Doctoral Dissertation Improvement Grant:Understanding Household Portfolio Facts and Asset Quantities over the Life Cycle

博士论文改进补助金：了解整个生命周期的家庭投资组合事实和资产数量

• 批准号: 0820105
• 负责人: Stijn Van Nieuwerburgh
• 金额: --
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0820105&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Improvement; Grant; Understanding

Doctoral Dissertation Improvement Grant: Understanding Household Portfolio Facts and Asset Quantities over the Life Cycle SSBE/SES 0820105: van NieuwerburghThis project uses a panel data set of Swedish households' wealth portfolios and socio-demographic variables to document households' investment behavior. The investment behavior is compared to that predicted with state-of-the-art life-cycle portfolio choice models. The purpose is to improve understanding of the driving forces in households' investment behavior and of the different investment styles in the cross-section, and over the life cycle. The project also contributes to the asset pricing literature.The project utilizes a unique dataset on households' wealth portfolios. It contains both households' disaggregated financial portfolios, down to the level of individual stocks and mutual funds, and information about real estate ownership. The first stage of the project consists of an evaluation of different features of life-cycle portfolio models. This stage is a natural continuation of existing research on optimal household portfolio choice. Among the features that have been proposed to improve the predictive ability of the class of life-cycle portfolio choice models with risky labor income are: (i) costs associated with stock-market participation; (ii) preference heterogeneity; (iii) the realization of disastrous economic shocks; and (iv) housing as an investment opportunity in the decision to own or rent.The data period is 1999-2006. Unlike other datasets with detailed information on households' wealth portfolios, the same households are sampled each year. Due to the panel dimension of the data, research in a second stage of the project is able to document facts about households' financial behavior over their life-time, conditional on the realization of shocks to health status and labor income. Examples of such responses are portfolio rebalancing and self-insurance. For welfare purposes, the ability (propensity) to consume out of real-estate wealth or other forms of wealth is of importance for non-elderly households that suffer major economic shocks and for the elderly. There are broader impacts of the results particularly for investment strategy and for the choice of policies for retirement savings and social security.The project also contributes to two strands of the asset-pricing literature. In recent research, heterogeneity across agents in combination with incomplete financial markets have been used to explain financial price relations such as the size of the equity premium. Access to high-quality micro data facilitates taking account of such matters and deriving appropriate model calibrations. The panel dimension also facilitates contributions to the literature on the estimation of asset pricing models (Euler equations) using Generalized Method of Moments (GMM). Combined with heterogeneous financial portfolios at the household level, the Euler equations for a given household produce powerful identifying restrictions.

博士论文改进补助金：了解生命周期中的家庭投资组合事实和资产配置SSBE/SES 0820105：货车Nieuwerburg该项目使用瑞典家庭财富投资组合和社会人口变量的面板数据集来记录家庭的投资行为。的投资行为进行比较，预测与国家的最先进的生命周期投资组合选择模型。其目的是增进对家庭投资行为的驱动力以及对各部门和整个生命周期中不同投资风格的理解。该项目还对资产定价文献作出了贡献，利用了关于家庭财富组合的独特数据集。它包含两个家庭的分类金融投资组合（下至个股和共同基金的水平）以及有关真实的房地产所有权的信息。该项目的第一阶段包括对生命周期投资组合模型的不同特征进行评价。这一阶段是现有的最优家庭投资组合选择研究的自然延续。其中已提出的功能，以提高预测能力的生命周期投资组合选择模型类与风险劳动收入是：（一）与股票市场参与的成本;（二）偏好异质性;（三）实现灾难性的经济冲击;（四）住房作为一个投资机会，在决定拥有或租用。与其他提供家庭财富组合详细信息的数据集不同，每年都对相同的家庭进行抽样。由于数据的面板维度，该项目第二阶段的研究能够记录家庭一生中的金融行为事实，条件是实现健康状况和劳动收入的冲击。这类对策的例子包括重新平衡投资组合和自我保险。从福利的角度来看，从房地产财富或其他形式的财富中消费的能力（倾向）对于遭受重大经济冲击的非老年家庭和老年人来说非常重要。有更广泛的影响的结果，特别是对投资策略和退休储蓄和社会保障的政策选择。该项目还有助于两股资产定价文献。在最近的研究中，异质性与不完全金融市场相结合的代理人已被用来解释金融价格关系，如股票溢价的大小。获得高质量的微观数据有助于考虑到这些问题，并得出适当的模型校准。面板尺寸也有利于文献的资产定价模型（欧拉方程）的估计使用广义矩法（GMM）的贡献。结合家庭层面的异质金融投资组合，给定家庭的欧拉方程产生了强大的识别限制。