A Bayesian approach for more reliable tail risk forecasts

This paper demonstrates that existing quantile regression models used for jointly forecasting Value-at-Risk (VaR) and expected shortfall (ES) are sensitive to initial conditions. Given the importance of these measures in financial systems, this sensitivity is a critical issue. A new Bayesian quantile regression approach is proposed for estimating joint VaR and ES models. By treating the initial values as unknown parameters, sensitivity issues can be dealt with. Furthermore, new additive-type models are developed for the ES component that are more robust to initial conditions. A novel approach using the open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and empirical results highlight the improvements in risk forecasts ensuing from the proposed methods.     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

What lies beneath—Negative interest rates and bank lending

We study the transmission of negative interest rates to bank lending around an unexpected policy rate cut into deep negative territory by the Swiss National Bank (−0.75%). We exploit a rich data set on transaction-level corporate loans matched with bank balance sheet data. We find that banks more affected by negative interest rates offer looser lending terms and lend more than other banks. This result is consistent with the risk-taking channel, where a lower policy rate spurs bank risk-taking to maintain profits. The result implies that, even in such deep negative territory, the reversal rate has not yet been hit.     

Network based evidence of the financial impact of Covid-19 pandemic

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analyzing the top 50 S&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.     

Liability for Past Environmental Contamination and Privatization

This paper examines the role ofliability for past environmental contaminationin the privatization processes of Central andEastern Europe. In particular, it establishes alink between a risk-averse investor's amount ofinformation regarding the extent of pastenvironmental contamination (and its cleanupcosts) and the investor's willingness to payfor a particular enterprise, i.e., bid. As theinvestor obtains a more precise estimate of theuncertain cleanup costs, the investor facesless risk; therefore, the investor's riskpremium falls and the investor's bid rises.This link generates four hypotheses regarding aprivatization agency's responses to theinvestor's knowledge of clean-up costs.     

Utility Regulation and Risk Allocation: The Roles of Marginal Cost Pricing and Futures Markets

The effects on consumer welfare of requiring a utility facing cost or demand risk to use either a fixed retail price or marginal cost pricing are assessed. With marginal cost pricing and cost volatility an efficient futures market allows consumer welfare to be at least as high in every state as with the fixed price. With demand risk marginal cost pricing can benefit the consumer in every state without harming the firm if the profit difference is transferred to the consumer. A futures market can act as a partial replacement for the transfer.     

巨灾风险证券化及在中国保险市场的应用

在分析了巨灾风险的损失频率与损失幅度的特点、传统保险与再保险对抗巨灾风险的局限性等方面的基础上，重点介绍了巨灾风险证券的几种产品类型，分析了各种风险转移方式的优缺点。最后，根据中国保险市场的特点，从应用环境和应用条件两个方面提出了开展巨灾风险证券化应用的若干设想。     

振兴东北老工业基地资本运营效益研究

现阶段,东北老工业基地存在着高消耗、低性能、低附加值、产品生产能力过剩等问题,因而,在市场经济的大潮中,造成了竞争力减弱、经济效益下滑的局面.发达国家的实践告诉我们,传统产业高科技化能改变这种局面,但高科技需要大量投入资本,故而需要研究传统产业高科技化资本运营的模式,从而降低高风险,实现高效益.     

信用的生态学诠释与信用生态链的构建

生态学有逐渐与经济学相渗透、相融合的趋势，从而扩展了经济学的研究边界。本文从生态学的视角对信用加以研究，首先介绍了信用生态的理论内涵，其次研究了良好的社会信用生态环境的评价标准，最后对构建和谐的社会信用生态链提出了具体的对策措施。     

Can the Risks of the Kyoto Mechanisms be Reduced Through Portfolio Diversification? Evidence from the Swedish AIJ Program

Through Joint Implementationand the Clean Development Mechanism, reductionsof greenhouse gas emissions achieved abroad canbe credited to domestic firms. However, thetechnical, economic and political risksinvolved may prevent the private sector frominvesting in such projects. This paperdescribes three types of risks which emissionreduction projects are exposed to. Eleven pilotprojects carried out under the ActivitiesImplemented Jointly (AIJ) program and financedby Sweden are evaluated. Actual project costsare found to exceed projected costs in allcases. Annual emission reductions also deviatefrom their expected values and vary stronglyover time, supporting the hypothesis that suchprojects are risky business. The riskmanagement tool portfolio diversification isapplied to a sample of Swedish AIJ projects.The results indicate that diversification canreduce the risks of greenhouse gas mitigationprojects significantly. Thus, carbon funds area promising way of lowering the risks of theKyoto Mechanisms for private sector investors.