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Title: Spectral backtests of forecast distributions with application to risk management Authors:  Alexander Alexander John McNeil - University of York (United Kingdom) [presenting]
Abstract: A class of backtests for forecast distributions is studied 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. The tests are illustrated by extensive examples in which we consider the performance when essential features of the forecast model are neglected, such as heavy tails and volatility.