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

博士论文改进补助金：在生命周期中理解家庭投资组合事实和资产数量SSBE/SES 0820105：van Nieuwerburg这个项目使用瑞典家庭财富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。将投资行为与最先进的生命周期投资组合选择模型预测的投资行为进行了比较。其目的是提高对家庭投资行为驱动力的了解，以及对横截面和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做出了贡献。该项目利用了一个关于家庭财富投资组合的独特数据集。它既包含家庭的分类金融投资组合，也包括个人股票和共同基金的水平，以及有关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点进行评价。这一阶段是现有关于最优家庭投资组合选择研究的自然延续。为了提高具有风险劳动收入的生命周期投资组合选择模型的预测能力，已提出的特征包括：(I)与股市参与相关的成本；(Ii)偏好异质性；(Iii)灾难性经济冲击的实现；以及(Iv)住房作为投资机会来决定是否拥有或租赁。与其他包含家庭财富组合详细信息的数据集不同，每年都会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关于家庭一生中财务行为的事实，条件是实现对健康状况和劳动收入的冲击。此类应对措施的例子包括投资组合再平衡和自我保险。就福利而言，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。这些结果还会产生更广泛的影响，特别是对投资战略以及退休储蓄和社会保障政策的选择。该项目还有助于资产定价文献的两个方面。在最近的研究中，代理人之间的异质性结合不完全的金融市场被用来解释金融价格关系，如股权溢价的大小。获取高质量的微观数据有助于考虑这些问题并得出适当的模型校准。面板维度还有助于使用广义矩方法(GMM)估计资产定价模型(Euler方程)的文献。与家庭层面的不同金融组合相结合，给定家庭的欧拉方程产生了强大的识别限制。

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.