重庆市培育金融核心区：布局、路径与对策

金融核心区的培育是提升重庆金融集聚力和辐射力以及构建长江上游地区金融中心的关键因素.在审视金融核心区的概念、内涵和重庆市培育金融核心区的必要性、可能性基础上,运用大系统分析方法的原理,提出重庆金融核心区的规划布局和总体思路;基于产业集群理论探讨重庆金融核心区的培育路径与对策.研究认为,培育重庆金融核心区,要在运筹高端金融商务发展体系、夯实现代金融业发展平台、拓展金融业发展空间等三个方面合理布局金融产业,促进形成金融业产业集群,并优化金融生态环境和金融主体结构、强化金融资源共享和政府公共服务职能.     

南宁创建区域性金融中心的可行性研究

金融中心作为金融竞争最重要的载体和平台,已成为展示一国(地区)综合实力或城市核心竞争力的最重要标志。作为我国大西南经济圈和泛北部湾经济区重要的中心城市,南宁建设区域性金融中心既有比较明显的自身优势,又具有有利的外部发展环境。为此,要抓住机遇,加快建设服务经济跨越式发展的金融平台,凝聚资金、人才和市场,充分发挥中心城市的聚集效应和辐射功能,才能在日趋激烈的区域竞争面前掌握主动,赢得建设区域性金融中心的先机,建构城市经济快速、健康发展的基础。     

金融发展与贸易依存度的实证研究——基于东部十省市面板数据的协整分析

本文利用东部十省市的面板数据,研究金融发展与贸易依存度的关系。运用面板单位根检验和面板协整检验,建立面板协整模型进行分析。结果显示：金融发展仅与进口依存度存在长期均衡关系,且不同省市金融发展对进口依存度的推动作用不同。最后分析其作用差异的潜在原因,并根据实证结果提出了政策建议。     

Bimodal skew-symmetric normal distribution

ABSTRACT

We introduce a new parsimonious bimodal distribution, referred to as the bimodal skew-symmetric Normal (BSSN) distribution, which is potentially effective in capturing bimodality, excess kurtosis, and skewness. Explicit expressions for the moment-generating function, mean, variance, skewness, and excess kurtosis were derived. The shape properties of the proposed distribution were investigated in regard to skewness, kurtosis, and bimodality. Maximum likelihood estimation was considered and an expression for the observed information matrix was provided. Illustrative examples using medical and financial data as well as simulated data from a mixture of normal distributions were worked.     

Generalized Laplacian Distributions and Autoregressive Processes

Generalized Laplacian distribution is considered. A new distribution called geometric generalized Laplacian distribution is introduced and its properties are studied. First- and higher-order autoregressive processes with these stationary marginal distributions are developed and studied. Simulation studies are conducted and trajectories of the process are obtained for selected values of the parameters. Various areas of application of these models are discussed.     

Tukey-Type Distributions in the Context of Financial Data

Abstract

In one-parameter (θ) families, we were not aware of explicit hypothesis testing scenarios where maximal invariant statistics failed to distinguish the models. We start with a concrete example (Sec. 2.2) to highlight such a hypothesis testing problem involving markedly different models. In this problem, because of the absence of a nontrivial uniformly most powerful invariant (UMPI) test, we briefly suggest two approaches to test the hypothesis. The first resolution (Sec. 3.1) is frequentist in nature. It utilizes a weight function on the parameter space and compares “average” distributions obtained under the null and alternative models in the sense of Wald (1947 Wald , A. ( 1947 ). Sequential Analysis . New York : Wiley . [Google Scholar] 1950 Wald , A. ( 1950 ). Statistical Decision Functions . New York : Wiley . [Google Scholar]). In contrast, a fully Bayesian resolution (Sec. 3.2) is also included. The note ends with a series of other interesting examples involving one-parameter families where maximal invariant statistics fail to distinguish the hypothesized models. The examples include easy-to-construct families of probability models involving only a single location or scale parameter θ.     

Regression Properties for Asymmetric Generalized Scale Mixtures of Multivariate Gaussian Variables

Analytical properties of regression and the variance–covariance matrix of asymmetric generalized scale mixture of multivariate Gaussian variables are presented. The analysis includes an in-depth analytical investigation of the first two conditional moments of the mixing variable. Exact computable expressions for the prediction and the conditional variance are presented for the generalized hyperbolic distribution using the inversion theorem for Fourier transforms. An application to financial log returns is demonstrated via the classical Euler approximation. The methodology is illustrated by analyzing the regression of intraday log returns for CISCO against the corresponding data from S&P 500.     

Financial-Firm Production of Monetary Services: A Generalized Symmetric Baroett Variable-Profit-Function Approach

An alternative monetary-production model of financial firms is employed to investigate supply-side monetary aggregation. Financial firms are conceived to produce monetary services as outputs through financial intermediation. A new method for testing the existence of consistent monetary-output aggregates in financial firms' production technology is developed in terms of a multiproduct firm's variable profit function, and the method does not require homotheticity of the aggregator function. We use a generalized symmetric Barnett flexible functional form. That specification satisfies global curvature conditions and retains its flexibility under the null hypothesis of weak separability. Neither of those properties is possessed by other flexible functional forms.     

Dynamic Savings Decisions in Agricultural Environments With Incomplete Markets

We develop a methodology for examining savings behavior in rural areas of developing countries that explicitly incorporates the sequential decision process in agriculture. The approach is used to examine the relative importance of alternative forms of savings in the presence and absence of formal financial intermediaries. Our results, based on stage-specific panel data from Pakistan, provide evidence that the presence of financial intermediaries importantly influences the use of formal savings and transfers for income smoothing. We also find that there are significant biases in evaluations of the savings-income relationship that are inattentive to the within-year dynamics of agricultural production.     

Splines for financial volatility

Summary.  We propose a flexible generalized auto-regressive conditional heteroscedasticity type of model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B -splines of lagged observations and volatilities. Estimation of such a B -spline basis expansion is constructed within the likelihood framework for non-Gaussian observations. As the dimension of the B -spline basis is large, i.e. many parameters, we use regularized and sparse model fitting with a boosting algorithm. Our method is computationally attractive and feasible for large dimensions. We demonstrate its strong predictive potential for financial volatility on simulated and real data, and also in comparison with other approaches, and we present some supporting asymptotic arguments.