Borrowing Constraints, Portfolio Choice, and Precautionary Motives: Theoretical Predictions and Empirical Complications

This paper studies effects of two classes of borrowing constraints, collateral- and income-based, on wealth accumulation, portfolio behavior and on precautionary motives. We examine the sensitivity of solutions to tightness of constraints, education level, and preference parameters. The models are calibrated using the 1992 Survey of Consumer Finances. The idea that constrained households engage in less borrowing and less holding of risky assets than desired is borne out for income-based constraints but not necessarily for constraints where assets also serve as collateral. The commonly used nonnegativity constraint on wealth turns out to be a very special case among collateral constraints: not only is constrained consumption equal to income but precautionary wealth holding is zero. Income-based constraints reverse the sign of precautionary effects on holdings of risky assets, and so do relatively tight collateral constraints. The latter reverse the sign of precautionary effects on borrowing, as well. Precautionary effects on wealth holding and on borrowing are smaller when income-based constraints are binding, though not necessarily so for collateral constraints. Results suggest that inclusion of constrained households in a sample of unconstrained ones is quite likely when using standard wealth-level cutoffs for sample splitting, and that it tends to bias empirically observed precautionary effects on wealth downwards. Estimated precautionary effects on risky assets and on borrowing may even be biased towards zero. These findings may help explain the failure of recent empirical studies to uncover sizeable precautionary effects on wealth and on portfolio composition.Precautionary saving, borrowing constraints, household portfolios

Non-expected Utility, Saving, and Portfolios

Existing findings suggest that standard, frictionless, expected-utility models have difficulty accounting for average and for median holdings of wealth and of risky assets, partly as a result of the largely unexplained limited proportion of stockholders among households. We analyze life-cycle wealth accumulation and portfolio choice under career uncertainty and quantifiable departures from expected utility maximization. Our specification nests expected utility and three types of non-expected utility: (i) Kreps-Porteus preferences that disentangle risk aversion from elasticity of substitution, (ii) Yaari's Dual Theory of Choice, and (iii) Quiggin's Rank-dependent Utility. Specifications (ii) and (iii) exhibit "first-order" risk aversion and kinked indifference curves. Solution of such models under multiple sources of risk presents conceptual and computational difficulties. We introduce a notion of equilibrium and a computational algorithm appropriate for such setups. Computed wealth and stockholding, based on calibrated income processes for three education categories, are compared to the 1992 Survey of Consumer Finances. Rank-dependent utility enhances the importance of precautionary effects. Contrary to priors in the literature, solutions are not typically at kinks; neither kinks nor actual solutions involve zero stockholding when income risk is recognized; and yet predictions about average wealth and risky assets tend to improve for all education categories. Mere disentangling of risk aversion from elasticity has small effects, while dual theory predictions are farther from the data and the signs of precautionary effects are reversed.precautionary motives, non-expected utility, first-order risk aversion, portfolio choice, saving

Borrowing Constraints, Portfolio Choice, and Precautionary

This paper studies effects of two classes of borrowing constraints, collateral- and income-based, on wealth accumulation, portfolio behavior and on precautionary motives. We examine the sensitivity of solutions to tightness of constraints, education level, and preference parameters. The models are calibrated using the 1992 Survey of Consumer Finances. The idea that constrained households engage in less borrowing and less holding of risky assets than desired is borne out for income-based constraints but not necessarily for constraints where assets also serve as collateral. The commonly used nonnegativity constraint on wealth turns out to be a very special case among collateral constraints: not only is constrained consumption equal to income but precautionary wealth holding is zero. Income-based constraints reverse the sign of precautionary effects on holdings of risky assets, and so do relatively tight collateral constraints. The latter reverse the sign of precautionary effects on borrowing, as well. Precautionary effects on wealth holding and on borrowing are smaller when income-based constraints are binding, though not necessarily so for collateral constraints. Results suggest that inclusion of constrained households in a sample of unconstrained ones is quite likely when using standard wealth-level cutoffs for sample splitting, and that it tends to bias empirically observed precautionary effects on wealth downwards. Estimated precautionary effects on risky assets and on borrowing may even be biased towards zero. These findings may help explain the failure of recent empirical studies to uncover sizeable precautionary effects on wealth and on portfolio composition.Precautionary saving, borrowing constraints, household portfolios

Over bedrijfskunde en andere kundes

Een bedrijfskundige is iemand die organisatieproblemen (inclusief de door hen geproduceerde ‘negatieve externaliteiten’) analyseert met behulp van wetenschappelijke inzichten. Een bedrijfskundige combineert een praktische probleemoriëntatie met een brede wetenschappelijke belangstelling

Het ziekenhuis als ontwerpvraagstuk:Marktgerichte structuren als voorwaarde voor interdisciplinaire, 'team-based' zorg

Ziekenhuizen behoren tot wat Mintzberg professionele bureaucratieën noemt. Ze worden geconfronteerd met problemen op het gebied van kwaliteit, patiëntvriendelijkheid, veiligheid en efficiency. Een belangrijke vraag is of ze voor het bereiken van deze doelen de juiste structuur bezitten. In zijn boek Structure in fives (1983) presenteert Mintzberg een ontwerptheorie waarmee deze vraag beantwoord zou kunnen worden. Structuurparameters spelen hierin een belangrijke rol. In het samen met Glouberman geschreven artikel (2001) past hij zijn ontwerptheorie toe op het ziekenhuis. In dat artikel wordt de vraag beantwoord: aan welke knoppen moet de ontwerper draaien om de problemen van ziekenhuizen op te lossen? In deze bijdrage vergelijk ik de opvattingen van Mintzberg met recente opvattingen over een patiëntgerichte en procesgestuurde organisatie van het ziekenhuis. Ik kom tot de conclusie dat wat hierin voorgesteld wordt, overeenkomt met 1) wat volgens Minztberg niet kan (het kantelen van de ziekenhuisorganisatie van een functionele naar een marktgerichte structuur) en 2) wat volgens Minztberg niet mag (het standaardiseren van output en processen). Dat is praktisch belangrijk, immers, volgens Mintzberg krijgen we nooit de zo gewenste multidisciplinaire, patiëntgerichte en proces- gestuurde ziekenhuisorganisatie. In deze bijdrage beperk ik mij nadrukkelijk tot het ziekenhuis als vraagstuk van structuurontwerp. Ik kan, binnen dit bestek, niet ingaan veranderingskundige vraagstukken op meso- en met vraagstukken van institutioneel herontwerp op macroniveau. Deze bijdrage is onderdeel van een vergelijkend onderzoek van de sterke en zwakke punten van verschillende organisatieontwerpbenaderingen (zie Achterbergh, Vriens, 2009; Christis, 2005, 2009 en 2011)

Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models

This paper considers the specification of covariance structures with tail estimates. We focus on two aspects: (i) the estimation of the VaR-CoVaR risk matrix in the case of larger number of time series observations than assets in a portfolio using quantile predictive regression models without assuming the presence of nonstationary regressors and; (ii) the construction of a novel variable selection algorithm, so-called, Feature Ordering by Centrality Exclusion (FOCE), which is based on an assumption-lean regression framework, has no tuning parameters and is proved to be consistent under general sparsity assumptions. We illustrate the usefulness of our proposed methodology with numerical studies of real and simulated datasets when modelling systemic risk in a network

Limit Theory under Network Dependence and Nonstationarity

These lecture notes represent supplementary material for a short course on time series econometrics and network econometrics. We give emphasis on limit theory for time series regression models as well as the use of the local-to-unity parametrization when modeling time series nonstationarity. Moreover, we present various non-asymptotic theory results for moderate deviation principles when considering the eigenvalues of covariance matrices as well as asymptotics for unit root moderate deviations in nonstationary autoregressive processes. Although not all applications from the literature are covered we also discuss some open problems in the time series and network econometrics literature.Comment: arXiv admin note: text overlap with arXiv:1705.08413 by other author

Asymptotic Theory for Moderate Deviations from the Unit Boundary in Quantile Autoregressive Time Series

We establish the asymptotic theory in quantile autoregression when the model parameter is specified with respect to moderate deviations from the unit boundary of the form (1 + c / k) with a convergence sequence that diverges at a rate slower than the sample size n. Then, extending the framework proposed by Phillips and Magdalinos (2007), we consider the limit theory for the near-stationary and the near-explosive cases when the model is estimated with a conditional quantile specification function and model parameters are quantile-dependent. Additionally, a Bahadur-type representation and limiting distributions based on the M-estimators of the model parameters are derived. Specifically, we show that the serial correlation coefficient converges in distribution to a ratio of two independent random variables. Monte Carlo simulations illustrate the finite-sample performance of the estimation procedure under investigation