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.     

高等教育发展与中国制造业结构升级

探究高等教育发展对制造业结构升级的影响和作用机制,有助于推进中国制造业结构升级的进程。基于2008—2017年中国27个省（市、自治区）的省际面板数据,借助中介效应模型,探究了高等教育发展对制造业结构升级的影响及作用机制。研究表明,高等教育可以通过提高人力资本积累、促进技术创新这两条路径推动中国制造业结构升级。行业异质性检验表明,高等教育发展对劳动密集型制造业影响显著为负,对技术密集型制造业影响显著为正;区域异质性分析表明,中部地区高等教育发展对制造业结构升级有显著正向影响。完善高等教育投资体系,并在区域间合理配置高等教育资源以及优化人才培养的学科专业设置,对中国制造业转型升级至关重要。     

县域经济发展的空间集聚特征与优化思路--以重庆市为例

地区之间人口集聚程度和经济集聚程度的差异是现实世界中普遍存在的特征和事实,这个变化趋势在全球范围内迄今依然是有增无减,而且集聚的大潮将不可阻挡。因此本文以经济密度和集聚程度作为主要指标,对重庆市2010年38个区县的集聚发展状况进行了分析,分析结果表明各区县的集聚发展并不平衡,具体可以划分为4个不同的集聚发展类型。从空间格局上看,主要呈现出如下特征：（1）“两翼”区县集聚发展程度整体偏低,（2）“一圈”内部存在差异,（3）长江南北存在明显差距。最后,结合前述分析结论和不同区县所属主体功能区类型,本文提出了有针对性的差异化集聚发展思路,以便促进全市区域经济更加科学协调发展。     

Socially optimal replacement of conventional with electric vehicles for the US household fleet

In this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.     

Do subjective expectations explain asset pricing puzzles?

The structural uncertainty model with Bayesian learning, advanced by Weitzman (AER 2007), provides a framework for gauging the effect of structural uncertainty on asset prices and risk premiums. This paper provides an operational version of this approach that incorporates realistic priors about consumption growth volatility, while guaranteeing finite asset pricing quantities. In contrast to the extant literature, the resulting asset pricing model with subjective expectations yields well-defined expected utility, finite moment generating function of the predictive distribution of consumption growth, and tractable expressions for equity premium and risk-free return. Our quantitative analysis reveals that explaining the historical equity premium and risk-free return, in the context of subjective expectations, requires implausible levels of structural uncertainty. Furthermore, these implausible prior beliefs result in consumption disaster probabilities that virtually coincide with those implied by more realistic priors. At the same time, the two sets of prior beliefs have diametrically opposite asset pricing implications.     

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.     

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.     

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.     

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.     

贸易结构、技术密度与经济增长——一个分析框架及基于中国数据的检验

以往的国际贸易理论都强调，贸易结构在促进经济增长方面起着重要作用。本文通过VAR模型估计了中国的贸易结构与经济增长的关系，结果显示，中国的贸易结构并不显著影响经济增长。