Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

The signaling effects of education in the online lending market: Evidence from China

In this study, we use data from an online lending platform named Xinxindai in China to empirically study the signaling effects of education for the default risk of borrowers. Three dependent variables are created, namely, the probability of default, overdue payments and overdue amount, and probit models, count models and Tobit models are employed correspondingly. The number of universities in the “211 Project” of China at the city level is employed as the instrumental variable. The empirical evidence shows that education generally plays a strong signaling role in the identification of borrowers’ default risk in China. The negative marginal effect of education declines as borrowing times increase and as the marketization of regions deepens. This study helps to fill an important gap in the existing literature. Platforms and lenders can use educational level for reference in identifying the default risk of borrowers.     

Insolvency regimes and firms' default risk under economic uncertainty and shocks

One of the arguments often advanced for implementing a stronger insolvency and bankruptcy framework is that it enhances credit discipline among firms. Using a large cross-country firm-level dataset, we empirically test whether a stronger insolvency regime reduces firms' likelihood of defaulting on their debt. In particular, we examine whether it reduces default risk during increased economic uncertainty and various external shocks. Our results confirm that a stronger insolvency regime moderates the adverse effects of economic shocks on firms' default risk. The effects are more pronounced for firms in the top half of the size distribution. We also explore channels through which improved creditor rights influence firms' default risk, including dependence on external finance, corporate leverage, and managerial ethics. Our main results are robust to an alternative measure of default risk, inclusion of currency and sovereign debt crisis episodes, and alternative estimations.     

结构性违约、信用分层与评级机构的策略性反应——来自中国信用债市场的证据

2014年以来，以主要集中于民企的"结构性违约"宣告了中国信用债市场刚性兑付的"结构性打破"，债券市场进入了新的发展阶段。利用2013-2017年债券数据，本文深入分析了结构性违约对评级机构行为和效率的影响，主要有如下发现：首先，信用评级的市场公信力会因政府隐性担保导致的"刚性兑付"而削弱；其次，结构性违约爆发之后，信用评级的整体效率有所提升，但在不同发行主体呈现分化，对于非城投类企业，信用评级对发行利差的影响显著增大，意味着评级公信力的显著提升，而对于城投类企业，评级效率并未明显改善；进一步研究表明，出现这种情况的原因在于评级机构在违约后采取了差异化的评级策略，对于违约风险较大的非城投类债券，评级机构倾向采取"收紧评级"的策略；对于违约风险较低的城投债，评级机构倾向于采取"放宽评级"的策略。这种策略性行为是导致市场"信用分层"的重要原因，并有可能推升民营企业的融资成本。     

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。     

债务违约、社会责任与政府支持

转型期中国，地方国有企业作为地区经济发展的一股中坚力量，当其发生债务违约时，作为实际控制人的地方政府究竟是否愿意提供支持，会优先选择哪些企业提供支持，政府的支持行为是否会影响到信贷资源的配置效率呢?为得到验证，文章选取2007-2016年沪深A股地方国有上市企业作为研究样本，实证分析了地方国企发生债务违约后，地方政府的支持行为及其对信贷资源配置效率的影响。研究结论表明：第一，地方国企发生债务违约后，地方政府会提供更多的"支持"，且支持额度与债务违约额呈显著正相关关系；第二，债务违约前，承担了更多社会责任（环保投资、捐赠以及就业等）的地方国企，在债务违约后可相应地获得更多的地方政府支持；第三，违约企业，尤其是得到更多政府支持的违约企业可获得更多的信贷资源，而其经营业绩却往往更差。由此表明政府干预一定程度上降低了信贷资源配置效率。     

Forecasting dynamic return distributions based on ordered binary choice

We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.     

Ruin probabilities in classical risk models with gamma claims

In this paper, we provide three equivalent expressions for ruin probabilities in a Cramér–Lundberg model with gamma distributed claims. The results are solutions of integro-differential equations, derived by means of (inverse) Laplace transforms. All the three formulas have infinite series forms, two involving Mittag–Leffler functions and the third one involving moments of the claims distribution. This last result applies to any other claim size distributions that exhibits finite moments.