Board of Governors of the Federal Reserve System (US)
Finance and Economics Discussion Series
Spectral Backtests of Forecast Distributions with Application to Risk Management
We study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modeled probability level. The choice of the kernel function makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and propose novel variants as well. In an empirical application, we backtest forecast distributions for the overnight P&L of ten bank trading portfolios. For some portfolios, test results depend materially on the choice of kernel.
Cite this item
Michael B. Gordy & Alexander J. McNeil, Spectral Backtests of Forecast Distributions with Application to Risk Management, Board of Governors of the Federal Reserve System (US), Finance and Economics Discussion Series 2018-021, 23 Mar 2018.
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
Keywords: Backtesting ; Risk management ; Volatility
This item with handle RePEc:fip:fedgfe:2018-21
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