Generating Volatility Forecasts from Value at Risk Estimates

  title={Generating Volatility Forecasts from Value at Risk Estimates},
  author={James W. Taylor},
  journal={Management Science},
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
8 Extracted Citations
12 Extracted References
Similar Papers

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 12 references

Triangular approximations for continuous random variables in risk analysis

  • D. Johnson
  • Journal of the Operational Research Society 53…
  • 2002
2 Excerpts

Technical Document

  • RiskMetrics.
  • Morgan Guaranty Trust Company of New York. White…
  • 1996

Conditional heteroskedasticity in asset returns : A new approach

  • M. Parkinson
  • Econometrica
  • 1991

Stationarity and persistence in the GARCH(1,1) model

  • D. B. Nelson
  • Econometric Theory 6 318-334. Nelson, D.B. 1991…
  • 1990
2 Excerpts

A conditionally heteroskedastic time series model for speculative prices and rates of return

  • T. Bollerslev
  • Review of Economics and Statistics
  • 1987
1 Excerpt

Understanding Robust and Exploratory Data

  • D. C. Hoaglin, F. Mosteller, J. W. Tukey
  • 1983
1 Excerpt

Similar Papers

Loading similar papers…