基于最优负债系数的上市银行违约概率测算模型与实证

当上市银行的长期负债系数γ的取值不同时,应用KMV模型测算出的银行违约概率大相径庭。根据债券的实际信用利差可以推算出上市银行的违约概率PDi,CS,根据长期负债系数γ可以运用KMV模型确定上市银行的理论违约概率PDi,KMV。本文通过理论违约率与实际违约率的总体差异∑ni=1|PDi,KMV-PDi,cs|最小的思路建立规划模型,确定了KMV模型的最优长期负债γ系数;通过最优长期负债系数γ建立了未发债上市银行的违约率测算模型、并实证测算了我国14家全部上市银行的违约概率。本文的创新与特色一是采用KMV模型计算的银行违约概率PDi,KMV与实际信用利差确定的银行违约概率PDi,CS总体差异∑ni=1|PDi,KMV-PDi,cs|最小的思路建立规划模型,确定了KMV模型中的最优长期负债γ系数;使γ系数的确定符合资本市场利差的实际状况,解决了现有研究中在0和1之间当采用不同的长期负债系数γ、其违约概率的计算结果截然不同的问题。二是实证研究表明,当长期负债系数γ=0.7654时,应用KMV模型测算出的我国上市银行违约概率与我国债券市场所接受的上市银行违约概率最为接近。三是实证研究表明国有上市银行违约概率最低,区域性的上市银行违约概率较高,其他上市银行的违约概率居中。     

Asset allocation under threshold autoregressive models

We discuss the asset allocation problem in the important class of parametric non‐linear time series models called the threshold autoregressive model in (J. Roy. Statist. Soc. Ser. A 1977; 140 :34–35; Patten Recognition and Signal Processing. Sijthoff and Noordhoff: Netherlands, 1978; and J. Roy. Statist. Soc. Ser. B 1980; 42 :245–292). We consider two specific forms, one self‐exciting (i.e. the SETAR model) and the other smooth (i.e. the STAR) model developed by Chan and Tong (J. Time Ser. Anal. 1986; 7 :179–190). The problem of maximizing the expected utility of wealth over a planning horizon is considered using a discrete‐time dynamic programming approach. This optimization approach is flexible enough to deal with the optimal asset allocation problem under a general stochastic dynamical system, which includes the SETAR model and the STAR model as particular cases. Numerical studies are conducted to demonstrate the practical implementation of the proposed model. We also investigate the impacts of non‐linearity in the SETAR and STAR models on the optimal portfolio strategies. Copyright © 2011 John Wiley & Sons, Ltd.     

基于神经网络的股票收益率预测研究

在金融领域的资产定价模型修正过程中,股市的非线性现象往往被选择性忽视,未纳入模型框架,现有模型亦无法刻画因子之间的非线性定价结构。为解决上述问题,引入了机器学习领域中的神经网络模型,以捕获市场组合收益率、市值、账面市值比三因子间的非线性定价结构,并对股票收益率进行预测。将该模型与经典Fama-French三因子模型在样本外拟合优度、多空策略业绩表现上做了对比,结果表明：神经网络模型能精准捕获市场组合收益率、市值、账面市值比3个因子之间的非线性关系,且在样本外拟合优度、多空策略业绩表现上均要优于传统三因子线性定价模型。     

B股相对于A股的市场投资价值研究——基于均值-方差张成与Block-Bootstrap模拟的分析

本文采用均值-方差张成的方法研究B股相对于A股的市场投资价值。首先,通过MonteCarlo模拟研究了均值-方差张成检验的小样本性质,发现模型的GMM-Wald检验存在显著的小样本偏倚,故采用基于残差再抽样的Block-Bootstrap方法模拟Wald统计量的分布以克服小样本偏倚的影响;接着分别对A、B股构造规模资产组合作为其资产的代理变量,利用模拟的Wald统计量进行实证研究,结果发现:B股未向国内居民开放前,相对于A股具有投资价值,向国内居民开放后,相对于A股不再具有投资价值。文章最后对B股投资价值的这种变化作出了经济解释,并提出相应的政策建议。     

真实利率波动对长期资产配置的影响(英文)

对于单期的投资者而言,无违约风险的固定收益证券被视为无风险资产.这是因为固定收益证券的收益率在投资的初期就能确定.然而在考虑长期的投资时,投资者可以调整资产配置,固定收益证券也将面临再投资的利率波动风险,因此不能再被视为无风险资产.本文在一类特殊的"习惯形成"效用函数的框架下讨论长期资产配置.在一系列为简化问题而作的假设之下,本文推导出了真实利率波动对风险资产配置权重的影响,并且为计算实际长期资产配置的最优比例提供了理论依据和算法.     

Multivariate time changes for Lévy asset models: Characterization and calibration

We build a theoretical framework for multivariate subordination of Brownian motions, with a common and an idiosyncratic component. This follows economic intuition and introduces generalizations of some well known multivariate Lévy processes for financial applications: the compound Poisson, NIG, Variance Gamma and CGMY. In most cases we obtain the characteristic function in closed form. The extension is first kept parsimonious, by adding one parameter only. The empirical fit of (linear) dependence is then increased, by allowing for dependent Brownian motions.     

Strategic foreign reserves risk management: Analytical framework

We present an analytical framework for active foreign exchange reserves management that integrates risk-return objectives with macroeconomic, macro-prudential and sovereign debt management concerns. Our framework allows for very general objective functions, does not restrict the class of eligible stochastic processes or limit the investment universe, and can incorporate many types of macroeconomic concerns. It incorporates several kinds of risk constraints in order to obtain benchmarks satisfying possible central bank requirements of safety, liquidity, returns, and stability. Feedback between outcomes and decisions is easy using tools that reshape distributions and functions of the outcomes. And the model can be run on a PC-based platform. We apply the framework to several common reserves management problems focusing especially on the formulation of model equations, generation of trees and estimation of density functions of outcomes. We compare our approach to those used by many central banks and discuss advantages to our approach.     

网络安全风险的模糊层次综合评估模型

针对网络安全风险评估中人为因素多、指标难以量化的问题，在分析网络安全要素的基础上，将模糊数学的方法运用于网络安全风险评估中，并结合层次分析方法，建立了网络安全风险的模糊层次综合评估模型．该模型首先建立逻辑的3级网络层次，即服务层、主机层和网络层．在服务层通过对资产、威胁和漏洞各因子的量化计算后得出各自的风险值，然后利用模糊评价方法逐级计算各层风险指数．实验数据测试表明：通过3个层次自下而上地递阶评价各安全要素，利用先局部后整体的评估策略能直观地给出系统的安全态势，并且能准确评估网络系统3个层次的安全状况．     

Modeling dependencies between rating categories and their effects on prediction in a credit risk portfolio

The internal‐rating‐based Basel II approach increases the need for the development of more realistic default probability models. In this paper, we follow the approach taken in McNeil A and Wendin J 7 (J. Empirical Finance 2007) by constructing generalized linear mixed models for estimating default probabilities from annual data on companies with different credit ratings. The models considered, in contrast to McNeil A and Wendin J 7 (J. Empirical Finance 2007), allow parsimonious parametric models to capture simultaneously dependencies of the default probabilities on time and credit ratings. Macro‐economic variables can also be included. Estimation of all model parameters are facilitated with a Bayesian approach using Markov chain Monte Carlo methods. Special emphasis is given to the investigation of predictive capabilities of the models considered. In particular, predictable model specifications are used. The empirical study using default data from Standard and Poor's gives evidence that the correlation between credit ratings further apart decreases and is higher than the one induced by the autoregressive time dynamics. Copyright © 2008 John Wiley & Sons, Ltd.     

Holdings of financial assets: A Markov chain analysis

This paper introduces the Markov chain model as a simple tool for analyzing the pattern of financial asset holdings over time. The model is based on transition probabilities which give the probability of switching $1 of wealth from one asset to another. An illustrative application is provided.