingVF,
  title={Generating Volatility Forecasts from Value at Risk Estimates},
  author={James W. Taylor},
  journal={Management Science},
  year={2005},
  volume={51},
  pages={712-725}
}
Statistical volatility models rely on the assumption that the shape of the conditional distribution is fixed over time and that it is only the volatility that varies. The recently proposed conditional autoregressive value at risk (CAViaR) models require no such assumption, and allow quantiles to be modelled directly in an autoregressive framework. Although useful for risk management, CAViaR models do not provide volatility forecasts, which are needed for several other important applications… CONTINUE READING
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