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Evaluating the predictive accuracy of volatility models
Author(s) -
Lopez Jose A.
Publication year - 2001
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/1099-131x(200103)20:2<87::aid-for782>3.0.co;2-7
Subject(s) - scoring rule , volatility (finance) , econometrics , consensus forecast , computer science , calibration , mean squared error , statistics , economics , mathematics , machine learning
Standard statistical loss functions, such as mean‐squared error, are commonly used for evaluating financial volatility forecasts. In this paper, an alternative evaluation framework, based on probability scoring rules that can be more closely tailored to a forecast user's decision problem, is proposed. According to the decision at hand, the user specifies the economic events to be forecast, the scoring rule with which to evaluate these probability forecasts, and the subsets of the forecasts of particular interest. The volatility forecasts from a model are then transformed into probability forecasts of the relevant events and evaluated using the selected scoring rule and calibration tests. An empirical example using exchange rate data illustrates the framework and confirms that the choice of loss function directly affects the forecast evaluation results. Copyright © 2001 John Wiley & Sons, Ltd.