Robust portfolio choice for a defined contribution pension plan with stochastic income and interest rate

This paper considers a robust portfolio choice problem for a defined contribution pension plan with stochastic income and stochastic interest rate. The investment objective of the pension plan is to maximize the expected utility of the wealth at the retirement time. We assume that the financial market consists of a stock, a zero-coupon bond and a risk-free asset. And the member of defined contribution pension plan is ambiguity-averse, which means that the member is uncertain about the expected return rate of the bond and stock. Meanwhile, the member's ambiguity-aversion level toward these two financial assets is quite different. The closed-form expressions of the robust optimal investment strategy and the corresponding value function are derived by adopting the stochastic dynamic programming approach. Furthermore, the sensitive analysis of model parameters on the optimal investment strategy are presented. We find that the member's aversion on model ambiguity increases her hedging demand and has remarkable impact on the optimal investment strategy. Moreover, we demonstrate that ignoring model uncertainty will lead to significant utility loss for the ambiguity-averse member, and the model uncertainty about the stock dynamics implies greater effect on the outcome of the investment than the bond.     

Bayes Estimates of Markov Trends in Possibly Cointegrated Series

Stylized facts show that average growth rates of U.S. per capita consumption and income differ in recession and expansion periods. Because a linear combination of such series does not have to be a constant mean process, standard cointegration analysis between the variables to examine the permanent income hypothesis may not be valid. To model the changing growth rates in both series, we introduce a multivariate Markov trend model that accounts for different growth rates in consumption and income during expansions and recessions and across variables within both regimes. The deviations from the multivariate Markov trend are modeled by a vector autoregression (VAR) model. Bayes estimates of this model are obtained using Markov chain Monte Carlo methods. The empirical results suggest the existence of a cointegration relation between U.S. per capita disposable income and consumption, after correction for a multivariate Markov trend. This result is also obtained when per capita investment is added to the VAR.     

资产配置模型的选择:回报、风险抑或二者兼具

近年来以风险平价为代表的基于风险的配置模型广为流行。这些模型的一大特点是放弃回报信息。而以均值方差模型代表的基于回报的配置模型则认为回报很重要而且默认对回报的预测是准确的。这两种做法都有问题。考虑到回报的可预测性得到了大量经验研究的支持,那么对于基于风险的配置模型而言,完全放弃回报则意味着有关回报的有用信息得不到充分利用。对于基于回报的配置模型而言,不考虑参数估计误差而且对输入参数敏感的缺点也大大抵消了它们利用回报信息带来的好处。那么,回报是否重要以及应该如何使用回报成了资产配置研究所面临的一个重大问题。为此,本文提出以风险平价为配置基准,以贝叶斯VAR回报预测为主观观点的Black-Litterman(贝叶斯BL)模型回答这一命题。利用1952-2016年的美国股票和债券季度数据,本文将贝叶斯BL模型与现有配置模型进行比较研究。实证结果表明,相比基于回报的配置模型,贝叶斯BL模型降低了组合风险;相比基于风险的配置模型,贝叶斯BL模型增强了组合回报。这些特性来自于它既能利用回报可预测性带来的有用信息,又能够发挥基于风险的配置模型在控制风险方面的优势。因此该模型表现出增强回报和控制风险兼具的特点,是一条具有潜力的资产配置新方案。     

中国房地产市场波动对宏观经济波动的影响研究

 房地产的波动会通过多种途径影响一国的宏观经济状况。为说明房地产市场的波动对我国宏观经济的影响,我们通过协整和VAR分析,实证了：①房地产价格波动对我国消费的影响,得出房价波动对社会消费品零售总额的波动有显著的负影响,且房价波动对消费波动的方差贡献最小都大于2.5%左右;②我国房地产投资波动对经济增长的影响,研究表明,房地产投资额的波动对GDP的增长率有显著的正影响,当房地产投资额的增长率上升1个百分点时,GDP增长率上涨0.181个百分点,且1个单位的房地产投资波动的冲击在第4个季度时达到最大,之后缓慢衰减,这说明房地产投资的波动对GDP有长期的影响;③我国房地产价格波动对通货膨胀的影响,计量结果表明,当房价的增长速度上升1个百分点时,通货膨胀率则上升0.118个百分点,且通货膨胀率对房价波动冲击的响应比较小,在第8个月达到最大,以后则开始衰减,而且达到的最大的影响以后,甚至出现负的、不稳定的影响。     

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We illustrate our approach on an equity database that consists of stocks from the Standard and Poor's index, and we compare our portfolios to this benchmark index. Our methodology differs from the usual quadratic programming approach to portfolio selection in three ways: (1) We employ informative priors on the expected returns and variance-covariance matrices, (2) we use daily data for estimation purposes, with upper and lower holding limits for individual securities, and (3) we use a dynamic asset-allocation approach that is based on reestimating and then rebalancing the portfolio weights on a prespecified time window. The key inputs to the optimization process are the predictive distributions of expected returns and the predictive variance-covariance matrix. We describe the statistical issues involved in modeling these inputs for high-dimensional portfolio problems in which our data frequency is daily. In our application, we find that our optimal portfolio outperforms the underlying benchmark.     

行业投资者情绪的测量及其对收益的影响效应研究

投资者情绪对行业收益影响研究的关键在于行业情绪的测量。基于封闭式基金折价、交易量、波动率、行业beta值和每股流通市值这5个单项情绪指标提取主成分,构建了23个申万一级行业的投资者综合情绪指数(IICSI),并通过面板数据模型分析了各行业的投资者情绪对收益的影响效应。实证结果表明,投资者情绪对当期收益具有短期、中期和长期的正向激励作用,但对预期收益则只在短期和中期表现出正向效应,而在长期反转为负向的抑制效应,并且情绪对当期收益和预期收益的影响都具有显著的行业差异性,最后基于情绪对预期收益的行业影响差异构建了不同投资期限下的套利策略。     

Vector autoregression modelling and forecasting growth of South Korea

In this paper, we have estimated vector autoregression (VAR), Bayesian vector autoregression (BVAR) and vector error-correction models (VECMs) using annual time-series data of South Korea for 1950-94. We find evidence supporting the view that growth of real per-capita income has been aided by income, investment and export growth, as well as government spending and exchange rate policies. The VECMs provide better forecasts of growth than do the VAR and BVAR models for both short-term and long-term predictions.     

SUBSAMPLING THE JOHANSEN TEST WITH STABLE INNOVATIONS

By taking into account the thick-tail property of the errors, cointegration analysis in vector error-correction models with infinite-variance stable errors is a natural generalization of cointegration analysis in error-correction models with normally distributed errors. We study the Johansen test for cointegrated systems under symmetric stable innovations with discrete spectral measures. The results show that the distributions of the Johansen test statistics under these innovations involve nuisance parameters. To overcome the problem of nuisance parameters, we implement a nonparametric subsampling procedure. We document some subsampling simulation results and demonstrate in an empirical example how the test can be used in practice.     

Sequential monitoring of minimum variance portfolio

This paper evaluates the economic effect of monitoring the minimum variance portfolio weights, which depend solely on the covariance matrix of returns. The investor decides whether the portfolio composition providing the smallest portfolio variance remains optimal at the beginning of every new investment period. For this purpose changes in the optimal weights are sequentially detected by means of EWMA control charts. Signals obtained from monitoring are used for improvement of the covariance matrix estimation procedure. The investment strategy exploiting signals from control charts is compared with a number of alternative approaches in the empirical study.     

Statistical inference of the efficient frontier for dependent asset returns

In the paper we consider the three characteristics of the efficient frontier. These characteristics are estimated by substituting the unknown parameters by the sample counterparts. Assuming that the asset returns follow a stationary Gaussian process it is shown that the estimated characteristics are asymptotically normally distributed. This result is used to determine the joint asymptotic distribution of the estimated portfolio return and the estimated portfolio variance in the case of the expected utility portfolio and the tangency portfolio, respectively.     

Nonparametric testing for long-horizon predictability with persistent covariates

We propose a testing procedure for long-horizon predictability via kernel-based nonparametric estimators of long-run covariances between multiperiod returns and persistent covariates. Asymptotic properties of the proposed tests are studied. As for implementation of the test, sieve bootstrap methods are employed to obtain reasonable approximation to the sample distribution of the test statistics. Monte Carlo simulations are conducted to verify the theoretical conjecture. Empirical analysis, using US monthly data from 1929 to 2011, are presented for testing stock return predictability of some forecasting financial variables. Long-term interest rates, unlike default spreads or price-earning ration, are found to show some forecasting power.     

Comparison of different estimation techniques for portfolio selection

The main problem in applying the mean-variance portfolio selection consists of the fact that the first two moments of the asset returns are unknown. In practice the optimal portfolio weights have to be estimated. This is usually done by replacing the moments by the classical unbiased sample estimators. We provide a comparison of the exact and the asymptotic distributions of the estimated portfolio weights as well as a sensitivity analysis to shifts in the moments of the asset returns. Furthermore we consider several types of shrinkage estimators for the moments. The corresponding estimators of the portfolio weights are compared with each other and with the portfolio weights based on the sample estimators of the moments. We show how the uncertainty about the portfolio weights can be introduced into the performance measurement of trading strategies. The methodology explains the bad out-of-sample performance of the classical Markowitz procedures.     

Linear statistical inference for global and local minimum variance portfolios

Traditional portfolio optimization has often been criticized for not taking estimation risk into account. Estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather than their variances and covariances. The global minimum variance portfolio has been advocated by many authors as an appropriate alternative to the tangential portfolio. This is because there are no expectations which have to be estimated and thus the impact of estimation errors can be substantially reduced. However, in many practical situations an investor is not willing to choose the global minimum variance portfolio but he wants to minimize the variance of the portfolio return under specific constraints for the portfolio weights. Such a portfolio is called local minimum variance portfolio. Small-sample hypothesis tests for global and local minimum variance portfolios are derived and the exact distributions of the estimated portfolio weights are calculated in the present work. The first two moments of the estimator for the expected portfolio returns are also provided and the presented instruments are illustrated by an empirical study.     

Optimal asset allocation for participating contracts with mortality risk under minimum guarantee

Abstract

We investigate an optimal investment problem of participating insurance contracts with mortality risk under minimum guarantee. The insurer aims to maximize the expected utility of the terminal payoff. Due to its piecewise payoff structure, this optimization problem is a non-concave utility maximization problem. We adopt a concavification technique and a Lagrange dual method to solve the problem and derive the representations of the optimal wealth process and trading strategies. We also carry out some numerical analysis to show how the portfolio insurance constraint impacts the optimal terminal wealth.     

Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence

Using survey data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a counter-cyclical fashion. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduce the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R². Expected business conditions retain predictive power even when including the key nonfinancial predictor, the generalized consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, whereas time-varying consumption/wealth may capture time-varying risk aversion.     

公司持有现金的对冲作用

本文探讨融资约束下公司现金策略与投资策略的内生联动性并引入对冲需求的思想,对公司现金持有量促进投资支出的对冲作用进行考察。研究结果表明,公司面临融资约束时通过变动现金策略以避免投资计划的改变,这种联动性导致公司现金策略变化而投资策略不常变动,公司因此具有较高的现金-现金流敏感度且现金-现金流敏感度比投资-现金流敏感度更能够有效反映公司融资约束。我们还发现,现金持有量促进投资的对冲作用受到市场的认同,投资者赋予公司现金策略和投资策略更高的价值评估。     

消费环境对我国农村居民消费影响的实证分析

本文以1983－2009年消费环境和农村居民消费的相关统计数据为样本,使用VEC模型就消费环境对农村居民消费水平的影响进行冲击响应、方差分解分析,考察了消费环境与农村居民消费水平的动态特征关系。实证结果表明：文化水平、医疗卫生条件、基础设施投资、自然条件与农村居民消费水平之间呈现稳定的协整关系,其中,文化水平、基础设施投资对农村居民消费水平的冲击有明显的效果,并且持续时期长,对农村居民消费水平的提高有显著作用。     

Time aggregation and skip sampling in cointegration tests

We examine the change of power of Johansen's VAR MLE cointegration test when samples are aggregated or skipped. We show by Monte Carlo simulation that although there are power gains when switching to high frequency data to gain more observations for a fixed time span, the power gains are much more significant when data with longer time span are used.     

基于因素模型的协高阶矩矩阵估计及其应用研究

在高阶矩投资组合中,使用传统样本估计方法会产生较高估计误差和模型设定误差。本文在多因素模型的基础上,给出一种改进的协高阶矩估计方法,分析了基于多因素模型压缩估计量的渐进一致性。蒙特卡洛模拟表明,多因素压缩估计量在有限样本中具有更小的平均绝对误差、根均方误差以及更高的平均绝对改进百分比,有效提高了协高阶矩矩阵估计的精度;即使在样本观测量比资产数目少时,估计的协高阶矩矩阵精度都会有较大提高。基于2005年6月至2019年5月沪深300成分股的高阶矩投资组合实证发现,多因素压缩方法与其他估计方法相比,在年化收益率上可以获得4.7%~32.8%的提升,最大回撤能够下降3.7%~18.3%,表明使用多因素压缩估计方法构建的投资组合有更大的可能获得更多货币效用增益,以及面临亏损时,产生的最大亏损更小。该方法有助于金融机构或理性投资者在进行投资组合时减小投资损失,获得更好的投资回报。     

居民消费、基本建设投资与GDP关系的实证分析

改革开放以来,中国经济的高速增长已经成为一个不争的事实,文章运用协整和误差修正模型验证消费和基本建设投资对CDP增长的作用。得出结论:无论是从长期均衡还是从短期变动来看,居民消费和基本建设投资对中国经济发展都起到了积极的促进作用,尤其是居民消费。