Arrow–Debreu equilibria for rank‐dependent utilities with heterogeneous probability weighting

We study Arrow–Debreu equilibria for a one‐period‐two‐date pure exchange economy with rank‐dependent utility agents having heterogeneous probability weighting and outcome utility functions. In particular, we allow the economy to have a mix of expected utility agents and rank‐dependent utility ones, with nonconvex probability weighting functions. The standard approach for convex economy equilibria fails due to the incompatibility with second‐order stochastic dominance. The representative agent approach devised in Xia and Zhou (2016) does not work either due to the heterogeneity of the weighting functions. We overcome these difficulties by considering the comonotone allocations, on which the rank‐dependent utilities become concave. Accordingly, we introduce the notion of comonotone Pareto optima, and derive their characterizing conditions. With the aid of the auxiliary problem of price equilibria with transfers, we provide a sufficient condition in terms of the model primitives under which an Arrow–Debreu equilibrium exists, along with the explicit expression of the state‐price density in equilibrium. This new, general sufficient condition distinguishes the paper from previous related studies with homogeneous and/or convex probability weightings.     

How does population density influence agricultural intensification and productivity? Evidence from Malawi

This article uses nationally representative household-level panel data from Malawi to estimate how rural population density impacts agricultural intensification and household well-being. We find that areas of higher population density are associated with smaller farm sizes, lower real agricultural wage rates, and higher real maize prices. Any input intensification that occurs seems to be going to increasing maize yields, as we find no evidence that increases in population density enable farmers to increase gross value of crop output per hectare. We also find evidence that households in more densely populated areas increasingly rely on off-farm income to earn a living, but there appears to be a rural population density threshold beyond which households can no longer increase off-farm income per capita.     

我国钢铁企业技术创新能力分布及演变态势研究

本文依据我国20家大中型上市钢铁企业1998～2013年各项技术创新指标的面板数据,在对企业技术创新能力进行综合评价的基础上,运用非参数Kernel密度估计方法及马尔可夫链分析模型,对我国钢铁企业技术创新能力的分布及发展演变态势进行了研究,研究结果显示：①我国钢铁企业技术创新能力呈现出“单峰→双峰→单峰”的演变规律,钢铁企业技术创新能力水平总体上都得到了一定程度的提高,但不同钢铁企业技术创新能力的发展速度和水平呈现出明显的不平衡性;②不同钢铁企业Kernel密度分布状态与企业所在地区铁矿资源禀赋和工业经济发展水平存在高度关联性;③钢铁企业技术创新能力经过长期发展和积累过程,客观上存在着低水平状态逐渐向高水平状态转移的现实可能性,但由于不同钢铁企业自身的差异性特征,技术创新能力发展将表现出不同步性;④加快企业优化重组和提高生产能力集中度、加强现代高新技术对钢铁产业的改造和升级、加大政府政策引导和科技资金投入是实现钢铁企业技术创新能力快速提升的重要途径。     

Financial density selection

We propose and study simple but flexible methods for density selection of skewed versions of the two most popular density classes in finance, the exponential power distribution and the t distribution. For the first type of method, which simply consists of selecting a density by means of an information criterion, the Schwarz criterion stands out since it performs well across density categories, and in particular when the DGP is normal. For the second type of method, general-to-specific density selection, the simulations suggest that it can improve the recovery rate in predictable ways by changing the significance level. This is useful because it enables us to increase (reduce) the recovery rate of non-normal densities by increasing (reducing) the significance level, if one wishes to do so. The third type of method is a generalisation of the second type, such that it can be applied across an arbitrary number of density classes, nested or non-nested. Finally, the methods are illustrated in an empirical application.     

中国省际劳均收入的收敛研究——基于核密度函数和Markov随机过程理论的分析

采用核密度函数和Markov随机过程理论研究了我国省区劳均收入分布的动态演变和发展趋势。研究发现,改革前我国省区劳均收入为多峰分布,其遍历分布为偏向低收入的双峰分布;改革后,劳均收入分布经历了从多峰到单峰再到双峰的演变过程,其遍历分布为偏向高收入的双峰分布。从长期发展趋势看,改革后我国多数地区将会收敛于高收入区间。     

SERIES EXPANSION OF THE SABR JOINT DENSITY

Under the SABR stochastic volatility model, pricing and hedging contracts that are sensitive to forward smile risk (e.g., forward starting options, barrier options) require the joint transition density. In this paper, we address this problem by providing closed‐form representations, asymptotically, of the joint transition density. Specifically, we construct an expansion of the joint density through a hierarchy of parabolic equations after applying total volatility‐of‐volatility scaling and a near‐Gaussian coordinate transformation. We then establish an existence result to characterize the truncation error and provide explicit joint density formulas for the first three orders. Our approach inherits the same spirit of a small total volatility‐of‐volatility assumption as in the original SABR analysis. Our results for the joint transition density serve as a basis for managing forward smile risk. Through numerical experiments, we illustrate the accuracy of our expansion in terms of joint density, marginal density, probability mass, and implied volatilities for European call options.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Safety at play: let's all help to prevent drowning

The association between place of residence, population density, relief and type of event (collision or non-collision of the vehicle) has not been evaluated in developing countries. The main objective of this study is to determine the differential factors associated with the occurrence of deaths of collision and non-collision automobile users in Patagonia, Argentina. A multiple logistic regression analysis was performed using as the dependent variable death by car accident (collision or non-collision of the vehicle) and sex, age, place of residence of the victim, relief and population density as the independent variables. Collision fatalities were related to areas of high population density, while non-collision fatalities were related to areas of low population density, mountainous landscape and place of residence of the victims outside the Patagonian region. The results obtained in this study indicate the need to develop differential primary prevention policies by place of residence of car occupants, focusing on Patagonia non-resident drivers and by emphasising non-collision accidents.     

Time Changes for Lévy Processes

The goal of this paper is to consider pure jump Lévy processes of finite variation with an infinite arrival rate of jumps as models for the logarithm of asset prices. These processes may be written as time-changed Brownian motion. We exhibit the explicit time change for each of a wide class of Lévy processes and show that the time change is a weighted price move measure of time. Additionally, we present a number of Lévy processes that are analytically tractable, in their characteristic functions and Lévy densities, and hence are relevant for option pricing.     

Traffic density,congestion externalities,and urbanization in China

ABSTRACT

Although there is an abundant regional literature analyzing traffic congestion, only a few studies have explored extending such analysis with spatial effects. This study uses a dynamic spatial Durbin model and city-level panel data for the period 2003–14 to investigate the spatial spillover effects of traffic congestion on urbanization in China. The results show that there is an inverted ‘U’-shaped relationship between urbanization and traffic density in local and neighbouring cities, and congestion effects have appeared. In the short and long run, the spatial effects of traffic congestion have become an important force restricting the effective promotion of urbanization in China.