Debt Overhang and Deleveraging in the US Household Sector: Gauging the Impact on Consumption

Using a novel data set for the U.S. states, this paper examines whether household debt and the protracted debt deleveraging help explain the dismal performance of U.S. consumption since 2007 in the aftermath of the housing bubble. By separating the concepts of deleveraging and debt overhang—a flow and a stock effect—we find that excessive indebtedness exerted a meaningful drag on consumption over and beyond wealth and income effects. The overall effect, however, is modest—‐around one sixth of the slowdown in consumption between 2000–06 and 2007–12—and mostly driven by states with particularly large imbalances in their household sector. This might be indicative of non‐linearities, whereby indebtedness begins to bite only when misalignments from sustainable debt dynamics become excessive.     

Hedonic pricing and the spatial structure of housing data – an application to Berlin

Housing prices are largely determined by physical location. By applying the outsample prediction accuracy of rental prices as evaluation criteria, we examine whether the choice of the hedonic model additionally depends on the spatial structure of housing data, i.e. accounting for locational effects by either district fixed effects or spatial econometric modelling. Our results show that a generalised spatio-temporal model outperforms a district fixed effects model only if the spatial density – the weighted mean distance to nearest neighbours – is relatively small. Moreover, we use the required density thresholds to deduce a pseudo indifference curve, thereby showing that the ratio of the weighted spatial distance-to-the mean district diameter increases with the mean sample size per district. This emphasises the role of data structure and district choices for model selection. Differences in data can thereby serve as an explanation for contradictory findings in literature, whether spatial econometric methods or simple district fixed effects are used.     

Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications

Recent methodological developments provide a way to incorporate the temporal dimension when accounting for spatial effects in hedonic pricing. Weight matrices should decompose the spatial effects into two distinct components: bidirectional contemporaneous spatial connections; and unidirectional spatio-temporal effects from past transactions. Our iterative estimation approach explicitly analyses the role of time in price determination. The results show that both spatio-temporal components should be included in model specification; past transaction information stops contributing to price determination after eight months; and limited temporal friction is exhibited within this period. These findings highlight the decidedly non-linear temporal patterns of such information effects.     

抵押物处置与农房抵押贷款困境：基于演化博弈的分析

研究目的:分析农民住房财产权抵押(简称农房抵押)贷款困境的主要原因以及抵押物处置对农房抵押贷款行为的影响机理。研究方法:演化博弈论。研究结果:农房抵押贷款发展面临困境的关键在于金融机构惜贷,而抵押物处置难是激发金融机构惜贷的重要原因,但是降低抵押物处置难度只是推动农房抵押贷款发展的必要而非充分条件。研究结论:要促进农房抵押贷款发展,除了必须降低抵押物处置难度外,还需要构建完善的农房抵押贷款制度体系。     

Forecasting Bordeaux wine prices using state-space methods

Generic Bordeaux red wine (basic claret) can be regarded as being similar to an agricultural commodity. Production volumes are substantial, they are traded at high frequency and the quality of the product is relatively homogeneous. Unlike other commodities and the top-end wines (which represent only 3% of the traded volume), there is no futures market for generic Bordeaux wine. Reliable forecasts of prices can to large extent replace this information deficiency and improve the functioning of the market. We use state-space methods with monthly data to obtain a univariate forecasting model for the average price. The estimates highlight the stochastic trend and the seasonality present in the evolution of the price over the period 1999 to 2016. The model predicts the path of wine prices out of sample reasonably well, suggesting that this approach is useful for making reasonably accurate forecasts of future price movements.     

Assigning Refugees to Landlords in Sweden: Efficient,Stable, and Maximum Matchings*

In this paper, we investigate the problem of finding housing for refugees once they have been granted asylum. In particular, we demonstrate that market design can play an important role in a partial solution to the problem. More specifically, we investigate a specific matching system, and we propose an easy-to-implement mechanism that finds an efficient, stable, and maximum matching. Such a matching guarantees that housing is efficiently provided to a maximum number of refugees, and that no refugee prefers another specific landlord to their current match when, at the same time, that specific landlord prefers that refugee to their own current match.     

Stock market dependence in crisis periods: Evidence from oil price shocks and the Qatar blockade

This paper examines the correlation and the dependence patterns of the Qatar stock market with other markets using copula statistical theory and exploiting new datasets covering the period August 1998 to June 2018. To examine the crisis –specific change in the average degree of dependence we decomposed the data into the time periods before and after oil price shocks and the 2017 political crisis among the Gulf Cooperation Council members (i.e. the Qatari blockade). Our findings from the static copula modelling show that the correlations between the Qatari and the other stock markets significantly change after the oil price and the blockade crisis as well. The degree of change in the correlation is time varying and differs from county-group to another. Moreover, our findings reveals that the 2008 global financial crisis has a stronger impact than the price shocks and political crisis. The findings of the paper are of interest and allow for formulating a reliable and dynamic portfolio design framework for investors and risk managers.     

Oil price and Gulf Corporation Council stock indices: New evidence from time-varying copula models

Using a time-varying GJR copula approach, we determine the conditional dependence of the GCC stock indices on oil price between 2007 and 2016. We show how to improve the forecasting accuracy of the co-movement of energy and stock prices in an equally weighted portfolio. Contrary to prior findings, we demonstrate that due to the different co-movements across the GCC stock indices, portfolios of oil assets and several GCC stocks are less likely to be affected by systemic risk. The different co-movements across several stock indices over time provide different entry and exit points for stock investors. This approach is in line with the ‘buy low/sell high’ adage.     

Estimating the duration of different seasons and their impact on hotel room prices

The purpose of the study is to accurately measure the span of different seasons and its effect on the fluctuation of the occupancy rate and the average room rates in the hospitality sector. While prior studies have concentrated on measuring seasonality using calendar months, this study takes a different approach by measuring seasons in terms of the date count so that the exact starting and ending date of a season can be identified. This step involves adopting a non-parametric methodology that split the time scale into several small parts to obtain a better fit of the relationship and that can detect the starting and end of the season when given specific dates.     

Permit markets,carbon prices and the creation of innovation clusters

Innovation clusters combining public and private effort to develop breakthrough technologies promise greater technological advances to slow down climate change. We use a multi-country model with an emission trading system to examine whether and how international climate policy can incentivize countries to create such innovation clusters. We find that a minimal carbon price is needed to attract applied research firms, but countries may nevertheless fail to invest in complementary research infrastructure. We construct a mechanism that leads to innovation clusters when emissions targets are set before uncertainty surrounding technological developments is resolved. It is a combination of low permit endowments for the country with the lowest costs to build the needed infrastructure, compensation for this country by profits from permit trade, and maximal possible permit endowments for the remaining countries. We outline how the EU-ETS can be further refined according to this mechanism.