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Further results on forecasting and model selection under asymmetric loss
Author(s) -
Christoffersen Peter F.,
Diebold Francis X.
Publication year - 1996
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/(sici)1099-1255(199609)11:5<561::aid-jae406>3.0.co;2-s
Subject(s) - uniqueness , heteroscedasticity , selection (genetic algorithm) , piecewise linear function , computer science , function (biology) , mathematical optimization , econometrics , piecewise , process (computing) , model selection , mathematics , machine learning , mathematical analysis , geometry , evolutionary biology , biology , operating system
We make three related contributions. First, we propose a new technique for solving prediction problems under asymmetric loss using piecewise‐linear approximations to the loss function, and we establish existence and uniqueness of the optimal predictor. Second, we provide a detailed application to optimal prediction of a conditionally heteroscedastic process under asymmetric loss, the insights gained from which are broadly applicable. Finally, we incorporate our results into a general framework for recursive prediction‐based model selection under the relevant loss function.