CVaR模型的有效边界与组合选择

CVaR模型是经典马柯维茨均值一方差模型的直接推广，即由CVaR来直接代替方差作为风险约束条件，使得投资组合模型在新的度量标准下更加合理。本文证明了基于CVaR约束下投资组合模型有效边界的上凸性，并在收益为正态分布的假定下，结合负指数效用函数，解决了投资组合的选择问题，求得具体的显示解，并得出与均值一方差模型相一致的结论。     

中国开放式基金投资组合风险值的实证—基于Copula-GARCH的分析

结合Copula技术和GARCH模型,建立了投资组合风险分析的Gopula-GARCH模型.由于该模型可以捕捉金融市场间的非线性相关性,因而可用于投资组合VaR的分析.利用这个模型,结合Monte Carlo,模拟技术,对我国第一支开放式基金一华安创新基金的投资组合进行了风险分析.     

沪深300股指期货套期保值实证研究

本文采用协整等分析方法,对沪深300股指标的进行投资组合研究,给出了动态投资组合的操作方法,结果表明基于模型选择的投资组合具有较好的系统均衡性和较高的收益率。同时对静态选择的50只股票组合、基于模型动态选择的7只股票的投资组合以及单个股票投资进行沪深300股指期货套期保值比的模拟实证分析,采用OLS回归模型估计法、双变量向量自回归模型方法、基于协整关系的误差修正模型方法、GARCH模型方法、简化的误差修正模型方法,对不同模型方法下的套期保值比进行实证研究。     

沪深300股指期货套期保值实证研究

本文采用协整筹分析方法,对沪深300 股指标的进行投资组合研究,给出了动态投资组合的操作方法,结果表明基于模型选择的投资组合具有较好的系统均衡性和较高的收益率.同时对静态选择的50只股票组合、基于模型动态选择的7只股票的投资组合以及单个股票投资进行沪深300股指期货套期保值比的模拟实证分析,采用OLS回归模型估计法、双变量向量自回归模型方法、基于协整关系的误差修正模型方法、GARCH模型方法、简化的误差修正模型方法,对不同模型方法下的套期保值比进行实证研究.     

刍议线性规划在证券投资组合决策中的应用

如何选择一个满意的投资组合,在既定条件下实现一个最有效率的风险一收益搭配,是证券投资组合的关键问题。本文在分析Markowitz模型及其发展的基础上提出了一种新的证券组合投资选择模型,与Markowitz模型相比更容易实践,通过建立和求解证券投资风险最小化模糊线性规划模型和投资收益最大化模糊线性规划模型,试图优化证券投资组合,从而作出科学的投资决策。文章最后给出应用示例。     

基于VaR的风险平价模型及实证研究

由于波动率难以描述资产的下行风险,构造了基于VaR的风险平价模型,该模型采用VaR值来度量资产的风险,然后运用风险平价的思想来构造投资组合。实证研究结果表明:基于该模型构造的投资组合虽然在年化收益方面不及传统的等权重投资组合,但是却在风险控制及夏普比率、卡尔玛比率方面具有明显优势。对于追求夏普比率最大化或者卡尔玛比率最大化的投资者来说,基于该模型构造的投资组合不失为合理选择。     

基于Copula模型的沪市A股投资组合风险分析

本文采用Copula—GARCH模型对金融市场中投资组合的风险问题进行分析,并选取沪市A股六支股票进行实证研究,结合Monte Carlo模拟计算了投资组合的VAR值,结果显示对资产进行适当的组合可降低投资者的风险,从而证实了模型的可行性和有效性。     

基于极端损失约束的均值-方差投资组合选择研究

将极端损失约束引入投资组合选择问题研究,构建了极端损失约束的均值一方差投资组合模型,通过拉格朗日乘数法,得到了最优投资组合策略及有效边界的解析解,并就投资策略运用方面进行了实证分析结果表明:在方差一均值坐标中有效边界与经典的均值一方差模型的有效边界形状是一致的,但极端损失约束越紧,有效前沿越向方差一均值坐标的右侧移动;在传统均值-方差模型中加入极端损失约束可以改善投资组合的业绩表现,相对于等权组合策略和最小方差策略而言,适当的极端损失约束可以给投资者带来最好的投资业绩。     

基于效用函数下的投资组合研究

投资组合是分散投资风险的有效途径。由于投资者既追求高收益,又想尽可能地回避风险,所以关于证券投资组合的最大收益、最小风险投资决策问题,提出了一个兼顾收益和风险的效用函数。对投资组合的选择,实际上是以效用最大化作为选择准则。由于大多数投资者都是风险厌恶者,厌恶风险的投资者其效用函数遵循边际效用递减规律,因此本文选用指数型效用函数曲线。文章首先讨论了Markowitz模型投资组合的有效边界,再利用投资者的效用函数,由根的唯一性定理,首先求出投资者的效用,进而求出最优组合下的期望收益率和方差,最后得到最优投资组合的比例。     

商业银行债券投资组合优化研究——基于均值-方差-CVAR模型

商业银行债券投资已经成为金融资产配置的一个重要内容,面对风险日益提高,收益不断降低的债券市场现状,如何调整债券投资组合的配置比重,成为当前商业银行债券投资业务的难题。本文利用均值-方差-CVAR模型作为投资组合的风险度量方法,选择国债、地方政府债、企业债和金融债作为不同的债券品种,分析了商业银行在债券投资组合中策略选择。结论认为,不同的商业银行应当根据自身的投资偏好和风险控制能力,确定不同模式的债券投资组合方案。     

Risky asset pricing based on safety first fund management

We study a portfolio selection model based on Kataoka's safety-first criterion (KSF model in short). We assume that the market is complete but without risk-free asset, and that the returns are jointly elliptically distributed. With these assumptions, we provide an explicit analytical optimal solution for the KSF model and obtain some geometrical properties of the efficient frontier in the plane of probability risk degree z _α and target return r _α. We further prove a two-fund separation and tangency portfolio theorem in the spirit of the traditional mean-variance analysis. We also establish a risky asset pricing model based on risky funds that is similar to Black's zero-beta capital asset pricing model (CAPM, for short). Moreover, we simplify our risky asset pricing model using a derivative risky fund as a reference for market evaluation.     

Nonlinear portfolio selection using approximate parametric Value-at-Risk

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the nonlinear portfolio selection formulation using VaR risk measure is in general a computationally intractable optimization problem. We investigate in this paper nonlinear portfolio selection models using approximate parametric Value-at-Risk. More specifically, we use first-order and second-order approximations of VaR for constructing portfolio selection models, and show that the portfolio selection models based on Delta-only, Delta–Gamma-normal and worst-case Delta–Gamma VaR approximations can be reformulated as second-order cone programs, which are polynomially solvable using interior-point methods. Our simulation and empirical results suggest that the model using Delta–Gamma-normal VaR approximation performs the best in terms of a balance between approximation accuracy and computational efficiency.     

A multicriteria model for portfolio management

The paper presents a new model to support the selection of a portfolio of stocks based on the results of the fieldwork undertaken with fund managers and using direct rating, MACBETH and optimisation techniques. The model consists of defining a benchmark portfolio (in this case, the Dow Jones Eurostoxx50) and scoring its different stocks according to several expected return criteria. Based on this multicriteria value analysis, a procedure is proposed to suggest adjustments to the proportions of the stocks in the portfolio. Finally, the risk of this modified portfolio is taken into consideration in an optimization module that includes constraints concerning the limits of variation for the proportion of each stock.     

Optimal commodity asset allocation with a coherent market risk modeling

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.     

Generalized DEA model of fundamental analysis and its application to portfolio optimization

Fundamental analysis is used in asset selection for equity portfolio management. In this paper, a generalized data envelopment analysis (DEA) model is developed to analyze a firm’s financial statements over time in order to determine a relative financial strength indicator (RFSI) that is predictive of firm’s stock price returns. RFSI is based on maximizing the correlation between the DEA-based score of financial strength and the stock market performance. This maximization involves a difficult binary nonlinear program that requires iterative re-configuration of parameters of financial statements as inputs and outputs. We utilize a two-step heuristic algorithm that combines random sampling and local search optimization. The proposed approach is tested with 230 firms from various US technology-industries to determine optimized RFSI indicators for stock selection. Then, those selected stocks are used within portfolio optimization models to demonstrate the usefulness of the scheme for portfolio risk management.     

The Impact of Venture Capital Monitoring

We show that venture capitalists' (VCs) on‐site involvement with their portfolio companies leads to an increase in both innovation and the likelihood of a successful exit. We rule out selection effects by exploiting an exogenous source of variation in VC involvement: the introduction of new airline routes that reduce VCs' travel times to their existing portfolio companies. We confirm the importance of this channel by conducting a large‐scale survey of VCs, of whom almost 90% indicate that direct flights increase their interaction with their portfolio companies and management, and help them better understand companies' activities.     

Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms

The behaviourally based portfolio selection problem with investor’s loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are: developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large-scale computational results for the prospect theory model have been obtained for real financial market data with up to 225 assets. Previously, as far as we are aware, only small laboratory tests (2–3 artificial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a differential evolution algorithm and a genetic algorithm which are capable of dealing with a large universe of assets have been developed. Specific breeding and mutation, as well as normalization, have been implemented in the algorithms. A tabulated comparative analysis of the algorithms’ parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversification benefit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in a bullish market. However, it performed worse in a bearish market than the index tracking model. A tabulated comparative analysis of the performance of the two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models has been tested out-of-sample in different conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.     

INTERNATIONAL DIVERSIFICATION OF EQUITIES AND FIXED-INCOME SECURITIES

Portfolio selection models have been applied principally to common stocks traded in the United States and in foreign stock markets. This study examines the efficient set of portfolios selected from a choice set that includes returns derived from domestic and international corporate bond and government bond indices as well as domestic and international stock indices. To assess the benefits of international multi-asset diversification, the authors examine the following issues: (1) the extent to which international and domestic fixed-income securities are included in efficient portfolios; (2) the effect on efficient set composition of using the Sharpe portfolio selection model as compared to the Markowitz portfolio selection model; (3) the sensitivity of efficient set characteristics produced from a single-index based portfolio selection model to alternative world market indices; and (4) the correspondence between expected and realized portfolio risk and return for the different portfolio selection models.     

Measuring large comovements in financial markets

A general, copula-based framework for measuring the dependence among financial time series is presented. Particular emphasis is placed on multivariate conditional Spearman's rho (MCS), a new measure of multivariate conditional dependence that describes the association between large or extreme negative returns—so-called tail dependence. We demonstrate that MCS has a number of advantages over conventional measures of tail dependence, both in theory and in practical applications. In the analysis of univariate financial series, data are filtered to remove temporal dependence as a matter of routine. We show that standard filtering procedures may strongly influence the conclusions drawn concerning tail dependence. We give empirical applications to two large data sets of high-frequency asset returns. Our results have immediate implications for portfolio risk management, derivative pricing and portfolio selection. In this context we address portfolio tail diversification and tail hedging. Amongst other aspects, it is shown that the proposed modeling framework improves the estimation of portfolio risk measures such as the value at risk.     

Does Inflation Targeting Help Reduce Financial Dollarization?

We examine whether adopting an inflation‐targeting regime helps reduce financial dollarization as predicted by Ize and Levy Yeyati's ( 2003 ) portfolio model. To address the self‐selection problem of policy adoption, we apply a variety of propensity score matching methods to a large sample of 106 developing countries for the years 1985–2004. We find strong evidence that inflation targeting has large and significant treatment effects on lowering both actual financial dollarization and the model implied minimum variance portfolio dollarization. Our results are robust to alternative samples and model specifications and also to control for additional factors in postmatching regressions.