The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform
Zumbach Gilles - One of the best experts on this subject based on the ideXlab platform.
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Tile test for back-testing risk evaluation
2020Co-Authors: Zumbach GillesAbstract:A new test for measuring the accuracy of financial market risk estimations is introduced. It is based on the probability integral transform (PIT) of the ex post realized returns using the ex ante probability distributions underlying the risk estimation. If the forecast is correct, the result of the PIT, that we called probtile, should be an Iid Random Variable with a uniform distribution. The new test measures the variance of the number of probtiles in a tiling over the whole sample. Using different tilings allow to check the dynamic and the distributional aspect of risk methodologies. The new test is very powerful, and new benchmarks need to be introduced to take into account subtle mean reversion effects induced by some risk estimations. The test is applied on 2 data sets for risk horizons of 1 and 10 days. The results show unambiguously the importance of capturing correctly the dynamic of the financial market, and exclude some broadly used risk methodologies.Comment: 22 pages, 12 figure
Gary Hewer - One of the best experts on this subject based on the ideXlab platform.
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A Helmholtz Principle Approach to Parameter Free Change Detection and Coherent Motion Using Exchangeable Random Variables
SIAM Journal on Imaging Sciences, 2011Co-Authors: Arjuna Flenner, Gary HewerAbstract:A parameter free technique for finding changes between images is discussed. The technique uses the Helmholtz principle to find changes in an input image in situations where only one or two images are available to build a background model. The Helmholtz principle locates image regions that are unlikely to occur due to an a priori Random image generation model, and it assigns a confidence level to each changed region. All previous algorithms based on the Helmholtz principle assumed that the image was generated using independent and identically distributed (Iid) Random Variables. The Iid assumption is replaced with the weaker exchangeable assumption, and a simple exchangeable Random Variable model based on the hypergeometric distribution is investigated. Furthermore, practical calculation methods are discussed. The calculation methods require new nonasymptotic bounds to the hypergeometric distribution, and a major contribution of this paper is the novel proof of this bound and its relationship to large deviation theory. The calculations also incorporate the fast level set transform tree structure of the image to create shape boundaries of changed regions. The algorithm is applied to the problems of change detection and coherent motion. We also illustrate that the exchangeable Random Variable model yields more consistent results than an equivalent Iid Random Variable model.
Arjuna Flenner - One of the best experts on this subject based on the ideXlab platform.
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A Helmholtz Principle Approach to Parameter Free Change Detection and Coherent Motion Using Exchangeable Random Variables
SIAM Journal on Imaging Sciences, 2011Co-Authors: Arjuna Flenner, Gary HewerAbstract:A parameter free technique for finding changes between images is discussed. The technique uses the Helmholtz principle to find changes in an input image in situations where only one or two images are available to build a background model. The Helmholtz principle locates image regions that are unlikely to occur due to an a priori Random image generation model, and it assigns a confidence level to each changed region. All previous algorithms based on the Helmholtz principle assumed that the image was generated using independent and identically distributed (Iid) Random Variables. The Iid assumption is replaced with the weaker exchangeable assumption, and a simple exchangeable Random Variable model based on the hypergeometric distribution is investigated. Furthermore, practical calculation methods are discussed. The calculation methods require new nonasymptotic bounds to the hypergeometric distribution, and a major contribution of this paper is the novel proof of this bound and its relationship to large deviation theory. The calculations also incorporate the fast level set transform tree structure of the image to create shape boundaries of changed regions. The algorithm is applied to the problems of change detection and coherent motion. We also illustrate that the exchangeable Random Variable model yields more consistent results than an equivalent Iid Random Variable model.
