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 (US)
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)
New Economics Papers: this item is included in nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Working Paper: Spectral backtests of forecast distributions with application to risk management (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgfe:2018-21
Ordering information: This working paper can be ordered from
http://www.federalre ... /feds/fedsorder.html
Access Statistics for this paper
More papers in Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (US) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ().