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Based on OECD evidence, equity/housing-price busts and credit crunches are followed by substantial increases in public consumption. These increases in unproductive public spending lead to increases in distortionary marginal taxes, a policy in sharp contrast with presumably optimal Keynesian fiscal stimulus after a crisis. Here we claim that this seemingly adverse policy selection is optimal under rational learning about the frequency of rare capital-value busts. Bayesian updating after a bust implies massive belief jumps toward pessimism, with investors and policymakers believing that busts will be arriving more frequently in the future. Lowering taxes would be as if trying to kick a sick horse in order to stand up and run, since pessimistic markets would be unwilling to invest enough under any temporarily generous tax regime.
We present empirical evidence on the heterogeneity in monetary policy transmission across countries with different home ownership rates. We use household-level data together with shocks to the policy rate identified from high-frequency data. We find that housing tenure reacts more strongly to unexpected changes in the policy rate in Germany and Switzerland –the OECD countries with the lowest home ownership rates– compared with existing evidence for the U.S. An unexpected decrease in the policy rate by 25 basis points increases the home ownership rate by 0.8 percentage points in Germany and by 0.6 percentage points in Switzerland. The response of non-housing consumption in Switzerland is less heterogeneous across renters and mortgagors, and has a different pattern across age groups than in the U.S. We discuss economic explanations for these findings and implications for monetary policy.
Asset transaction prices sampled at high frequency are much staler than one might expect in the sense that they frequently lack new updates showing zero returns. In this paper, we propose a theoretical framework for formalizing this phenomenon. It hinges on the existence of a latent continuous-time stochastic process pt valued in the open interval (0; 1), which represents at any point in time the probability of the occurrence of a zero return. Using a standard infill asymptotic design, we develop an inferential theory for nonparametrically testing, the null hypothesis that pt is constant over one day. Under the alternative, which encompasses a semimartingale model for pt, we develop non-parametric inferential theory for the probability of staleness that includes the estimation of various integrated functionals of pt and its quadratic variation. Using a large dataset of stocks, we provide empirical evidence that the null of the constant probability of staleness is fairly rejected. We then show that the variability of pt is mainly driven by transaction volume and is almost unaffected by bid-ask spread and realized volatility.
Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
A tale of one exchange and two order books : effects of fragmentation in the absence of competition
(2018)
Exchanges nowadays routinely operate multiple, almost identically structured limit order markets for the same security. We study the effects of such fragmentation on market performance using a dynamic model where agents trade strategically across two identically-organized limit order books. We show that fragmented markets, in equilibrium, offer higher welfare to intermediaries at the expense of investors with intrinsic trading motives, and lower liquidity than consolidated markets. Consistent with our theory, we document improvements in liquidity and lower profits for liquidity providers when Euronext, in 2009, consolidated its order ow for stocks traded across two country-specific and identically-organized order books into a single order book. Our results suggest that competition in market design, not fragmentation, drives previously documented improvements in market quality when new trading venues emerge; in the absence of such competition, market fragmentation is harmful.
This paper presents new evidence on the expectation formation process from a Dutch household survey. Households become too optimistic about their future income after their income has improved, consistent with the over-extrapolation of their experience. We show that this effect of experience is persistent and that households over-extrapolate income losses more than income gains. Furthermore, older households over-extrapolate more, suggesting that they did not learn over time to form more accurate expectations. Finally, we study the relationship between expectation errors and consumption. We find that more over-optimistic households intend to consume more and subsequently report higher consumption, even though they do not consume as much as they intended to. These results suggests that overextrapolation hurts consumers and amplify business cycles.
The propagation of regional shocks in housing markets: evidence from oil price shocks in Canada
(2018)
Shocks to the demand for housing that originate in one region may seem important only for that regional housing market. We provide evidence that such shocks can also affect housing markets in other regions. Our analysis focuses on the response of Canadian housing markets to oil price shocks. Oil price shocks constitute an important source of exogenous regional variation in income in Canada because oil production is highly geographically concentrated. We document that, at the national level, real oil price shocks account for 11% of the variability in real house price growth over time. At the regional level, we find that unexpected increases in the real price of oil raise housing demand and real house prices not only in oil-producing regions, but also in other regions. We develop a theoretical model of the propagation of real oil price shocks across regions that helps understand this finding. The model differentiates between oil-producing and non-oil-producing regions and incorporates multiple sectors, trade between provinces, government redistribution, and consumer spending on fuel. We empirically confirm the model prediction that oil price shocks are propagated to housing markets in non-oil-producing regions by the government redistribution of oil revenue and by increased interprovincial trade.
We analytically characterize optimal monetary policy for an augmented New Keynesian model with a housing sector. In a setting where the private sector has rational expectations about future housing prices and inflation, optimal monetary policy can be characterized without making reference to housing price developments: commitment to a 'target criterion' that refers to inflation and the output gap only is optimal, as in the standard model without a housing sector. When the policymaker is concerned with potential departures of private sector expectations from rational ones and seeks to choose a policy that is robust against such possible departures, then the optimal target criterion must also depend on housing prices. In the empirically realistic case where housing is subsidized and where monopoly power causes output to fall short of its optimal level, the robustly optimal target criterion requires the central bank to 'lean against' housing prices: following unexpected housing price increases, policy should adopt a stance that is projected to undershoot its normal targets for inflation and the output gap, and similarly aim to overshoot those targets in the case of unexpected declines in housing prices. The robustly optimal target criterion does not require that policy distinguish between 'fundamental' and 'non-fundamental' movements in housing prices.
We establish that the labor market helps discipline asset managers via the impact of fund liquidations on their careers. Using hand-collected data on 1,948 professionals, we find that top managers working for funds liquidated after persistently poor relative performance suffer demotion coupled with a significant loss in imputed compensation. Scarring effects are absent when liquidations are preceded by normal relative performance or involve mid-level employees. Seen through the lens of a model with moral hazard and adverse selection, these results can be ascribed to reputation loss rather than bad luck. The findings suggest that performance-induced liquidations supplement compensation-based incentives.
In talent-intensive jobs, workers’ quality is revealed by their performance. This enhances productivity and earnings, but also increases layoff risk. Firms cannot insure workers against this risk if they compete fiercely for talent. In this case, the more risk-averse workers will choose less quality-revealing jobs. This lowers expected productivity and salaries. Public unemployment insurance corrects this inefficiency, enhancing employment in talent-sensitive industries, consistently with international evidence. Unemployment insurance dominates legal restrictions on firms’ dismissals, which penalize more talent-sensitive firms and thus depress expected productivity. Finally, unemployment insurance fosters education, by encouraging investment in risky human capital that enhances talent discovery.