Longer-term time-series volatility forecasts.

Louis H. Ederington, Wei Guan

Research output: Contribution to journalArticlepeer-review

Abstract

Option pricing models and longer-term value-at-risk (VaR) models generally require volatility forecasts over horizons considerably longer than the data frequency. The typical recursive procedure for generating longer-term forecasts keeps the relative weights of recent and older observations the same for all forecast horizons. In contrast, we find that older observations are relatively more important in forecasting at longer horizons. We find that the Ederington and Guan (2005) model and a modified EGARCH (exponential generalized autoregressive conditional heteroskedastic) model in which parameter values vary with the forecast horizon forecast better out-of-sample than the GARCH (generalized autoregressive conditional heteroskedastic), EGARCH, and Glosten, Jagannathan, and Runkle (GJR) models across a wide variety of markets and forecast horizons.

Original languageAmerican English
JournalDefault journal
StatePublished - Jan 1 2010

Keywords

  • Volatility
  • Risk assessment
  • Forecasting techniques
  • Stochastic models
  • Securities prices
  • Time Series
  • Studies

Disciplines

  • Business
  • Finance

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