对EWS模型中金融危机界定的比较研究 ——利用EVT理论

对主要金融危机预警模型中货币危机指标(货币压力指数)与阈值的确定方式进行比较分析,之后利用EVT对EMPI的尾部进行估计,同时确定新的个体阈值,并与原模型的结果进行对比。发现不同的EW S模型确定的危机时间有较大差异,各种方法在危机的判断上存在分歧;三种主要EW S模型中的EMPI在统计上表现出较强的非正态性。     

论风险投资行为及其规律

章从作提出的行为统计结构理论出发，认为包括风险投资行为在内的风险行为都遵循三条规律，即风险行为的预期效益引导规律，风险责任承担的制约规律，风险效用规律。     

我国住房抵押贷款证券化的必要性和可行性分析

随着我国房地产业的高速发展和单一的房地产银行融资手段，我国商业银行住房抵押贷款余额占其贷款余额的比重越来越高，从而使得我国商业银行面临的金融风险越来越大，而且单一的银行融资手段不利于我国房地产业的高速发展，住房抵押贷款证券化对我国房地产业和银行业的发展意义巨大。本文试分析了住房抵押贷款证券化这一金融创新工具在我国实行的必要性和可行性。     

外资进入对本土企业风险承担的影响：资本逐利还是风险规避

外资自由化作为我国资本市场开放的重要内容,在促进境内外资本要素互联互通的同时,也深刻影响着微观企业行为。以我国2007—2017年A股上市公司作为研究样本,实证检验外资进入对我国本土企业风险承担的影响。研究发现：外资进入能够显著提升企业的风险承担水平。进一步地,外资进入对企业风险承担的促进效应在融资约束严重、股权集中度更高和市场化进程缓慢地区的企业中更加显著。此外,在不同资本市场开放水平,外资进入对企业风险承担的作用存在明显差异。     

我国商业银行中小企业信贷风险管理研究

在经济新常态下,我国商业银行中小企业信贷业务面临着机遇和挑战。中小企业发展迅速,在推动就业、增加税收等方面起着重要作用。但其自身稳定性弱、风险大,使得商业银行对其谨慎放贷,中小企业融资难成为制约其发展的一大重要因素。论文通过对我国商业银行中小企业信贷现状及其风险形成进行分析,对商业银行中小企业信贷风险管理提出相应策略和管理措施。     

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.