The Returns on Human Capital: Good News on Wall Street is Bad News on Main Street

We use a standard single-agent model to conduct a simple consumption growth accounting exercise. Consumption growth is driven by news about current and expected future returns on the market portfolio. The market portfolio includes financial and human wealth. We impute the residual of consumption growth innovations that cannot be attributed to either news about financial asset returns or future labor income growth to news about expected future returns on human wealth, and we back out the implied human wealth and market return process. This accounting procedure only depends on the agent's willingness to substitute consumption over time, not her consumption risk preferences. We find that innovations in current and future human wealth returns are negatively correlated with innovations in current and future financial asset returns, regardless of the elasticity of intertemporal substitution. The evidence from the cross-section of stock returns suggests that the market return we back out of aggregate consumption innovations is a better measure of market risk than the return on the stock market.

Monetary Policy in an Equilibrium Portfolio Balance Model

Portfolio balance, sterilized foreign exchange intervention

Effect of multiple allelic drop-outs in forensic RMNE calculations

Technological advances such as massively parallel sequencing enable increasing amounts of genetic information to be obtained from increasingly challenging samples. Certainly on low template, degraded and multi-contributor samples, drop-outs will increase in number for many profiles simply by analyzing more loci, making it difficult to probabilistically assess how many drop-outs have occurred and at which loci they might have occurred. Previously we developed a Random Man Not Excluded (RMNE) method that can take into account allelic drop-out while avoiding detailed estimations of the probability that drop-outs have occurred, nor making assumptions about at which loci these drop-outs might have occurred. The number of alleles that have dropped out, does not need to be exactly known. Here we report a generic Python algorithm to calculate the RMNE probabilities for any given number of loci. The number of allowed drop-outs can be set between 0 and twice the number of analyzed loci. The source code has been made available on https://github.com/fvnieuwe/rmne. An online web-based RMNE calculation tool has been made available on http://forensic.ugent.be/rmne. The tool can calculate these RMNE probabilities from a custom list of probabilities of the observed and non-observed alleles from any given number of loci. Using this tool, we explored the effect of allowing allelic drop-outs on the evidential value of random forensic profiles with a varying number of loci. Our results give insight into how the number of allowed drop-outs affects the evidential value of a profile and how drop-out can be managed in the RMNE approach

Housing Collateral, Consumption Insurance and Risk Premia: An Empirical Perpective

In a model with housing collateral, the ratio of housing wealth to human wealth shifts the conditional distribution of asset prices and consumption growth. A decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. Using aggregate data for the US, we find that a decrease in the ratio of housing wealth to human wealth predicts higher returns on stocks. Conditional on this ratio, the covariance of returns with aggregate risk factors explains eighty percent of the cross-sectional variation in annual size and book-to-market portfolio returns. A data appendix for this paper is available .

Expertise in digitalisering: met vallen en opstaan

Een overzicht van de opgebouwde expertise van de Universiteitsbibliotheek Gent inzake digitalisering en aanbieden van gedigitaliseerde content

Information Immobility and the Home Bias Puzzle

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why doesn't global information access eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio out-performance and asset prices. Finally, we propose new avenues for empirical research.

A Theory of Housing Collateral, Consumption Insurance and Risk Premia

In a model with housing collateral, a decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. This collateral mechanism can quantitatively replicate the conditional and the cross-sectional variation in risk premia on stocks for reasonable parameter values. The increase of the conditional equity premium and Sharpe ratio when collateral is scarce in the model matches the increase observed in US data. The model also generates a return spread of value firms over growth firms of the magnitude observed in the data, because the term structure of consumption strip risk premia is downward sloping.