Static Hedging of Geometric Average Asian Options with Standard Options

In this article, we first establish a theorem that represents the price of an Asian option in terms of standard European options with a shorter term and different strikes. Then using Gauss–Hermite numerical integration, we discretize our theorem so as to use Monte Carlo simulation to examine the error of the static hedging under the Black–Scholes model and the Merton jump-diffusion model. For ease of comparison, we also provide the error of the dynamic hedging. The numerical results show that the static hedging strategy performs better than the dynamic one under both models.     

Phase I trial design for drug combinations with Bayesian model averaging

Various statistical models have been proposed for two‐dimensional dose finding in drug‐combination trials. However, it is often a dilemma to decide which model to use when conducting a particular drug‐combination trial. We make a comprehensive comparison of four dose‐finding methods, and for fairness, we apply the same dose‐finding algorithm under the four model structures. Through extensive simulation studies, we compare the operating characteristics of these methods in various practical scenarios. The results show that different models may lead to different design properties and that no single model performs uniformly better in all scenarios. As a result, we propose using Bayesian model averaging to overcome the arbitrariness of the model specification and enhance the robustness of the design. We assign a discrete probability mass to each model as the prior model probability and then estimate the toxicity probabilities of combined doses in the Bayesian model averaging framework. During the trial, we adaptively allocated each new cohort of patients to the most appropriate dose combination by comparing the posterior estimates of the toxicity probabilities with the prespecified toxicity target. The simulation results demonstrate that the Bayesian model averaging approach is robust under various scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

When police patrols matter. The effect of police proximity on citizens’ crime risk perception

Crime risk perception is known to be an important determinant of individual well-being. It is therefore crucial that we understand the factors affecting this perception so that governments can identify the (public) policies that might reduce it. Among such policies, public resources devoted to policing emerge as a key instrument not only for tackling criminal activity but also for impacting on citizens’ crime risk perception. In this framework, the aim of this study is to analyze both the individual and neighbourhood determinants of citizens’ crime risk perception in the City of Barcelona (Spain) focusing on the effect of police proximity and taking into account the spatial aspects of neighbourhood characteristics. After controlling for the possible problems of the endogeneity of police forces and crime risk perception and the potential sorting of individuals across neighbourhoods, the results indicate that crime risk perception is reduced when non-victims exogenously interact with police forces. Moreover, neighbourhood variables, such as proxies of social capital and the level of incivilities, together with individual characteristics have an impact on citizens’ crime risk perception.     

Bayesian Variable Selection Under Collinearity

In this article, we highlight some interesting facts about Bayesian variable selection methods for linear regression models in settings where the design matrix exhibits strong collinearity. We first demonstrate via real data analysis and simulation studies that summaries of the posterior distribution based on marginal and joint distributions may give conflicting results for assessing the importance of strongly correlated covariates. The natural question is which one should be used in practice. The simulation studies suggest that posterior inclusion probabilities and Bayes factors that evaluate the importance of correlated covariates jointly are more appropriate, and some priors may be more adversely affected in such a setting. To obtain a better understanding behind the phenomenon, we study some toy examples with Zellner’s g-prior. The results show that strong collinearity may lead to a multimodal posterior distribution over models, in which joint summaries are more appropriate than marginal summaries. Thus, we recommend a routine examination of the correlation matrix and calculation of the joint inclusion probabilities for correlated covariates, in addition to marginal inclusion probabilities, for assessing the importance of covariates in Bayesian variable selection.     

Small Area Estimation Using Estimated Population Level Auxiliary Data

Unit level linear mixed models are often used in small area estimation (SAE), and the empirical best linear unbiased prediction (EBLUP) is widely used for the estimation of small area means under such models. However, EBLUP requires population level auxiliary data, atleast area specific aggregated values. Sometimes population level auxiliary data is either not available or not consistent with the survey data. We describe a SAE method that uses estimated population auxiliary information. Empirical results show that proposed method for SAE produces an efficient set of small area estimates.     

D-optimal Factorial Designs under Generalized Linear Models

Generalized linear models (GLMs) have been used widely for modeling the mean response both for discrete and continuous random variables with an emphasis on categorical response. Recently Yang, Mandal and Majumdar (2013 Yang, J., Mandal, A., Majumdar, D. (2013). Optimal designs for 2^k factorial experiments with binary response. Technical Report, Available at: http://arxiv.org/pdf/1109.5320v4.pdf. [Google Scholar]) considered full factorial and fractional factorial locally D-optimal designs for binary response and two-level experimental factors. In this article, we extend their results to a general setup with response belonging to a single-parameter exponential family and for multilevel predictors.     

中国企业出口与生产率关系实证研究——对制造业企业自我选择效应和出口中学效应的分析

运用2005—2008年中国14个制造业企业面板数据,分析企业出口的自我选择效应和出口中学效应,结果表明:中国制造业企业出口与生产率的关系违背异质性企业贸易模型,自我选择效应不显著,并且存在"生产率悖论";出口中学效应也不显著。虽然出口企业的生产率与出口规模正相关,但在控制其他影响出口规模的变量后,生产率与出口规模则呈现负相关;在控制其他影响生产率的变量后,出口规模与生产率同样也会呈现负相关。中国企业出口的自我选择效应不显著的原因在于其加工贸易等现实条件与经典理论假设的背离;而中国企业出口中学效应不明显的重要原因则在于其自我选择效应的不显著和规模报酬递减。     

盈余质量与资本成本——来自中国上市公司的经验证据

盈余质量和资本成本一直是西方实证研究的热点,以2001年～2004年中国上市公司数据为样本,采用DD模型和Jones模型计量盈余质量,检验公司盈余质量对资本成本的影响.研究结果表明,盈余质量显著影响公司资本成本,公司盈余质量越高资本成本越低.按照产权性质分类回归的结果表明,无论是国有还是民营上市公司,盈余质量越高资本成本越低,这在一定程度上说明市场能区分质量好坏的公司,能有效引导资源优化配置.     

全球经济社会转型与马克思社会哲学模式的再认识

建立在劳动和资本分析基础上的“经济基础-上层建筑”、“自然关系-社会关系”双层冲突论,构成马克思社会哲学的完整模式。“西方”与“非西方”世界构成当今全球经济社会转型紧密联系的两面。仅从西方世界内部来看,其消费社会转型使“消费”、“社会关系”等上升为其社会矛盾的主要方面,“生产”、“自然关系”等则相对退居其次,这导致其内部社会冲突的“去自然化”;而从全球范围来看,西方通过资本跨国流通把作为“人与自然之间的物质变换活动”的生产转移到非西方国家,则是将全球社会冲突“再自然化”的现实依据。“自然关系一社会关系”之间的冲突,构成当今全球经济社会转型中的主要矛盾之一,重构马克思社会哲学模式,具有极其重大的时代意义和本土意义。     

Has the Nordic Welfare Model Been Transformed?

The Nordic welfare model is undergoing a fundamental transformation. Using Denmark we show how a universal welfare state model is gradually being transformed into an emergent multi‐tiered welfare state. Whereas the Danish pension system's having become multi‐tiered in the 1990s, with private schemes – collective and individual – supplementing public schemes is well documented, scant attention has focused on more recent developments in other areas of the welfare state. This article shows how the multi‐tiered welfare state spread in the 2000s to policies for families, the unemployed and the sick. Although Denmark still offers universal coverage in core welfare state areas, the increased use of occupational and fiscal welfare as well as changes in public schemes has gradually transformed the nation into a multi‐tiered welfare state that is more dualistic and individualistic, with participation in the labour market becoming still more important for entitlement to benefits. These profound changes have taken place in such a way that although core characteristics are still in place, new structures and understandings of the welfare state are also developing. Thus classical typologies need revision, so that they include more focus on this combination of universality and institutional attachment to the labour market. Moreover, measures of what welfare comprises should include not only public but also private elements.