EconPapers    
Economics at your fingertips  
 

Spectral backtests of forecast distributions with application to risk management

Michael Gordy () and Alexander J. McNeil

Papers from arXiv.org

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.

New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
Date: 2017-08, Revised 2019-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
http://arxiv.org/pdf/1708.01489 Latest version (application/pdf)

Related works:
Working Paper: Spectral Backtests of Forecast Distributions with Application to Risk Management (2018) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1708.01489

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().

 
Page updated 2019-10-13
Handle: RePEc:arx:papers:1708.01489