Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.     

金融资产配置与违约风险:蓄水池效应,还是逐利效应？

本文以2007—2016年中国A股上市公司为研究对象,检验了企业金融资产配置对违约风险的影响。实证研究发现:金融资产持有量越多,企业的违约风险越低,金融资产配置的“蓄水池效应”显著;在货币政策宽松时期,金融资产配置导致的代理冲突显现,宽松的货币政策会抑制金融资产投资对违约风险的降低作用。政府规制也会有一定的公司治理作用,将产业政策纳入讨论发现:对于产业政策支持的行业来说,企业金融资产配置能够降低违约风险,但是宽松的货币政策会刺激管理层的短视投资行为,抑制政府规制的公司治理作用。进一步地,本文提出会计稳健性的提升是企业金融资产配置降低违约风险的重要路径。本文的研究结论丰富了企业金融资产配置动机和违约风险影响因素的讨论,能够为政府部门防范经济运行中的内在风险提供有益借鉴。     

The effects of credit default swaps on corporate investment

ABSTRACT

This paper examines how credit default swaps (CDS) affect the corporate investment of the referenced entities. We document a significant reduction in corporate investment after CDS trading, a result that is robust to alternative model specifications and a set of endogeneity tests. Our findings of the increased firm risk and cost of capital support the costly external capital channel. The cross-sectional variations in CDS effects demonstrate that both reduced monitoring and the empty creditor problem might be the underlying forces driving the costly external capital channel. Our additional analysis implies that CDS trading is associated with an enhancement in investment efficiency for firms that are prone to overinvestment.     

经济开放与货币需求:国际金融风险及持币成本的测度

在中国开放经济体制下的基准货币需求模型中,本文将源于国际金融市场的持币成本设为遗漏潜变量,并构建特定的国际金融综合指数(CIFI)作为该潜变量的测度。借鉴机器学习与测度理论,本文利用对数误差修正模型提出了分步降维的CIFI构造算法,构造了长期CIFI和短期CIFI。结果表明,CIFI构造中的无监督降维步骤有助于减少高维金融数据中的冗余信息。实证分析发现,国际机会成本对中国货币需求具有规律性的前导影响,而在2007至2008年国际金融危机期间,央行的应急措施对长期CIFI所代表的非均衡冲击起到明显的阻截效果,对短期CIFI的影响基本是持续不变的。通过综合指数构造与宏观货币需求模型的算法连接,可以利用CIFI的构成结构从前导时间与影响强度两方面追踪冲击货币需求的国际金融风险的具体来源,这为宏观决策者监测国际金融市场提供了颇有规律的信息。在方法论上,本研究为如何利用模型监测国际金融市场影响宏观经济开辟了一条新路。     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach

Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a state-of-the-art machine learning method (i.e., extreme gradient boosting) to explore the non-linear associations between built environment and active travel for working and shopping. Our findings are as follows. (1) For both purposes, trip characteristics contribute the greatest, and the built environment is also quite important and has larger collective contributions for active travel than does socioeconomics. (2) The relative importance of built environment on active travel for shopping is evidently larger than that for working. (3) All built-environment variables have non-linear associations with active travel, and associations with active travel for working are generally in inverted U or V shapes, while those with shopping trips have much more complex patterns. (4) Differences in the threshold value and gradient exist between built-environment associations with active travel for working and shopping and between variables at origins and destinations. Decision makers are recommended to meticulously disentangle the complex influences of built environment on active travel and distinguish between diverse purposes to make informed and targeted interventions.     

Industry risk transmission channels and the spillover effects of specific determinants in China’s stock market: A spatial econometrics approach

This paper uses a spatial econometrics approach to study the industry risks in China’s stock market. We comprehensively consider the real linkage and information risk transmission channels and analyze the risk spillovers of specific determinants. Our empirical results show the following: 1) The real linkage channel and information channel are both effective transmission channels for driving spillover effects, and the information channel is of the utmost importance. 2) The spillover effects of specific determinants exist and are persistent. The superposition of spillover effects may lead to extreme risk. 3) The transmission channels and spillover effects are asymmetric in different regimes.     

Multi-city exploration of built environment and transit mode use: Comparison of Melbourne,Amsterdam and Boston

The built environment is an important determinant of travel demand and mode choice. Establishing the relationship between the built environment and transit use using direct models can help planners predict the impact of neighborhood-level changes, that are otherwise overlooked. However, limited research has compared the impacts of the built environment for different networks and for individual transit modes.This paper addresses this gap by developing built environment and transit use models for three multimodal networks, Amsterdam, Boston and Melbourne, using a consistent methodology. A sample of train, tram and bus sites with similar station-area built environments are selected and tested to establish if impacts differ by mode. It is the first study that develops neighborhood-level indicators for multiple locations using a consistent approach.This study compares results for ordinary least squares regression and two-stage least squares (2SLS) regression to examine the impact of transit supply endogeneity on results. Instrumented values are derived for bus and tram frequency in Melbourne and bus frequency in Boston. For other mode and city combinations, the 2SLS approach is less effective at removing endogeneity.Results confirm that different associations exist between the built environment and transit modes, after accounting for mode location bias, and that this is true in multiple networks. Local access and pedestrian connectivity are more important for bus use than other modes. Tram is related to commercial density. This finding is consistent for all samples. Land use mix and bicycle connectivity also tend to be associated with higher tram use. Train use is highest where opportunities exist to transfer with bus. Population density is commonly linked to ridership, but its significance varies by mode and network.More research is needed to understand the behavioral factors driving modal differences to help planners target interventions that result in optimal integration of land use with transit modes.     

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection

This paper develops a novel time-varying multivariate Copula-MIDAS-GARCH (TVM-Copula-MIDAS-GARCH) model with exogenous explanatory variables to model the joint distribution of returns. The model accounts for mixed frequency factors that affect the time-varying dependence structure of financial assets. Furthermore, we examine the effectiveness of the proposed model in VaR-based portfolio selection. We conduct an empirical analysis on estimating the 90%, 95%, 99% VaRs of the portfolio constituted of the Shanghai Composite Index, Shanghai SE Fund Index, and Shanghai SE Treasury Bond Index. The empirical results show that the proposed TVM-Copula-MIDAS-GARCH model is effective to investigate the nonlinear time-varying dependence among those three indices and performs better in portfolio selection.