Portfolio frontiers with restrictions to tracking error volatility and value at risk

Asset managers are often given the task of restricting their activity by keeping both the value at risk (VaR) and the tracking error volatility (TEV) under control. However, these constraints may be impossible to satisfy simultaneously because VaR is independent of the benchmark portfolio. The management of these restrictions is likely to affect portfolio performance and produces a wide variety of scenarios in the risk-return space. The aim of this paper is to analyse various interactions between portfolio frontiers when risk managers impose joint restrictions upon TEV and VaR. Specifically, we provide analytical solutions for all the intersections and we propose simple numerical methods when such solutions are not available. Finally, we introduce a new portfolio frontier.     

基于copula的投资组合选择模型的研究

本文首先介绍了投资组合理论与copula,然后给出基于概率的收益率等定义,建立基于概率的收益率的投资组合选择模型并给出具体解法,接着通过选取上证领先指数与深证领先指数2004年9月1日至2006年5月26日的日收盘数据进行实证分析,发现在收益率(基于概率的收益率)一定的情况下,通过投资组合可以降低风险。pápápá     

Long-term U.S. infrastructure returns and portfolio selection

Our understanding of the long-term return behavior and portfolio characteristics of public infrastructure investments is limited by a relatively short history of empirical data. We re-construct U.S. listed infrastructure index returns by mapping their monthly performance to received systematic and industry risk factors from 1927 through 2010. Our findings reveal that the infrastructure returns in recent years may understate the tail-risk that investors could experience over the long-term, however, this tail-risk is commensurate with holding a broad portfolio of U.S. stocks. For mean-variance and mean-CVaR investors, we report the benefits of holding public infrastructure assets in investment portfolios.     

How does the choice of Value-at-Risk estimator influence asset allocation decisions?

Considering the growing need for managing financial risk, Value-at-Risk (VaR) prediction and portfolio optimisation with a focus on VaR have taken up an important role in banking and finance. Motivated by recent results showing that the choice of VaR estimator does not crucially influence decision-making in certain practical applications (e.g. in investment rankings), this study analyses the important question of how asset allocation decisions are affected when alternative VaR estimation methodologies are used. Focusing on the most popular, successful and conceptually different conditional VaR estimation techniques (i.e. historical simulation, peak over threshold method and quantile regression) and the flexible portfolio model of Campbell et al. [J. Banking Finance. 2001, 25(9), 1789–1804], we show in an empirical example and in a simulation study that these methods tend to deliver similar asset weights. In other words, optimal portfolio allocations appear to be not very sensitive to the choice of VaR estimator. This finding, which is robust in a variety of distributional environments and pre-whitening settings, supports the notion that, depending on the specific application, simple standard methods (i.e. historical simulation) used by many commercial banks do not necessarily have to be replaced by more complex approaches (based on, e.g. extreme value theory).     

The selection of multinational equity portfolios: forecasting models and estimation risk

The problem considered is the selection of a portfolio of international assets, particularly the forecasting of the inputs to a selection algorithm. Four models of the asset return generating process are considered, two of which ignore the international nature of the universe of assets, two which exploit it in different ways. Several estimation methods are considered for each component: expected return, variance and covariance of returns. The combinations of model and estimation method are first evaluated in terms of their forecasting performance for the components mentioned for the individual assets. The universe used is the components of the Financial Times Eurotrack 100 Index. Significant differences were found between the forecasting accuracy of the methods considered for each component. In the final stage of the analysis, a comparison of the returns on portfolios chosen using each combination showed a significant difference. The analysis suggests that the choice of estimation method is more critical than the choice of pricing model.     

基于VaR的开放式股票型基金市场风险的测量与评价

通过采用半参数法计算投资组合VaR,得到相应VaR的近似置信区间,并结合成分VaR、边际VaR对投资组合vaR进行分解,结果发现,VaR作为风险管理工具同样可以有效应用于开放式股票型基金市场风险的测量与评价.     

Forecasting market risk of portfolios: copula-Markov switching multifractal approach

This paper proposes a new methodology for modeling and forecasting market risks of portfolios. It is based on a combination of copula functions and Markov switching multifractal (MSM) processes. We assess the performance of the copula-MSM model by computing the value at risk of a portfolio composed of the NASDAQ composite index and the S&P 500. Using the likelihood ratio (LR) test by Christoffersen [1998. “Evaluating Interval Forecasts.” International Economic Review 39: 841–862], the GMM duration-based test by Candelon et al. [2011. “Backtesting Value at Risk: A GMM Duration-based Test.” Journal of Financial Econometrics 9: 314–343] and the superior predictive ability (SPA) test by Hansen [2005. “A Test for Superior Predictive Ability.” Journal of Business and Economic Statistics 23, 365–380] we evaluate the predictive ability of the copula-MSM model and compare it to other common approaches such as historical simulation, variance–covariance, RiskMetrics, copula-GARCH and constant conditional correlation GARCH (CCC-GARCH) models. We find that the copula-MSM model is more robust, provides the best fit and outperforms the other models in terms of forecasting accuracy and VaR prediction.     

模型危机还是应用不当造成的危机?——对《高盛风险之海市蜃楼》一文批驳VaR模型之反驳

次贷风波所引起的金融危机不但使国际银行业一度岌岌可危,而且沉重打击了世界经济。作为市场风险管理的核心,Vag模型遭到各方诟病。通过分析《高盛风险之海市蜃楼》一文,本文归纳了对Vag模型的几点认识误区,包括忽略对外部数据准确性的考核与检验,在模型应用上未能对正常市场情况与极端市场情况区别对待等。本文进而论证Vag仍是当前风险管理的首选工具,提出对收益率概率密度分布的选择将是Vag模型进一步发展的核心与关键,强调数据分析,指出量化研究应作为主观判断的前提与基础等观点。     

Should investors include green bonds in their portfolios? Evidence for the USA and Europe

The green bond market has seen a rapid growth world widely in recent years. This paper explores the role of green bonds in asset allocation using the dynamic R-vine copula-based mean-CVaR approach. We compare the performance of portfolios including green bonds with that of portfolios including conventional bonds in the U.S. and European markets. Empirical results show that portfolios with green bonds outperform portfolios with conventional bonds in terms of risk-adjusted returns in the majority of cases in both markets. The benefit of green bonds comes from both the increase in the return and the decrease in the volatility for most of the cases. Overall, our findings suggest that green bonds are beneficial to investors.     

基于边际效应的银行个体系统性风险贡献测度研究

本文使用系统性风险β值法度量我国上市银行的系统性风险以及上市银行对系统性风险的贡献度。研究结果表明,单个机构对系统性风险的贡献不仅取决于系统性风险β值,还受到其个体风险值的影响。总体而言,国有大型商业银行的系统性风险β值高于中小股份制商业银行,对系统性风险的边际贡献和影响也较大。但中小股份制商业银行抵御风险的能力相对较弱,尽管β值较小,一旦个体风险值急剧增加,其对系统性风险的影响也可能超过国有大型商业银行。因此,系统性风险的防范既要关注那些系统性风险β值大的银行,也要关注个体风险值可能出现剧烈波动的中小银行。