从利益共同体到命运共同体:地方政府隐性债务的合作治理

在地方政府性债务的治理中,隐性债务因其增长迅速、规模不清和风险不确定,成为未来债务治理的重点和难点。隐性债务挟裹主体众多,关联利益甚广,因此,基于共同体视角,明确治理主体,有利于化解风险。在隐性债务生产阶段,因利而合的共同体大肆攫取利益导致隐性债务激增;在债务风险凸显阶段,利益共同体的本能选择不是"安危与共",而是"大难临头各自保"。但债务风险的"飞去来器效应"和群体性焦虑,将促使利益共同体走向命运共同体。命运共同体既是利益共同体,更是治理共同体。在隐性债务治理阶段,共同体应形成"共生"意识、采取"共治"行动、构筑"共担"保障,实现有效共治。     

Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach

This study is conducted to investigate the prediction of corporate financial distress based on the Merton (1974) market-based Distance to Default (DD) model over the period from 1997 to 2016 which covers a range of economic financial circumstances, including the Asian Financial Crisis (AFC) and Global Financial Crisis (GFC). The study focusses on the six largest countries in the ASEAN Economic Community (AEC), comprising of Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Unlike previous studies which focus mainly on bankruptcy, this paper focusses on early warning distress indicators that signal distress well before bankruptcy. This is when firms experience difficulty in servicing debt as measured by interest coverage ratio (ICR) at a firm level and non-performing loans (NPLs) at a country level. Key empirical findings from this paper indicate that the market-based distance-to-default (DD) model is generally a good early warning indicator of financial distress in the following year, particularly for ICR, but that prediction accuracy varies between individual countries in the Southeast Asian region.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

地方财政政策逆周期调节：财政分权还是债务增发*

财政政策工具实际操作偏离最初设计将会降低政策工具的调控效果，导致宏观经济波动加剧。针对地方政府财政政策的逆周期性调控目标，本文关注财政分权深化和政府债务增发的逆周期性效应。研究发现：地方政府财政政策总体呈现顺周期特征。财政分权程度的提高放大了地方财政政策的顺周期性。地方政府债务增发使地方财政政策呈现逆周期特征。地方政府财政政策及其收入面、支出面均呈现顺周期性，由“顺”转“逆”的债务平衡点分别为0137，0113，0209。当前绝大部分地区的债务规模都在债务平衡点之下。驱动地方政府债务逆周期调控的内在因素是官员晋升激励。晋升激励的程度越高，地方债务逆周期调控的力度就越大。但外部市场融资环境的波动削弱了地方政府债务逆周期调控的力度。本文的政策主张是，不宜过度财政分权，应该扩大地方债发行规模，促使地方财政政策逆周期调节。     

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.     

地方政府债务空间与空间债务——基于地根经济的区位黏性视角

本文从人口流动的区位黏性这一全新视角，将地方政府性债务的李嘉图等价命题纳入新经济地理研究框架进行理论分析。为了进一步揭示理论模型的政策含义，采用空间计量方法验证沿海地区地方政府性债务的局部正相关特征。最优的债务政策取决于经济系统的初始条件，在非对称经济结构已经存在的情况下，单纯建设本地区的基础设施只会降低区域内的交易成本，这对于那些仅着眼于投巨资用于城市“硬件”建设的地方政府来说显然是一叶障目。建议依靠基础设施网络提高区域协同创新能力，培育规模报酬递增的专业化产业集群。     

Is there a diversification “cost” of Shari’ah compliance? Empirical evidence from Malaysian equities

Islamic equity portfolios work with a smaller investment universe given the filtering of non-Shari’ah compliant stocks. It has been theoretically argued that this culminates in suboptimal portfolio diversification, which in turn adversely affects risk-adjusted returns. We offer empirical evidence that such a conceived portfolio diversification “penalty” is far from a foregone conclusion, at least empirically. Our results tend to indicate that Islamic portfolios are not invariably handicapped in terms of portfolio diversification. We also explored dimensions that may account for differences in the relative investment performance between Islamic and conventional portfolios, such as portfolio constraints, short selling and market conditions. We believe this paper is among the first to apply substantial empirical analysis specifically with respect to the portfolio diversification perspective on Islamic equity investments.     

Private information in currency markets

Using daily abnormal currency returns for the universe of countries with flexible exchange rates, we show local currency depreciations ahead of unscheduled, public sovereign debt downgrade announcements. Consistent with the private information hypothesis, the effect is stronger in lower institutional quality countries and holds after we control for concurrent public information and for publicly available rumors about the forthcoming downgrades. Our results persist when abnormal currency returns are adjusted for global carry and dollar risk factors, world equity and bond returns, as well as local stock market returns. Finally, the currency depreciations are permanent, providing evidence for a link between fundamentals and currency markets.     

Firm financing and growth in the Arab region

This paper documents how firms in Arab countries use equity, corporate bond and syndicated loan markets to obtain financing and grow. Working with a new dataset on issuance activity in domestic and international markets and firm performance, the paper finds that capital raising through these markets has grown rapidly since the early 1990s and involved an increasing number of firms. Whereas the amounts raised in equity and loan markets (relative to gross domestic product) stand well relative to international standards, bond issuance activity lags behind. However, bond financing has gained importance over time. Equity issuances take place primarily in domestic markets, whereas bonds and loans are mostly issued internationally, display long maturities and entail low levels of credit risk. Issuing firms from the Arab region are very large compared to international standards. They also tend to be larger, faster growing and more leveraged than non-issuing firms in Arab countries.     

Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting

We present a simple quantile regression-based forecasting method that was applied in the probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data are log transformed and split into a long-term trend component and a remainder term. The key forecasting element is the quantile regression approach for the remainder term, which takes into account both weekly and annual seasonalities, such as their interactions. Temperature information is used only for stabilizing the forecast of the long-term trend component. Information on public holidays is ignored. However, the forecasting method still placed second in the open data track and fourth in the definite data track, which is remarkable given the simplicity of the model. The method also outperforms the Vanilla benchmark consistently.