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Evaluating interval forecasts of high‐frequency financial data
Author(s) -
Clements Michael P.,
Taylor Nick
Publication year - 2003
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.703
Subject(s) - markov chain , heteroscedasticity , volatility (finance) , futures contract , interval (graph theory) , econometrics , autoregressive conditional heteroskedasticity , index (typography) , futures market , independence (probability theory) , interval data , economics , mathematics , computer science , statistics , finance , combinatorics , world wide web , data envelopment analysis
A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression‐based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated. Copyright © 2003 John Wiley & Sons, Ltd.