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UID:20260601T044308EDT-3570J2lvZp@132.216.98.100
DTSTAMP:20260601T084308Z
DESCRIPTION:Spectral Backtests of Forecast Distributions with Application t
 o Risk Management.\n\nIn this talk we study a class of backtests for forec
 ast distributions in which the test statistic is a spectral transformation
  that weights exceedance events by a function of the modelled probability 
 level. The choice of the kernel function makes explicit the user’s priorit
 ies for model performance. The class of spectral backtests includes tests 
 of unconditional coverage and tests of conditional coverage. We show how t
 he class embeds a wide variety of backtests in the existing literature\, a
 nd propose novel variants as well. We assess the size and power of the bac
 ktests in realistic sample sizes\, and in particular demonstrate the trade
 off between power and specificity in validating quantile forecasts.\n\n  
 \n
DTSTART:20170929T183000Z
DTEND:20170929T193000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Alexander J. McNeil\, University of York
URL:/mathstat/channels/event/alexander-j-mcneil-univer
 sity-york-270541
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