带有劳动收入的最优消费和投资问题研究进展

现代投资组合理论一直是金融工程中活跃的研究方向.首先阐述了消费-投资组合问题的研究现状,介绍了不同时期国内外学者的研究成果和方法,接着较为详细地论述了带有劳动收入的最优消费和投资组合问题并给出了所要研究的模型,即含有时变的投资机会和动态劳动收入的模型,最后给出所要研究模型在通胀环境和Knight不确定下的拓展框架.     

基于模糊值损益证券的期望效用优化分析

对由具有模糊值损益证券所构成市场中的期望效用优化问题进行了分析.在分析中使用了加权期望效用模型度量模糊值财富对应的期望效用,提出了模糊意义下的套利概念,证明了最优投资组合存在当且仅当市场不存在模糊意义下的套利机会,重点讨论了最优投资组合的性质,并利用最优组合描述了资产的当前价格.     

在红利支付情形下考虑负效用的消费投资问题

研究了经济代理人面临红利支付和劳动负效用情形下投资与退休选择问题,其中考虑了风险资产派发红利及 劳动会给经济代理人带来效用损失的情形.代理人享有退休选择权,退休决策使得代理人避免了劳动负效用,却必须要放弃工资收入.代理人效用来自消费,并且受劳动负效用的直接影响.利用动态规划的方法去解自由边值问题,得到了代理人临界财富水平和最优消费投资组合策略显示解.     

私募股权投资的估值问题研究进展

阐述了私募股权投资的估值问题.首先简要介绍了投资组合问题的发展历史与研究成果.接着较为详细地论述了私募股权投资的估值问题的研究现状,包括有限合伙人的投资组合选择,以及对投资企业的价值评估体系和评估方法的研究,并给出了所要研究的模型,即含有未支撑风险的私募股权投资模型.最后给出所要研究模型在通胀环境、Knight不确定环境下以及跳扩散环境下的拓展框架.     

组合预测最优权系数的非线性规划确定方法及其在农气预报上的应用

本文利用非线性规划理论对组合预测最优权系数的确定进行探讨,通过Kuhn—Tucker条件,将原来较复杂的组合预测中确定最优仅系数的二次规划模型转化为较为简单的线性规划模型,并将这一模型投人作物产量预报实例中.     

带有习惯形成的最优消费-投资与闲暇选择问题

研究了在带有习惯形成,随机机会集,随机工资和劳动供给弹性的生命周期模型下的消费-投资和闲暇选择问题.在股票支付红利情形下,利用随机微分方程,建立了带有红利情形的证券市场模型.在投资者偏好由不可分离的冯诺依曼·摩根斯坦指数刻画下,给出了投资者最优消费-投资与闲暇选择策略的显式表达式.     

基于遗传算法的最优证券投资组合模型

为了研究能使风险损失率最小、收益最大的最优证券投资组合问题，首先提出了多目标规划模型，并采用模糊优选法将多目标模型单目标化。随后尝试采用遗传算法来对其求解，给出了模型的遗传算法编码规则和算法步骤。最后通过实例论证了模型的合理性及遗传算法的有效性。     

区间系统与区间优化模型——理论与应用

区间系统与区间优化模型是一种应用广泛的描述不确定性问题的数学模型.本文综述区间系统与区间优化模型在理论与应用方面的研究进展.特别地,本文将介绍区间线性系统的解和可解性问题、区间线性优化模型的最优解和最优性问题以及区间二次优化模型的最优解和最优性问题,最后简单概述它们的各种应用实例.     

致洪大暴雨的风险评估及气象效益

该文用概率分布统计推断、马尔科夫模型和统计决策理论对致洪大暴雨进行了风险评估和最优决策,并对防洪气象效益进行了估算.     

基于去噪和分形的加密货币投资组合模型优化研究

为提高投资效益,本文针对传统投资组合模型的缺陷,结合经验模态分解（EMD）去噪法和多重分形消除趋势交叉相关分析法（MF-DCCA）,提出经验模态分解去噪下的多重分形投资组合模型（简称EMD-Mean-MF-DCCA）.将新模型应用于极具投机性的加密货币投资组合,结合滚动窗口技术进行样本外检验和分析,实证结果显示：无论加密货币价格处于上升还是下降趋势,EMD-Mean-MF-DCCA相对于其他传统投资组合模型及未去噪的分形投资组合模型,均在盈利能力和夏普比率方面具有明显优化效果,且当加密货币价格大幅下跌时,基于新模型的组合投资策略也具有较好的抵抗风险能力.     

Multiple adaptation types with mitigation: A framework for policy analysis

Effective climate policy will consist of mitigation and adaptation implemented simultaneously in a policy portfolio to reduce the risks of climate change. Previous studies of the tradeoffs between mitigation and adaptation have implicitly framed the problem deterministically, choosing the optimal paths for all time. Because climate change is a long-term problem with significant uncertainties and opportunities to learn and revise, critical tradeoffs between mitigation and adaptation in the near-term have not been considered. We propose a new framework for considering the portfolio of mitigation and adaptation that explicitly treats the problem as a multi-stage decision under uncertainty. In this context, there are additional benefits to near-term investments if they reduce uncertainty and lead to improved future decisions. Two particular features are fundamental to understanding the relevant tradeoffs between mitigation and adaptation: (1) strategy dynamics over time in reducing climate damages, and (2) strategy dynamics under uncertainty and potential for learning. Our framework strengthens the argument for disaggregating adaption as has been proposed by others. We present three stylized classes of adaptation investment types as a conceptual framework: short-lived “flow” spending, committed “stock” investment, and lower capacity “option” stock with the capability of future upgrading. In the context of sequential decision under uncertainty, these subtypes of adaptation have important tradeoffs among them and with mitigation. We argue that given the large policy uncertainty that we face currently, explicitly considering adaptation “option” investments is a valuable component of a near-term policy response that can balance between the flexible flow and committed stock approaches, as it allows for the delay of costly stock investments while at the same time allowing for lower-cost risk management of future damages.     

Farm programs and climate change

The view that the agricultural sector could largely offset any negative impacts of climate change by altering production practices assumes the government will not create disincentives for farmers to adapt. U.S. farm programs, however, often discourage such obvious adaptations as switching crops, investing in water conserving technologies, and entry or exit. We outline a simple portfolio model describing producer decision making: we then use this framework to assess how specific U.S. farm programs might affect adaption to climate change. Three future climate scenarios are considered and in each the present structure of U.S. farm programs discourages adaptation.     

Optimal mitigation strategies with negative emission technologies and carbon sinks under uncertainty

In recent years a body of literature has arisen on the topic of how to compose the optimal portfolio of mitigation options. The focus has been mainly on options involving shifts from high- to low- or even negative-carbon technologies. Natural sinks play an important role in any attempt to stabilize atmospheric CO₂ and usually enter as a constant term in the overall carbon budget. In this paper, we introduce natural sinks to the carbon management problem and analyze the implications for negative emission technology deployment and the overall mitigation strategy. Amongst other sensitivity analyses, we also investigate the impact of uncertainty in the carbon sink, which we find to raise the importance of negative emissions in the mitigation portfolio significantly lowering the cost of the policy mix.     

Understanding the causes and consequences of differential decision-making in adaptation research: Adapting to a delayed monsoon onset in Gujarat,India

Weather variability poses numerous risks to agricultural communities, yet farmers may be able to reduce some of these risks by adapting their cropping practices to better suit changes in weather. However, not all farmers respond to weather variability in the same way. To better identify the causes and consequences of this heterogeneous decision-making, we develop a framework that identifies (1) which socio-economic and biophysical factors are associated with heterogeneous cropping decisions in response to weather variability and (2) which cropping strategies are the most adaptive, considering economic outcomes (e.g., yields and profits). This framework aims to understand how, why, and how effectively farmers adapt to current weather variability; these findings, in turn, may contribute to a more mechanistic and predictive understanding of individual-level adaptation to future climate variability and change. To illustrate this framework, we assessed how 779 farmers responded to delayed monsoon onset in fifteen villages in Gujarat, India during the 2011 growing season, when the monsoon onset was delayed by three weeks. We found that farmers adopted a variety of strategies to cope with delayed monsoon onset, including increasing irrigation use, switching to more drought-tolerant crops, and/or delaying sowing. We found that farmers’ access to and choice of strategies varied with their assets, irrigation access, perceptions of weather, and risk aversion. Richer farmers with more irrigation access used high levels of irrigation, and this strategy was associated with the highest yields in our survey sample. Poorer farmers with less secure access to irrigation were more likely to push back planting dates or switch crop type, and economic data suggest that these strategies were beneficial for those who did not have secure access to irrigation. Interestingly, after controlling for assets and irrigation access, we found that cognitive factors, such as beliefs that the monsoon onset date had changed over the last 20 years or risk aversion, were associated with increased adaptation. Our framework illustrates the importance of considering the complexity and heterogeneity of individual decision-making when conducting climate impact assessments or when developing policies to enhance the adaptive capacity of local communities to future climate variability and change.     

Using portfolio theory to guide reforestation and restoration under climate change scenarios

The general problem addressed by this study is that of designing a decision support system for planned adaptation to climate change that uses the principles of modern portfolio theory to minimise risk and maximise return of adaptive actions in an environment of deep uncertainty over future climate scenarios. Here we show how modern portfolio theory can use the results of a climate change impact model to select an optimal set of seed sources to be used in regenerating forests of white spruce in an environment of multiple, equally plausible future climates. This study shows that components of solutions are not selected to perform equally well across all plausible futures; but rather, that components are selected to specialise in particular climate scenarios. The innovation of this research rests in demonstrating that the powerful and widely used principles of quantifying and planning for risk and return in the uncertain environment of asset markets can be applied successfully to serve the objectives of planned adaptation to climate change.