A Dynamic-Programming Approach to Multiperiod Asset Allocation

Academicians and practitioners recently have focused a great deal of attention on the issue of retirement asset allocation. However, research on the academic side typically has assumed a static allocation of a fixed amount over the investor's lifetime, while the advice on the practitioner side has been largely ad hoc in nature. Moreover, both academics and practitioners often fail to link allocations to the individual's attitude toward risk. This paper uses several performance measures that incorporate the individual's aversion to risk and finds the allocations in the year before retirement that maximize the expected value of those performance measures. It then uses a dynamic programming procedure to roll back one year at a time to determine optimal allocations for previous years as well. We find that the traditional advice that young investors should invest more heavily in equity (with a gradual shift to more debt as they near retirement) indeed is correct, and in fact the optimal equity allocation is even higher than commonly suggested. Deviations of the growth in an individual's income from a long-term national average did not seem to significantly affect the optimal allocations. The optimal allocations, however, vary widely as a function of (1) investor attitudes toward risk and (2) accumulated savings to date. These results suggest greater care should be taken to assess and incorporate these factors into the asset-allocation decision.     

Tactical Industry Allocation and Model Uncertainty

We use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out‐of‐sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out‐of‐sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.     

Copula—CVaR资产组合选择模型分析

采用基于MonteCarlo数值模拟技术的Copula—CVaR风险评估模型讨论Copula函数的选择对投资决策的影响,度量资产组合的集成风险,总结出了资产组合风险度量的一般步骤。通过计算资产组合的VaR和CvaR值,实证检验说明：ClaytonCopula由于能更好地刻画尾部特征,从而在危机时期准确度更高。根据该模型进行资产选择可以使投资者的选择更加稳健。     

资产配置中的投资时钟模型

投资时钟模型是将资产配置和行业策略同经济周期相联系的资产配置方法。经济周期划分为衰退、复苏、过热和滞胀四个阶段,各阶段都对应着收益表现超过一般市场的某一特定资产类别:债券、股票、大宗商品和现金。投资时钟模型还可以帮助资产配置的行业选择。在经济复苏阶段,投资于成长性的周期性行业;在过热阶段,投资于价值性的周期性行业;在滞胀时期,投资于价值型的防御性行业;在衰退时期,投资于成长性的防御性行业。     

随机规划模型与商业银行资产的最优配置

有关商业银行资产负债管理模型的研究表明,基于多阶段带简单补偿的资产负债随机模型适合现阶段中国商业银行的资产负债管理问题。本文建立了三大国有上市商业银行的简化资产负债管理模型,运用遗传算法进行运算求解。模型结果能反映商业银行资产配置的基本变化趋势;贷款配置比例始终大于债券投资比例,表明银行仍然立足于传统信贷业务;三大商业银行的最优资产配置略有差异。考虑到商业银行资产收益率以及存款负债流的不确定性,实证结果表明该模型对实际管理决策具有现实指导意义。     

Model Uncertainty, Limited Market Participation, and Asset Prices

We demonstrate that limited participation can arise endogenouslyin the presence of model uncertainty and heterogeneous uncertainty-averseinvestors. When uncertainty dispersion among investors is small,full participation prevails in equilibrium. Equity premium isrelated to the average uncertainty among investors and a conglomeratetrades at a price equal to the sum of its single-segment components.When uncertainty dispersion is large, investors with high uncertaintychoose not to participate in the stock market, resulting inlimited market participation. When limited participation occurs,participation rate and equity premium can decrease in uncertaintydispersion and a conglomerate trades at a discount.     

Learning about Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation

This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a long-horizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a state-dependent relation between the optimal portfolio choice and the investment horizon. There is substantial market timing in the optimal hedge demands, which is caused by stochastic covariance between stock return and dynamic learning. The opportunity cost of ignoring predictability or learning is found to be quite substantial.     

Optimal Multiperiod Asset Allocation: Matching Assets to Liabilities in a Discrete Model

Investment and risk control are becoming increasingly important for financial institutions. Asset allocation provides a fundamental investing principle to manage the risk and return trade‐off in financial markets. This article proposes a general formulation of a first approximation of multiperiod asset allocation modeling for institutions that invest to meet the target payment structures of a long‐term liability. By addressing the shortcomings of both single‐period models and the single‐point forecast of the mean variance approach, this article derives explicit formulae for optimal asset allocations, taking into account possible future realizations in a multiperiod discrete time model.     

Socialism and Intrafirm Asset Allocation

We rely on a survey of Swiss firms to document deviation from first‐best for reasons of internal ‘fairness’ when allocating resources. This ‘socialist’ practice is more widespread in smaller than in larger firms. It ignores the reputation and past performance of the managers who apply for funding, but takes into account their hierarchical position and their past use of resources. Socialism is only partially explained by concerns about empire building and managerial optimism, and it is not meant to benefit shareholders.     

Jumps and Dynamic Asset Allocation

This paper analyzes the optimal dynamic asset allocation problem in economies with infrequent events and where the investment opportunities are stochastic and predictable. Analytical approximations are obtained, with which a thorough comparative study is performed on the impacts of jumps upon the dynamic decision. The model is then calibrated to the U.S. equity market. The comparative analysis and the calibration exercise both show that jump risk not only makes the investor's allocation more conservative overall but also makes her dynamic portfolio rebalancing less dramatic over time.     

Efficient Post-Retirement Asset Allocation

Abstract

To examine post-retirement asset allocation, an extension to the classic Markowitz risk-return framework is suggested. Assuming that retirees make constant (real dollar) annual withdrawals from their portfolios, reward and risk measures are defined to be the mean and standard deviation of wealth remaining at end of life. Asset returns and time of death are both treated as random variables. Assuming constant lifetime asset allocation, the risk and reward measures can be evaluated analytically, and an efficient frontier can be determined. Life annuities can be used to extend the left-hand (low-risk) side of the efficient frontier. The desired level of wealth at end of life can be used to choose a desirable portfolio on the efficient frontier. The desirable portfolio strongly depends on the withdrawal rate. It is suggested (although not proven) that asset allocations strategies that vary with age do not add efficiency in this model, and asset allocation strategies that vary with wealth can add efficiency.     

Asset Allocation in Bankruptcy

This paper investigates the consequences of liquidation and reorganization on the allocation and subsequent utilization of assets in bankruptcy. Using the random assignment of judges to bankruptcy cases as a natural experiment that forces some firms into liquidation, we find that the long‐run utilization of assets of liquidated firms is lower relative to assets of reorganized firms. These effects are concentrated in thin markets with few potential users and in areas with low access to finance. These findings suggest that when search frictions are large, liquidation can lead to inefficient allocation of assets in bankruptcy.     

高维的相关性建模及其在资产组合中的应用

考虑高维相关结构的典型事实和可操作性,本文构建了机制转换的动态等相关(RS-DEC)模型,并给出了模型参数估计的步骤和大样本性质。RS-DEC模型不仅可以对高维资产建模,而且能够刻画资产间相关结构突变和非对称的特征。实证考察了上交所97只股票的组合问题。RS-DEC模型具有很好的样本内拟合效果,且平滑概率能提供相关结构变化的时点信息;与Na$\ddot{\shortmid}$ve(1/N)策略相比,基于Sharpe比和最小标准差的业绩评估标准,样本外测试显示RS-DEC模型能改善资产配置的绩效,显著性检验也支持了该结论。     

Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model

We investigate the implications of time-varying expected returnand volatility on asset allocation in a high dimensional setting.We propose a dynamic factor multivariate stochastic volatility(DFMSV) model that allows the first two moments of returns tovary over time for a large number of assets. We then evaluatethe economic significance of the DFMSV model by examining theperformance of various dynamic portfolio strategies chosen bymean-variance investors in a universe of 36 stocks. We findthat the DFMSV dynamic strategies significantly outperform variousbenchmark strategies out of sample. This outperformance is robustto different performance measures, investor’s objectivefunctions, time periods, and assets.     

A Factor Allocation Approach to Optimal Bond Portfolio

This paper proposes a new method to a bond portfolio problem in a multi-period setting. In particular, we apply a factor allocation approach to constructing the optimal bond portfolio in a class of multi-factor Gaussian yield curve models. In other words, we consider a bond portfolio problem in terms of a factors’ allocation problem. Thus, we can obtain clear interpretation about the relation between the change in the shape of a yield curve and dynamic optimal strategy, which is usually hard to be obtained due to high correlations among individual bonds. We first present a closed form solution of the optimal bond portfolio in a class of the multi-factor Gaussian term structure model. Then, we investigate the effects of various changes in the term structure on the optimal portfolio strategy through series of comparative statics.     

Dynamic Asset Allocation under Inflation

We develop a simple framework for analyzing a finite-horizon investor's asset allocation problem under inflation when only nominal assets are available. The investor's optimal investment strategy and indirect utility are given in simple closed form. Hedge demands depend on the investor's horizon and risk aversion and on the maturities of the bonds included in the portfolio. When short positions are precluded, the optimal strategy consists of investments in cash, equity, and a single nominal bond with optimally chosen maturity. Both the optimal stock–bond mix and the optimal bond maturity depend on the investor's horizon and risk aversion.