Flight-to-quality and correlation between currency and stock returns

We document that capital flows in and out of emerging or developed markets are sensitive to global equity market conditions. Capital tends to move out of emerging into developed countries in global down markets, leading to depreciation (appreciation) of emerging (developed) currencies. This generates a positive (negative) correlation between currency and equity in emerging (developed) markets which is amplified by the magnitude of the capital movement. We also verify that hedging currency risks may undo the natural hedge and increase the total return volatility under negative correlation.     

‘Swift Rail’—funding local rail transit through smarter growth

Many medium-size cities suffer from severe traffic congestion and poor accessibility, limiting their potential to improve their economy, environment, and social equity. These problems could be addressed by creating high-quality transit linking such cities with suburban and other catchment areas through use of existing rail lines and abandoned former railway alignments. This concept, termed ‘Swift Rail’, would call for an innovative approach to local railway and transport development.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

How much do online consumers really value free product returns? Evidence from eBay

Consumer return rates have been steadily rising in recent years, resulting in growing costs for retailers who must manage the returns process and the disposition of returned products. This cost pressure is driven in part by extremely generous return policies, such as giving consumers a full refund upon return. Interestingly, this common retail practice of full refunds is inconsistent with the recommendations of many analytical models of returns, which nearly always show that a partial refund is optimal. Such inconsistencies between theory and practice might arise when the decision drivers included in the analytical models do not match the decision drivers in practice. It might also be the case that retailers are overly optimistic about the value that consumers assign to a full refund, and thus assume that the value of such a policy outweighs its costs. In this paper, we use data collected from eBay, where identical products are sold with different return policies, to investigate these open questions in the literature. We analyze both the return policy drivers from the retailer's perspective and the return policy value from the consumer's perspective. Our results suggest that the value of a full refund policy to consumers may not be as large as one might expect, and it also exhibits a large heterogeneity across buyers with different levels of online purchase experience. In addition, we provide empirical evidence for what has long been suspected by online retailers – that a non-refundable forward shipping charge quickly erodes any value that consumers assign to return policies. The generality of our results is limited by the fact that eBay differs from traditional retail contexts in many respects, including the fact that eBay buyers may not be representative of the general buyer population. However, our study of how eBay consumers value free returns provides new insights into an understudied area, and it can serve as a starting point for future studies of the value of return policies in other retail contexts.     

Housing Allowance and the Rent of Low‐income Households

This paper analyses the effect of a housing allowance programme on the rent paid by assisted low‐income households. The data consist of 12,000 rental households in the free‐market and municipal sectors. The effect is estimated using the programme reform of 2002 as an instrument. The reform increased the allowance of the assisted households depending on housing characteristics, but it had little effect on the eligibility. Results suggest that in the private sector, one additional euro of allowance increases the rent of claimants by 60–70 cents. The effect is zero in the municipal sector, as rents are tied to the housing costs.     

The Cross‐Section of Expected Stock Returns: What Have We Learnt from the Past Twenty‐Five Years of Research?

I review the recent literature on cross-sectional predictors of stock returns. Predictive variables used emanate from informal arguments, alternative tests of risk-return models, behavioural biases, and frictions. More than fifty variables have been used to predict returns. The overall picture, however, remains murky, because more needs to be done to consider the correlational structure amongst the variables, use a comprehensive set of controls, and discern whether the results survive simple variations in methodology .     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.     

Neoliberalization,Housing Institutions and Variegated Gentrification: How the ‘Third Wave’ Broke in Amsterdam

Neil Smith argues that in the last two decades gentrification has become a generalized global urban phenomenon. His theory is at a high level of abstraction, as it links urban gentrification to globalization, financial capitalism and neoliberalization. With these global processes, all cities have experienced ‘third wave’ gentrification. The theory, however, leaves little room for variegations of gentrification, apart from characterizing geographical differences as idiosyncrasies or as minor variations. This downplays the role of intervening mechanisms that impinge upon the pursuit and social outcomes of gentrification. This article aims to amend abstract theory by looking at Amsterdam from a historical institutional perspective. In Amsterdam, gentrification was particularly made possible by a process of neoliberalization within the housing system. However, as institutional change is incremental and based on layering and conversion, many older institutional arrangements remain in place. These arrangements tend to slow gentrification and assuage social consequences. Nevertheless, neoliberal modifications to the housing system have been accelerating the pursuit of gentrification. Current policies will very likely lead to exclusionary displacement.     

Comparing linear and nonlinear forecasts for stock returns

We compare the out-of-sample performance of monthly returns forecasts for two indices, namely the Dow Jones (DJ) and the Financial Times (FT) indices. A linear and a nonlinear artificial neural network (ANN) model are used to generate the out-of-sample competing forecasts for monthly returns. Stationary transformations of dividends and trading volume are considered as fundamental explanatory variables in the linear model and the input variables in the ANN model. The comparison of out-of-sample forecasts is done on the basis of forecast accuracy, using the Diebold and Mariano test [J. Bus. Econ. Stat. 13 (1995) 253.], and forecast encompassing, using the Clements and Hendry approach [J. Forecast. 5 (1998) 559.]. The results suggest that the out-of-sample ANN forecasts are significantly more accurate than linear forecasts of both indices. Furthermore, the ANN forecasts can explain the forecast errors of the linear model for both indices, while the linear model cannot explain the forecast errors of the ANN in either of the two indices. Overall, the results indicate that the inclusion of nonlinear terms in the relation between stock returns and fundamentals is important in out-of-sample forecasting. This conclusion is consistent with the view that the relation between stock returns and fundamentals is nonlinear.     

智能化住宅是建筑行业可持续发展的方向

文章论述了在知识经济时代，集高新技术为一体的智能化住宅建筑将成为可持续发展的人居环境的主流和趋势。