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A Note on the Normalized Errors in ARCH and Stochastic Volatility Models

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Author Info
Nelson, Daniel B.
Abstract

It is well-known that conditional heteroskedasticity thickens the tails of the unconditional distribution of an error term relative to its conditional distribution. To what extent do imperfect forecasts of the conditional variance undo this tail thickening? This note considers the effect of changing the quality of the information embodied in a forecast of a conditional variance. Adding noise of a certain form thickens the tails of the normalized errors, but decreasing the amount of information used in the forecast may or may not thicken the tails. We also explore the relation between tail thickness and various notions of volatility forecasts. The relationship is surprisingly complicated.

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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 12 (1996)
Issue (Month): 01 (March)
Pages: 113-128
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:cup:etheor:v:12:y:1996:i:01:p:113-128_00

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  1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," NBER Working Papers 7488, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Nour Meddahi, 2001. "An Eigenfunction Approach for Volatility Modeling," CIRANO Working Papers 2001s-70, CIRANO. [Downloadable!]
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This page was last updated on 2009-11-24.


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