The Experts below are selected from a list of 5526 Experts worldwide ranked by ideXlab platform
Jung-bin Su - One of the best experts on this subject based on the ideXlab platform.
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Alternative statistical distributions for estimating value-at-risk: theory and evidence
Review of Quantitative Finance and Accounting, 2012Co-Authors: Jung-bin SuAbstract:A number of applications presume that asset returns are normally distributed, even though they are widely known to be skewed leptokurtic and fat-tailed and excess kurtosis. This leads to the underestimation or overestimation of the true value-at-risk (VaR). This study utilizes a composite Trapezoid Rule, a numerical integral method, for estimating quantiles on the skewed generalized t distribution (SGT) which permits returns innovation to flexibly treat skewness, leptokurtosis and fat tails. Daily spot prices of the thirteen stock indices in North America, Europe and Asia provide data for examining the one-day-ahead VaR forecasting performance of the GARCH model with normal, student’s t and SGT distributions. Empirical results indicate that the SGT provides a good fit to the empirical distribution of the log-returns followed by student’s t and normal distributions. Moreover, for all confidence levels, all models tend to underestimate real market risk. Furthermore, the GARCH-based model, with SGT distributional setting, generates the most conservative VaR forecasts followed by student’s t and normal distributions for a long position. Consequently, it appears reasonable to conclude that, from the viewpoint of accuracy, the influence of both skewness and fat-tails effects (SGT) is more important than only the effect of fat-tails (student’s t ) on VaR estimates in stock markets for a long position.
Cheng-few Lee - One of the best experts on this subject based on the ideXlab platform.
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Alternative statistical distributions for estimating value-at-risk: theory and evidence
Review of Quantitative Finance and Accounting, 2012Co-Authors: Cheng-few LeeAbstract:A number of applications presume that asset returns are normally distributed, even though they are widely known to be skewed leptokurtic and fat-tailed and excess kurtosis. This leads to the underestimation or overestimation of the true value-at-risk (VaR). This study utilizes a composite Trapezoid Rule, a numerical integral method, for estimating quantiles on the skewed generalized t distribution (SGT) which permits returns innovation to flexibly treat skewness, leptokurtosis and fat tails. Daily spot prices of the thirteen stock indices in North America, Europe and Asia provide data for examining the one-day-ahead VaR forecasting performance of the GARCH model with normal, student’s t and SGT distributions. Empirical results indicate that the SGT provides a good fit to the empirical distribution of the log-returns followed by student’s t and normal distributions. Moreover, for all confidence levels, all models tend to underestimate real market risk. Furthermore, the GARCH-based model, with SGT distributional setting, generates the most conservative VaR forecasts followed by student’s t and normal distributions for a long position. Consequently, it appears reasonable to conclude that, from the viewpoint of accuracy, the influence of both skewness and fat-tails effects (SGT) is more important than only the effect of fat-tails (student’s t ) on VaR estimates in stock markets for a long position.
Sever S Dragomir - One of the best experts on this subject based on the ideXlab platform.
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mercer Trapezoid Rule for the riemann stieltjes integral with applications
Journal of Advances in Mathematics, 2013Co-Authors: Mohammad W Alomari, Sever S DragomirAbstract:In this paper several new error bounds for the Mercer–Trapezoid quadrature Rule for the Riemann-Stieltjesintegral under various assumptions are proved. Applications for functions of selfadjoint operators on complexHilbert spaces are provided as well.
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A Generalisation of the Trapezoidal Rule for the Riemann-Stieltjes Integral and Applications
2000Co-Authors: Sever S Dragomir, Constantin Buse, Marius Boldea, Liliana BraescuAbstract:A generalisation of the Trapezoid Rule for the Riemann-Stieltjes integral and applications for special means are given.
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some remarks on the Trapezoid Rule in numerical integration
1999Co-Authors: Sever S Dragomir, P. Cerone, A. SofoAbstract:In this paper, by the use of some classical results from the Theory of Inequalities, we point out quasi-Trapezoid quadrature formulae for which the error of approximation is smaller than in the classical case. Examples are given to demonstrate that the bounds obtained within this paper may be tighter than the classical ones. Some applications for special means are also given.
Jesse A. Berlin - One of the best experts on this subject based on the ideXlab platform.
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statistical characteristics of area under the receiver operating characteristic curve for a simple prognostic model using traditional and bootstrapped approaches
Journal of Clinical Epidemiology, 2002Co-Authors: David J. Margolis, Warren B Bilker, Raymond C Boston, Russell Localio, Jesse A. BerlinAbstract:Prognostic models are increasingly common in the biomedical literature. These models are frequently evaluated with respect to their ability to discriminate between those with and without an outcome. The area under the receiver-operating curve (AROC) is often used to assess discrimination. In this study, we introduce a bootstrap method, and, using Monte Carlo simulation, we compare three different bootstrap approaches with four commonly used methods in their ability to accurately estimate 95% confidence intervals (CIs) around the AROC for a simple prognostic model. We also evaluated the power of a bootstrap method and the commonly used Trapezoid Rule to compare different prognostic models. We show that several good methods exist for calculating 95% CIs of AROC, but the maximum likelihood estimation method should not be used with small sample sizes. We further show that for our simple prognostic model a bootstrap z-statistic approach is preferred over the Trapezoidal method when comparing the AROCs of two related models.
Gen-ichiro Soma - One of the best experts on this subject based on the ideXlab platform.
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Effects of orally administered LPSp on OGTT response, plasma insulin and HbA1c.
2018Co-Authors: Yutaro Kobayashi, Hiroyuki Inagawa, Chie Kohchi, Kimiko Kazumura, Hiroshi Tsuchiya, Toshiyuki Miwa, Katsuichiro Okazaki, Gen-ichiro SomaAbstract:(A–B) In order to evaluate glucose tolerance, an oral glucose tolerance test (OGTT) test was performed at week 16. The glucose level in blood obtained from the tail vein was measured at 0, 15, 30, 60 and 120 min after glucose loading (2 g D-glucose/kg BW). The AUC was calculated using the Trapezoid Rule. (C–D) The fasting plasma insulin and HbA1c levels were measured using commercial kits after the experiment. Values are presented as the mean ± SEM, n = 8. A single asterisk (*) and double asterisks (**) indicate statistically significant differences (p < 0.05 and p < 0.01 respectively) as compared with the control group (two way ANOVA, post-hoc Tukey test). Unless indicated, no significance difference was observed between groups. Different letters indicate statistically significant difference between treatments (p < 0.05, one way ANOVA, post-hoc Tukey test).
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Effects of orally administered LPSp on fasting blood glucose, OGTT response, plasma insulin, and HbA1c.
2018Co-Authors: Yutaro Kobayashi, Hiroyuki Inagawa, Chie Kohchi, Kimiko Kazumura, Hiroshi Tsuchiya, Toshiyuki Miwa, Katsuichiro Okazaki, Gen-ichiro SomaAbstract:(A) The fasting glucose level in blood obtained from the tail vein is measured at every 4th week (weeks 0, 4, 8, 12, and 16). (B–C) In order to evaluate glucose tolerance, an oral glucose tolerance test (OGTT) was performed at week 17. The blood glucose level was measured at 0, 15, 30, 60, and 120 min after glucose loading (2 g D-glucose/kg BW) and AUC was calculated using the Trapezoid Rule. (D–E) The plasma insulin and HbA1c levels were measured after the experiment. Values are presented as the mean ± SEM, n = 4–7. * p < 0.05 for HFD vs. NC; † p < 0.05 for HFD + LPS 0.3 mg/kg vs. HFD; ‡ p < 0.05 for HFD + LPS 1mg/kg vs. HFD (two-way ANOVA followed by Tukey’s multiple-comparisons test). Unless indicated, no significance difference is observed between groups. Differences in letters between bars (e.g., a, b) indicate statistically significant differences between groups (p < 0.05, one-way ANOVA followed by Tukey’s multiple-comparisons test).
