Spectral backtests of forecast distributions with application to risk management

Michael Gordy and Alexander J. McNeil

Journal of Banking & Finance, 2020, vol. 116, issue C

Abstract: 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.

Keywords: Backtesting; Volatility; Risk management (search for similar items in EconPapers)
JEL-codes: C52 G21 G28 G32 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Spectral backtests of forecast distributions with application to risk management (2019)
Working Paper: Spectral Backtests of Forecast Distributions with Application to Risk Management (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:116:y:2020:i:c:s0378426620300844

DOI: 10.1016/j.jbankfin.2020.105817

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