Quantile forecasts for financial volatilities based on parametric and asymmetric models

For financial volatilities such as realized volatility and volatility index, a new parametric quantile forecast strategy is proposed, focusing on forecast interval and value at risk (VaR) forecast. This fully addresses asymmetries in 3 parts: mean, volatility and distribution. The asymmetries are addressed by the LHAR (leverage heterogeneous autoregressive) model of McAleer and Medeiros (2008) and Corsi and Reno (2009) for the mean part, by the EGARCH model for the volatility part, and by the skew-t distribution for the error distribution part. The method is applied to the realized volatilities and the volatility indexes of the US S&P 500 index, the US NASDAQ index, the Korea KOSPI index in which significant asymmetries are identified. Considerable out-of-sample forecast improvements of the forecast interval and VaR forecast are demonstrated for the volatilities: forecast intervals of volatilities have better coverages with shorter lengths and VaR forecasts of volatility indexes have better violations if asymmetries are properly addressed rather than ignored. The proposed parametric method reveals considerably better out-of-sample performance than the recently proposed semiparametric quantile regression approach of Zikes and Barunik (2016).     

Equal‐tailed confidence intervals for comparison of rates

Several methods are available for generating confidence intervals for rate difference, rate ratio, or odds ratio, when comparing two independent binomial proportions or Poisson (exposure‐adjusted) incidence rates. Most methods have some degree of systematic bias in one‐sided coverage, so that a nominal 95% two‐sided interval cannot be assumed to have tail probabilities of 2.5% at each end, and any associated hypothesis test is at risk of inflated type I error rate. Skewness‐corrected asymptotic score methods have been shown to have superior equal‐tailed coverage properties for the binomial case. This paper completes this class of methods by introducing novel skewness corrections for the Poisson case and for odds ratio, with and without stratification. Graphical methods are used to compare the performance of these intervals against selected alternatives. The skewness‐corrected methods perform favourably in all situations—including those with small sample sizes or rare events—and the skewness correction should be considered essential for analysis of rate ratios. The stratified method is found to have excellent coverage properties for a fixed effects analysis. In addition, another new stratified score method is proposed, based on the t‐distribution, which is suitable for use in either a fixed effects or random effects analysis. By using a novel weighting scheme, this approach improves on conventional and modern meta‐analysis methods with weights that rely on crude estimation of stratum variances. In summary, this paper describes methods that are found to be robust for a wide range of applications in the analysis of rates.     

Evolution of victimization,tolerance and detection of intimate partner violence among young Spanish women / Evolución de victimización,tolerancia y detección de la violencia de pareja entre mujeres jóvenes españolas

Intimate partner violence that takes place in first couple relationships has received increasing attention in recent years, leading our knowledge on different topics to grow remarkably. The objective of this study is two-fold: on the one hand, we compare levels of victimization and tolerance for two samples, from two waves of assessment; and on the other hand, we focus on coherence among victimization and the use of labels such as abuse, fear and feeling trapped in the relationship in both waves. A total sample of 3,844 women between the ages of 16 and 21 took part in the study, 18% in the first wave in 2003–05 and 82% in the second wave in 2011–13. Data were collected using the Cuestionario de Violencia de Novios (CUVINO), a questionnaire that provides information on the prevalence of and tolerance towards eight different kinds of abuse, along with self-labelling questions on the perception of abuse. Results showed a similarity in prevalence and labelling in both waves of assessment, with higher tolerance in the second wave. We discuss the implications of these findings for improving current prevention programs on dating violence.     

Exploring Homophobia in Tbilisi,Georgia

The purpose of this study is to determine statistical predictors of homophobic attitudes among the residents of Tbilisi, Georgia. We analyze 2013 survey data from a representative sample of the Tbilisi adult population. Residents were asked about their attitudes, beliefs, and political and social values in the context of the May 17, 2013 attack on LGBT activists on the International Day Against Homophobia and Transphobia (IDAHOT). Findings show that homophobia is significantly predicted by male gender, lower levels of education, acceptance of social inequality, nonliberal attitudes, and perceiving homosexuals as a “threat to national security.” However, psychological perceptions and personal experiences also indirectly influence homophobic attitudes: the findings suggest that males report homophobic attitudes more often than females do and tend to be even more homophobic when they believe that homosexuality is inborn rather than acquired. The study also found that people without liberal attitudes tend to be more homophobic when they have personal contacts with homosexuals. This article highlights the need for a more comprehensive approach to education and the promotion of liberal values as well as legal equality for LGBTQ individuals to decrease the level of homophobia in Georgian society and, specifically, in Tbilisi.     

Bayesian estimation and prediction based on lognormal record values

In this paper we consider the problems of estimation and prediction when observed data from a lognormal distribution are based on lower record values and lower record values with inter-record times. We compute maximum likelihood estimates and asymptotic confidence intervals for model parameters. We also obtain Bayes estimates and the highest posterior density (HPD) intervals using noninformative and informative priors under square error and LINEX loss functions. Furthermore, for the problem of Bayesian prediction under one-sample and two-sample framework, we obtain predictive estimates and the associated predictive equal-tail and HPD intervals. Finally for illustration purpose a real data set is analyzed and simulation study is conducted to compare the methods of estimation and prediction.     

Confidence intervals for a population proportion using group testing in the presence of misclassification

In this study, we consider the construction of the confidence interval for the population proportion while using group testing with misclassification. We propose two confidence intervals based on Cornish-Fisher expansion and a modified Wilson’s interval based on a newly developed estimator. We investigate the performance of these intervals extensively and also apply the methods to real datasets. Our newly derived methods have competitive performance compared with existing methods.     

Confidence intervals for coefficients of variation in two-parameter exponential distributions

This article examines confidence intervals for the single coefficient of variation and the difference of coefficients of variation in the two-parameter exponential distributions, using the method of variance of estimates recovery (MOVER), the generalized confidence interval (GCI), and the asymptotic confidence interval (ACI). In simulation, the results indicate that coverage probabilities of the GCI maintain the nominal level in general. The MOVER performs well in terms of coverage probability when data only consist of positive values, but it has wider expected length. The coverage probabilities of the ACI satisfy the target for large sample sizes. We also illustrate our confidence intervals using a real-world example in the area of medical science.     

Equal-tailed and shortest Bayesian tolerance intervals based on exponential k-records

The problem of constructing the equal-tailed and shortest Bayesian tolerance intervals that control percentages in both tails of the exponential distribution based on k-record values is considered. Equal-tailed and shortest Bayesian tolerance factors are derived. Practical examples using real and simulated k-record values are given to illustrate the proposed results.     

Economic design based on a multivariate Bayesian VSI chart with a dual control limit

In this paper, a multivariate Bayesian variable sampling interval (VSI) control chart for the economic design and optimization of statistical parameters is designed. Based on the VSI sampling strategy of a multivariate Bayesian control chart with dual control limits, the optimal expected cost function is constructed. The proposed model allows the determination of the scheme parameters that minimize the expected cost per time of the process. The effectiveness of the Bayesian VSI chart is estimated through economic comparisons with the Bayesian fixed sampling interval and the Hotelling's T² chart. This study is an in-depth study on a Bayesian multivariate control chart with variable parameter. Furthermore, it is shown that significant cost improvement may be realized through the new model.     

Reliability inference for a general lower-truncated family of distributions under records

Based on record values, point and interval estimators are proposed in this paper for the parameters of a general lower-truncated family of distributions. Maximum likelihood and bias-corrected estimators are obtained for unknown model parameters. Based on a sufficient and complete statistic, the bias-corrected estimator is also shown to be uniformly minimum variance unbiased estimator. Different exact confidence intervals and exact confidence regions are constructed for the both model and truncated parameters, and other confidence interval estimates based on asymptotic distribution theory and bootstrap approaches are obtained as well. Finally, two real-life examples and a numerical study are presented to illustrate the performance of our methods.