有交易费的无套利资产组合优化性质

针对所给出的有交易费的资产过程模型，引入了资产折算函数以刻划套期保值和套利机会，并利用辅助鞅和凸分析的对偶方法，讨论了该模型下基于无套利分析的资产组合优化可达性的一些性质．     

Mean-CVaR模型下的资产组合和破产风险控制

为了克服尾部风险测度CVaR模型本身的不足，并且给“如何实现资产组合的破产风险与期望利润的最优配置”问题提供一个更加符合现实的答案，本文在CVaR模型基础上，通过把风险资本的来源内生于资本禀赋以及把风险资本的机会成本引入利润函数的方式提出了线性Mean—CVaR模型。同时，本文通过对“上证50”成分股进行选择的实证分析给出了由线形Mean—CVaR模型得到的更加合理的资产组合与资本储备。     

动态资产份额定价理论模型

运用倒向随机微分方程数学方法 ,建立了动态资产份额定价理论模型 .这一模型是资产份额定价法的改进 .求解模型得到动态资产份额定价理论公式 ,并得出结论 :资产份额定价公式完全可以作为特例 ,以离散时间意义和在不考虑动态投资的情况下 ,由动态资产份额定价理论公式得到 .     

不允许卖空的组合投资决策

本建立了一定置信水平下最小收益最大准则下的组合投资决策模型，该模型不仅适用于风险规避，也适用于风险偏好，在风险资产的收益率联合服从正态分布的假设下，给出不容许卖空情形下的求解有效资产组合的算法，并给出一个算例。     

选择资产组合的EP-MV模型及最优解的解析表示

本文提出了存在无风险资产贷出或借入时的有效投资组合模型(EP-MV模型)，研究了不允许卖空(投资比例非负)约束条件下，EP-MV优化模型的算法，给出了有效投资组合投资比例的解析表示．在资产收益由多因素模型产生的基础上，得到了资产与有效投资组合的期望收益及风险的估计，便于实际应用．     

有交易的未定权益无套利定价区间

本文首先给出了有效交易费资产模型下套利机会的定义，利用辅助鞅和资产折算函数等方法，讨论了该模型下未定权益无套利定价问题，得到的结果是有效易费的未定权益无套利定价区间。     

不完全市场下静、动态资产优化配置的等价问题

风险资产个数小于噪声源个数时，市场不完全．通过增加满足一定条件的风险资产，可以构建一个虚构的完全市场。结合路径独立性原则给出静、动态资产优化配置等价的充分必要条件。最后给出几个具体的满足静、动态资产优化配置等价的市场模型。     

看住公共资产

澳大利亚刚刚发布了“综合战略资产管理指南”，它主要是告诉负责管理公营部门资产的专业人员如何妥善管理资产。随着澳大利亚联邦、各州和各领地政府建造项目的资产价值超过6000亿澳元，如何让这些资产发挥最大效益已经成为社会十分关注的问题。     

资本资产定价模型的改进及在中国股市的实证分析

对于投资者来说,具备有效的评估价值和风险关系的理论工具十分重要.以中国股票市场的数据对资本资产定价模型进行了实证检验,面对并不理想的检验结果,借用因素模型的研究方法,在传统模型中引入其他可能影响资产回报的因素,利用中国股市的数据对改进的模型进行了实证检验,并与传统模型的检验结果相比较,发现改进模型的解释力相对于传统模型有了显著的提高.     

信用资产组合优化的"条件在险值-补偿"型随机规划模型

本文对于信用资产组合的优化问题给出了一个稳健的模型，所建模型涉及了条件在险值（CVaR）风险度量以及具有补偿限制的随机线性规划框架，其思想是在CVaR与信用资产组合的重构费用之间进行权衡，并降低解对于随机参数的实现的敏感性．为求解相应的非线性规划，本文将基本模型转化为一系列的线性规划的求解问题．     

Optimal asset allocation aid system: From “one-size” vs “tailor-made” performance ratio

Optimal asset allocation well-fitting investors’ goals is a pressing challenge in risk management. Making a step forward to the Sharpe ratio, the parameter-dependent Sortino–Satchell, Generalized Rachev and Farinelli–Tibiletti performance ratios are suggested for personalizing asset allocation. Tailor-made optimal asset paths for five different investor risk profiles are traced over a rolling 12 month investing horizon. Our simulations show a satisfactorily good match between asset allocation and correspondent risk profile. Specifically, Generalized Rachev ratios outperform in personalized allocation for “extreme” risk profiles, i.e. conservative and aggressive investors, whereas Sortino–Satchell and Farinelli–Tibiletti ratios for those that are more moderate. Sharpe ratio confirms its ability in constructing steady-diversified portfolios, although underperformed.     

A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans

A data-driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed computational framework can scale to high dimensional multi-asset problems. More importantly, the proposed approach can determine the optimal NN control directly from market returns, without assuming a particular parametric model for the return process. We validate the proposed NN learning solution by comparing the NN control to the optimal control determined by solution of the Hamilton–Jacobi–Bellman (HJB) equation. The HJB equation solution is based on a double exponential jump model calibrated to the historical market data. The NN control achieves nearly optimal performance. An alternative data-driven approach (without the need of a parametric model) is based on using the historic bootstrap resampling data sets. Robustness is checked by training with a blocksize different from the test data. In both two and three asset cases, we compare performance of the NN controls directly learned from the market return sample paths and demonstrate that they always significantly outperform constant proportion strategies.     

Optimal asset allocation for pension funds under mortality risk during the accumulation and decumulation phases

In a financial market with one riskless asset and n risky assets whose prices are lognormal, we solve in a closed form the problem of a pension fund maximizing the expected CRRA utility of its surplus till the (stochastic) death time of a representative agent. We consider a unique asset allocation problem for both accumulation and decumulation phases. The optimal investment in the risky assets must decrease during the first phase and increase during the second one. We accordingly suggest it is not optimal to manage the two phases separately, and outsourcing of allocation decisions should be avoided in both phases. JEL: G23, G11 MSC 2000: 62P05, 91B28, 91B30, 91B70, 93E20     

Applying simulation optimization to the asset allocation of a property–casualty insurer

Proper asset allocations are vital for property–casualty insurers to be competitive and solvent. Theories of finance offer little practical guidance in constructing such asset allocations however. This research integrates simulation models with a newly developed evolutionary algorithm for the multi-period asset allocation problem of a property–casualty insurer. We first construct a simulation model to simulate operations of a property–casualty insurer. Then we develop multi-phase evolution strategies (MPES) to be used with the simulation model to search for promising asset allocations for the insurer. A thorough experiment is conducted to evaluate the performance of our simulation optimization approach. Computational results show that MPES is an effective search algorithm. It dominates the grid search method by a significant margin. The re-allocation strategy resulting from MPES outperforms re-balancing strategies significantly. This research further demonstrates that the simulation optimization approach can be used to study economic issues related to multi-period asset allocation problems in practical settings.     

On the performance of West’s bubble test: A simulation approach

In this research we examine the ability of West’s bubble test [1] in detecting speculative bubbles using Brock’s (1982) [2] intertemporal general equilibrium model of asset pricing as the basis for a simulation study. In this setting, (1) the economy, by construction is efficient and produces the maximally possible amount of welfare for society, and (2) asset prices reflect the utility-maximizing behavior of consumers and the profit-maximizing behavior of firms. We find that the West’s bubble test flag as “bubbles” in the simulated data yet the data is produced from an economy in which markets are efficient in welfare production.     

Tracking models and the optimal regret distribution in asset allocation

We present a tracking model for asset allocation that tracks desired investment goals. The model is shown to be optimal with respect to an investor's ‘regret distribution’, the cumulative distribution of the difference between the revenue under perfect foresight and that possible without foresight. Relationships with Markowitz mean/variance models are also explored.     

Asset allocation with distorted beliefs and transaction costs

In this paper we study the problem of the optimal portfolio selection with transaction costs for a decision-maker who is faced with Knightian uncertainty. The decision-maker’s portfolio consists of one risky and one risk-free asset, and we assume that the transaction costs are proportional to the traded volume of the risky asset. The attitude to uncertainty is modeled by the Choquet expected utility. We derive optimal strategies and bounds of the no-transaction region for both optimistic and pessimistic decision-makers. The no-transaction region of a pessimistic investor is narrower and its bounds lie closer to the origin than that of an optimistic trader. Moreover, under the Choquet expected utility the structure of the no-transaction region is not necessarily a closed interval as it is under the standard expected utility model.     

Does the financial market compensate investors for operational Losses?

It is well known that several industries, like the hotel industry, are subject to low frequency high impact events resulting from their operations. However, there is a dearth of academic research in this area. In this paper we propose an innovative methodology to study the problem using a combination of Asset Pricing Models and an original database. We find that asset prices compensate investors not only for market and credit risk, but also for operational risk.     

Defending against multiple different attackers

One defender defends, and multiple heterogeneous attackers attack, an asset. Three scenarios are considered: the agents move simultaneously; the defender moves first; or the attackers move first. We show how the agents’ unit costs of defense and attack, their asset evaluations, and the number of attackers influence their investments, profits, and withdrawal decisions. Withdrawal does not occur in one-period (simultaneous) games between two agents, at least with the commonly used ratio-form contest success function, but can occur in two-period games between two agents. The presence of one particularly strong attacker can cause other attackers to withdraw from the contest if the advantaged attacker appropriates so much of the defender’s asset that it is no longer sufficiently attractive to interest other attackers. In such cases, the defender focuses exclusively on the strong attacker. An advantaged defender may be able to deter attacks by moving first, but will continue to suffer from attacks if moving second. This suggests the importance of proactive rather than reactive defense.