Spectral backtests of forecast distributions with application to risk management
Michael Gordy () and
Alexander J. McNeil
Papers from arXiv.org
We study a class of backtests for forecast distributions in which the test statistic depends on a spectral transformation that weights exceedance events by a function of the modeled probability level. The weighting scheme is specified by a kernel measure which 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 further propose novel variants which are easily implemented, well-sized and have good power. 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.
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
Date: 2017-08, Revised 2019-07
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Working Paper: Spectral Backtests of Forecast Distributions with Application to Risk Management (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1708.01489
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