基于经验分布的投资选择

假定股票市场是一列独立同分布的随机市场收益率的理想模型,考虑经济人在任意时刻进入市场开始投资,经过一段时间后离开市场.利用经验对数最优投资组合得到了资金的渐近最优增长率.这一结果确立了普通投资选择与滑动投资模型的密切联系.     

基于动态非线性损失厌恶的投资组合优化与实证研究

从行为金融学的角度考虑投资者损失厌恶的心理特征,构建了基于线性损失厌恶和非线性损失厌恶行为投资组合模型。利用中国市场数据模拟一种静态情景和四种动态情景,实证研究不同损失厌恶投资组合模型在不同情景下不同损失厌恶程度的最优资产配置策略和投资绩效表现,并将结果与均值方差模型等传统的投资组合模型进行比较。研究发现损失厌恶投资组合模型优于传统投资组合模型,不同情景下不同程度损失厌恶投资者具有不同的资产配置策略,其投资绩效表现也不尽相同。     

双边Burr分布及其在金融市场风险预测与优化中的应用

考虑到金融数据具有非对称、尖峰厚尾特征,文章将具有尖峰厚尾特征的Burr分布拓展至双边Burr(TSB)分布,给出了其重要的数字特征、极大似然估计、最小二乘估计以及加权最小二乘估计,并通过数值模拟验证了这三种参数估计方法的有效性.其次,文章基于TSB分布构建GJR-GARCH模型,旨在研究TSB分布相比于常见分布在度量金融风险方面的优势.实证结果表明,与正态分布、t分布、GED分布、双边Weibull分布和双边Lomax分布相比,基于该分布的GJR-GARCH模型具有最高的VaR预测精度.另外,文章将基于TSB分布的GJR-GARCH模型与Copula函数结合来构建均值-CVaR模型以研究多元投资组合的风险优化,实证研究亦表明能够刻画非对称特征的该模型具有更好的CVaR预测效果.最后,稳健性检验结果证实TSB分布对于金融风险预测以及投资组合优化的改进效果不依赖于波动率模型和Copula函数的设定.     

稳定分布的多期投资组合模型及其应用

本文研究了多期投资组合模型的问题.利用非正态稳定分布和参数估计的方法,建立了市场上含一个无风险证券和多个风险证券时多期投资组合的模型,对于描述风险证券所具有的偏态和过度峰态的非正态特征及其股市中的应用起到了作用.     

组合投资与资本资产定价模型

根据效用理论 ,投资者在期望效用最大化准则下选择组合投资方案 ,通过改进均值 -方差模型假定 ,在完全市场 ( perfect markets)条件下由组合投资模型推导出广义的资本资产定价模型 .并证明了在投资者具有二次效用 ,或者收益率服从联合正态分布的情形下 ,它与夏普 -林特纳的资本资产定价模型 ( CA PM)一致     

基于VaR的房地产投资组合模型设计与应用

本文以风险和收益的动态刻画为核心,在房地产投资组合中引入基于VaR模型的风险评价,通过资产收益和预提费用在持有期内的现值构造效用函数,建立基于VaR的投资组合优化模型,实现房地产投资的最优组合。对于上海房地产市场两种不同资产进行组合的实证分析表明该模型具有一定的实用性和有效性。     

基于偏度的多期组合投资调整模型

由于不同时期资产收益率以及投资者对风险和收益偏好的变化，加之资金等条件的限制，大多数组合投资问题具有明显的动态特征。本文把单期投资组合拓展到多期，引入偏度和风险度量工具VaR，并考虑交易费用的影响，建立了多期投资组合调整模型。最后，给出实证分析对模型进行分析研究，这对投资者的连续投资行为具有一定的指导作用。     

几种基于CAPM的最优投资组合构造方案及其比较

本文在William Sharpe的资本资产定价模型（简称CAPM）的基础上,考虑了条件CAPM,就条件CAPM中的β系数为常数和时变系数两种情况,在不同的假设下分别给出了描述真实市场的模型,利用此模型给出了条件CAPM中模型参数的估计方法。对每种不同的描述真实市场的模型,我们选用了上海股市的若干股票构造了最优投资组合,并进行投资组合评估分析,最后对这几种情况下的最优投资组合的表现进行了比较。     

投资控制模型解的渐近性质

本文利用初值比较法和边值比较法，讨论了投资系统解的渐适性质，得到了投资控制模型的平衡解是全局渐近稳定的．     

CVaR风险度量模型在投资组合中的运用

风险价值(VaR)是近年来金融机构广泛运用的风险度量指标，条件风险价值(CVaR)是VaR的修正模型，也称为平均超额损失或尾部VaR，它比VaR具有更好的性质。在本中，我们将运用风险度量指标VaR和CVaR，提出一个新的最优投资组合模型。介绍了模型的算法，而且利用我国的股票市场进行了实证分析，验证了新模型的有效性，为制定合理的投资组合提供了一种新思路。     

Portfolio optimization with a neural network implementation of the coherent market hypothesis

Capital market research seems to be widely governed by traditional static linear models like arbitrage pricing theory and capital asset pricing model, though there is some evidence that better results can be achieved using nonlinear approaches. In this study we described a portfolio optimization model based on artificial neural networks embedded in the framework of a nonlinear dynamic capital market model, the coherent market hypothesis. The main advantage of this theory is that it drops the premise of rational investors and therefore relaxes the precondition of approximately normally distributed stock returns. Neural networks are used to estimate the return distributions in order to forecast the fundamental situation and the level of group behavior of the specific stocks. On the basis of these forecasts the relative stock performance is predicted and used to manage stock portfolios, In a simulation with out-of-sample data from 1991–1994 a portfolio constructed from the eight best ranked stocks achieved an annual return rate about 25% higher than that of the market portfolio and one built from the eight worst ranked stocks attained a return about 25% lower than the market portfolio's return rate. A hedging strategy based on the two aforementioned portfolios leads to a consistently positive annual return of about 25% regardless of the movements of the market portfolio with only 41% of the risk of a buy and hold strategy in the market portfolio.     

Weighted entropy and optimal portfolios for risk-averse Kelly investments

Following a series of works on capital growth investment, we analyse log-optimal portfolios where the return evaluation includes ‘weights’ of different outcomes. The results are twofold: (A) under certain conditions, the logarithmic growth rate leads to a supermartingale, and (B) the optimal (martingale) investment strategy is a proportional betting. We focus on properties of the optimal portfolios and discuss a number of simple examples extending the well-known Kelly betting scheme. An important restriction is that the investment does not exceed the current capital value and allows the trader to cover the worst possible losses. The paper deals with a class of discrete-time models. A continuous-time extension is a topic of an ongoing study.     

On expected utility for financial insurance portfolios with stochastic dependencies

The effect of background risks as human capital, market risks and catastrophic events has been considered in the literature in different contexts. In this note, we consider financial insurance portfolios with insurable risks and one background risk (uninsurable financial asset), such that the random losses and the background risk depend on environmental parameters. We study how dependencies between the risks influence the expected utility of the portfolio’s wealth distribution under risk aversion, when the environmental parameters are random. Stochastic bounds for the expected wealth are given from modeling the dependence between the parameters by different notions. Similar results are given for multivariate portfolios with n groups and multivariate risk aversion, besides an expected utility comparison result for the minimum and the total portfolio’s wealth.     

Robust portfolio selection involving options under a “ marginal+joint ” ellipsoidal uncertainty set

In typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not large enough, the realized returns of resulting portfolios will be outside of the uncertainty set when an extreme event such as market crash or a large shock of asset returns occurs. The goal of this paper is to propose robust portfolio selection models under so-called “ marginal+joint” ellipsoidal uncertainty set and to test the performance of the proposed models. A robust portfolio selection model under a “marginal + joint” ellipsoidal uncertainty set is proposed at first. The model has the advantages of models under the separable uncertainty set and the joint ellipsoidal uncertainty set, and relaxes the requirements on the uncertainty set. Then, one more robust portfolio selection model with option protection is presented by combining options into the proposed robust portfolio selection model. Convex programming approximations with second-order cone and linear matrix inequalities constraints to both models are derived. The proposed robust portfolio selection model with options can hedge risks and generates robust portfolios with well wealth growth rate when an extreme event occurs. Tests on real data of the Chinese stock market and simulated options confirm the property of both the models. Test results show that (1) under the “ marginal+joint” uncertainty set, the wealth growth rate and diversification of robust portfolios generated from the first proposed robust portfolio model (without options) are better and greater than those generated from Goldfarb and Iyengar’s model, and (2) the robust portfolio selection model with options outperforms the robust portfolio selection model without options when some extreme event occurs.     

股市收益率的极限分布

本文提出了一个描述股市收益率与成交量变化率的关系的非线性统计模型.通过这个模型我们证明了收益率序列{rn}依参数不同分别依分布收敛于指数列维稳定分布和列维稳定分布.     

基于几种风险测度的多阶段组合优化研究

基于均值-方差(MV)、VaR(Value at Risk)、CVaR(Conditional VaR)、HMCR(p=1,2,3)(Higher Moment Coherent Risk)几种风险测度进行多阶段组合优化研究。首先从一致性公理和随机占优一致性角度分析几种风险测度的风险识别能力,认为HMCR(p=2,3)的风险识别能力最高,然后给出静态和动态下的风险规避型的规划函数及多阶段CVaR和HMCR模型,最后依据单阶段和多阶段优化模型,对上证50指数成份股进行实证分析。对比单阶段和多阶段下几种风险测度优化组合的累计收益率及几种风险测度之间的关系,结合上证50指数收益率发现,多阶段优化组合要整体优于单阶段优化组合,且HMCR(p=2,3)要优于指数收益率和其它几种风险测度。从投资者投资决策方面来分析,HMCR(p=2,3)型积极投资策略比较适用于股市平稳期、顶峰期和下降期,被动投资策略比较适用于股市上升期。     

博彩型股票与投资者的博彩性偏好——基于中国股票市场数据的实证研究

在新兴资本市场中,股票价格容易受到投资者投资偏好的影响。其中,投资者的博彩性投资偏好是本文研究的重点。本文基于中国股票市场实际情况,借鉴并改进了过往文献对于博彩型股票的分类方法,将高个体偏度、高个体波动率、低价格和高超额换手率的股票定义为博彩型股票,并发现个人投资者过度配置了博彩型股票,存在明显的博彩性投资偏好。本文验证了在中国股票市场中博彩型股票未来收益表现较差的结论,说明投资者偏好博彩型股票的投资行为存在非理性偏误。同时,相对于主板市场而言,在投机性相对较强的中小板和创业板市场中的博彩型股票未来收益表现更差;无论市场在上升或下降环境中,博彩型股票的未来负超额收益一直稳定存在。因此,本文认为应在中国股票市场中加强投资者教育,以降低个人投资者的非理性行为偏差,维护股票市场的健康稳定发展。     

Precautionary measures for credit risk management in jump models

Sustaining efficiency and stability by properly controlling the equity to asset ratio is one of the most important and difficult challenges in bank management. Due to unexpected and abrupt decline of asset values, a bank must closely monitor its net worth as well as market conditions, and one of its important concerns is when to raise more capital so as not to violate capital adequacy requirements. In this paper, we model the trade-off between avoiding costs of delay and premature capital raising, and solve the corresponding optimal stopping problem. In order to model defaults in a bank's loan/credit business portfolios, we represent its net worth by Lévy processes, and solve explicitly for the double exponential jump-diffusion process and for a general spectrally negative Lévy process.     

The development of efficient portfolios in Japan with particular emphasis on sales and earnings forecasting

In this study, we show that earnings forecasting creates an index-tracking portfolio that dominates the historical model trade-off curve. We find that using Toyo Keizai earnings forecasts improves geometric means by over 300 basis points compared to the historical model. Weighted latent root regression is used in this study to create portfolios that have outperformed the Japanese market in backtest and in real-time performance.     

汽车保险损失率期权定价模型及实证研究

随着汽车保险行业的迅速发展,如何通过证券衍生产品来转嫁汽车保险越发引起人们的重视。本文在Taehan Bae等人的研究基础上给出了当索赔额分布服从指数分布、Γ-分布、混合指数分布、对数正态分布时的汽车保险损失率期权的定价公式,并以太平洋保险公司的有关索赔数据作为样本,利用Γ-分布下的汽车保险损失率期权定价公式对其进行实证研究,得到汽车保险损失率期权价格的近似值,具有很好的理论意义和现实意义。