Spectral Backtests of Forecast Distributions with Application to Risk Management

Michael Gordy and Alexander J. McNeil

No 2018-021, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

Abstract: 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.

Keywords: Backtesting; Risk management; Volatility (search for similar items in EconPapers)
JEL-codes: C52 G21 G28 G32 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2018-03-23
New Economics Papers: this item is included in nep-rmg
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Citations: View citations in EconPapers (1)

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https://www.federalreserve.gov/econres/feds/files/2018021pap.pdf (application/pdf)

Related works:
Journal Article: Spectral backtests of forecast distributions with application to risk management (2020)
Working Paper: Spectral backtests of forecast distributions with application to risk management (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2018-21

DOI: 10.17016/FEDS.2018.021

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