Japan's financial crises and lost decades

In this paper, we explore the role of financial intermediation malfunction in macroeconomic fluctuations in Japan. To this end we estimate, using Japanese data, a financial accelerator model in which the balance sheet conditions of entrepreneurs in a goods-producing sector and those of a financial intermediary affect macroeconomic activity. We find that shocks to the balance sheets of the two sectors have been quantitatively playing important role in macroeconomic fluctuations by affecting lending rates and aggregate investments. Their impacts are prominent in particular during financial crises. Shocks to the entrepreneurs’ balance sheets have played a key role in lowering investment in the bubble burst during the early 1990s and in the global financial crisis during the late 2000s. Shocks to the financial intermediaries’ balance sheets have persistently lowered investment throughout the 1990s.     

The effects of haze pollution on stock performances: evidence from China

Haze pollution has become the most important environmental issue in China in recent years. Using the data of PM_2.5 concentration and stocks of listed companies located in Beijing between 2010 and 2014, this article investigates the effects of haze pollution on stock performances. Empirical results indicate that haze pollution has significant negative effects on stock returns and significant positive effects on stock volatilities, through the channel of investors’ mood. Furthermore, the effects of haze pollution on stock returns emerge gradually and the effects of haze pollution on stock volatilities weaken gradually over time during a trading day.     

Using partial identification methods to estimate the effect of violence against women on their children’s health outcomes

A growing literature in economics seeks to estimate the costs of violence against women by examining, for example, its impact on the health outcomes of their children. However, it is difficult to assign a causal interpretation to these nonexperimental studies due to the presence of unobservable characteristics affecting violence and health outcomes simultaneously. The lack of credible instrumental variables applicable in several countries further limits our knowledge. I address this gap by using new partial identification methods to estimate the relative size of the unobservables needed to eliminate the estimated effects in nonexperimental studies. I also expand the external validity of the analysis by using data from five standardized nationally representative household surveys in Latin America. Consistent with previous studies, cross-sectional estimates show large negative associations between violence against women and an array of child health outcomes. However, when accounting for omitted variable bias, at best, two-thirds of the estimates remain robust and they are concentrated on the outcomes with the largest cross-sectional estimated impacts.     

Multi-party computation mechanism for anonymous equity block trading: A secure implementation of turquoise plato uncross

Dark pools are financial trading venues where orders are entered and matched in secret so that no order information is leaked. By preventing information leakage, dark pools offer the opportunity for large volume block traders to avoid the costly effects of market impact. However, dark pool operators have been known to abuse their privileged access to order information. To address this issue, we introduce a provably secure multi-party computation mechanism that prevents an operator from accessing and misusing order information. Specifically, we implement a secure emulation of Turquoise Plato Uncross, Europe's largest dark pool trading mechanism, and demonstrate that it can handle real world trading throughput, with guaranteed information integrity.     

包西高速铁路引入鄂尔多斯地区方案研究

包西高速铁路作为我国"八纵八横"高速铁路通道中重要的纵向通道之一,建成后呼包鄂城市群和关中平原城市群将实现有效连接。鄂尔多斯市为典型的"一城双核"城市,包西高速铁路的引入将对地区客运布局产生较大影响,根据鄂尔多斯地区城市及铁路现状、城市总体规划及交通规划,结合地区客运量及特点,提出地区内包西高速铁路引入方案及车站设置方案,确定合理的引入及客站布点,研究地区内高速铁路客站布局,结合地区客流特点,确定高速铁路车站分工;合理确定引入地区线、站位方案将有助于完善鄂尔多斯铁路枢纽的客运铁路布局,有助于完善鄂尔多斯地区高速铁路网络,提升呼包鄂城市群的辐射效应,促进城市建设和经济发展。     

Preference aggregation and atoms in measures

This paper examines the aggregation of preferences with a finitely additive measure space of agents. We consider three types of non-dictatorship axioms: non-dictatorship, coalitional non-dictatorship, and atomic non-dictatorship. First, we show that the existence of an atom is a necessary and sufficient condition for the existence of a social welfare function that satisfies weak Pareto, independence of irrelevant alternatives, and coalitional non-dictatorship. Second, we simultaneously impose non-dictatorship and coalitional non-dictatorship, and specify a necessary and sufficient condition for the finitely additive measure that guarantees the compatibility among the axioms. Third, we impose all non-dictatorship axioms and show that the corresponding measure is extremely restricted.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.