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On the choice of scoring functions for forecast comparisons

University of British Columbia

Forecasting of risk measures is an important part of risk management for financial institutions. Value-at-Risk and Expected Shortfall are two commonly used risk measures and accurately predicting these risk measures enables financial institutions to plan adequately for possible losses. Point forecasts from different methods can be compared using consistent scoring functions, provided the underlying functional to be forecasted is elicitable. It has been shown that the choice of a scoring function from the family of consistent scoring functions does not influence the ranking of forecasting methods as long as the underlying model is correctly specified and nested information sets are used. However, in practice, these conditions do not hold, which may lead to discrepancies in the ranking of methods under different scoring functions. We investigate the choice of scoring functions in the face of model misspecification, parameter estimation error and nonnested information sets. We concentrate on the family of homogeneous consistent scoring functions for Value-at-Risk and the pair of Value-at-Risk and Expected Shortfall and identify conditions required for existence of the expectation of these scoring functions. We also assess the finite-sample properties of the Diebold-Mario Test, as well as examine how these scoring functions penalize for over-prediction and under-prediction with the aid of simulation studies.

Text

2017-04-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/61318

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/