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Right Tail (right + tail)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Business, Economics, Finance and Accounting	50%

Selected Abstracts

Stock Options and the Corporate Demand for Insurance

JOURNAL OF RISK AND INSURANCE, Issue 2 2006
Li-Ming Han
This article shows that a corporate manager compensated in stock options makes corporate decisions to maximize stock option value. Overinvestment is a consequence if risk increases with investment. Facing the choice of hedging corporate risk with forward contracts on a stock market index fund and insuring pure risks the manager will choose the latter. Hedging with forwards reduces weight in both tails of corporate payoff distribution and thus reduces option value. Insuring pure risks reduces the weight in the left tail where the options are out-of-the-money and increases the weight in the right tail where the options are in-the-money; the effect is an increase in the option value. Insurance reduces the overinvestment problem but no level of insurance coverage can reduce investment to that which maximizes the shareholder value. [source]

The restricted likelihood ratio test at the boundary in autoregressive series

JOURNAL OF TIME SERIES ANALYSIS, Issue 6 2009
Willa W. Chen
Abstract., The restricted likelihood ratio test, RLRT, for the autoregressive coefficient in autoregressive models has recently been shown to be second-order pivotal when the autoregressive coefficient is in the interior of the parameter space and so is very well approximated by the distribution. In this article, the non-standard asymptotic distribution of the RLRT for the unit root boundary value is obtained and is found to be almost identical to that of the in the right tail. Together, these two results imply that the distribution approximates the RLRT distribution very well even for near unit root series and transitions smoothly to the unit root distribution. [source]

A Semiparametric Estimate of Treatment Effects with Censored Data

BIOMETRICS, Issue 3 2001
Ronghui Xu
Summary. A semiparametric estimate of an average regression effect with right-censored failure time data has recently been proposed under the Cox-type model where the regression effect ,(t) is allowed to vary with time. In this article, we derive a simple algebraic relationship between this average regression effect and a measurement of group differences in K -sample transformation models when the random error belongs to the Gp family of Harrington and Fleming (1982, Biometrika69, 553,566), the latter being equivalent to the conditional regression effect in a gamma frailty model. The models considered here are suitable for the attenuating hazard ratios that often arise in practice. The results reveal an interesting connection among the above three classes of models as alternatives to the proportional hazards assumption and add to our understanding of the behavior of the partial likelihood estimate under nonproportional hazards. The algebraic relationship provides a simple estimator under the transformation model. We develop a variance estimator based on the empirical influence function that is much easier to compute than the previously suggested resampling methods. When there is truncation in the right tail of the failure times, we propose a method of bias correction to improve the coverage properties of the confidence intervals. The estimate, its estimated variance, and the bias correction term can all be calculated with minor modifications to standard software for proportional hazards regression. [source]

Value-at-risk for long and short trading positions

JOURNAL OF APPLIED ECONOMETRICS, Issue 6 2003
Pierre Giot
In this paper we model Value-at-Risk (VaR) for daily asset returns using a collection of parametric univariate and multivariate models of the ARCH class based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed density models when the left and right tails of the distribution of returns must be modelled. Thus, VaR for traders having both long and short positions is not adequately modelled using usual normal or Student distributions. We suggest using an APARCH model based on the skewed Student distribution (combined with a time-varying correlation in the multivariate case) to fully take into account the fat left and right tails of the returns distribution. This allows for an adequate modelling of large returns defined on long and short trading positions. The performances of the univariate models are assessed on daily data for three international stock indexes and three US stocks of the Dow Jones index. In a second application, we consider a portfolio of three US stocks and model its long and short VaR using a multivariate skewed Student density. Copyright © 2003 John Wiley & Sons, Ltd. [source]