互联网金融与传统金融业双向风险溢出效应研究

基于2013-2019年互联网金融指数和申万行业指数的日收盘价数据，采用GARCH族模型结合CoVaR方法，从定量计量和动态特征分析两方面入手，考察了互联网金融行业与传统金融业之间的双向风险溢出效应.结果表明，互联网金融与各传统金融业之间均存在双向不对称的正向风险溢出且传统金融业对互联网金融的风险溢出强度显著高于互联网金融对传统金融业的风险溢出强度；从整体来看，互联网金融与银行业之间双向风险溢出效应最强，但从局部分析，互联网金融可能会对证券业造成“激增式”风险溢出，不可掉以轻心；此外，互联网金融与各传统金融业之间的风险溢出还具有周期性特征.     

P2P保险的道德风险问题研究

伴随信息产业的快速发展和互联网保险的广泛运用,近几年来国际上出现了一种新型保险模式――P2P保险(Peer to Peer Insurance)。该模式基于互联网,通过聚集若干风险类型相似的投保人组建风险共担互助小组,风险互助小组成员间一般是亲戚、朋友等熟人关系。P2P保险形成的相互监督机制和声誉机制,可以较好地解决传统保险市场中存在的道德风险问题。本文利用经济学中的比较静态分析方法,研究了P2P保险的道德风险问题,并从理论上证明了在P2P保险模式下,投保人会倾向于更加努力防范风险以降低出险概率,道德风险问题可以得到有效缓解。     

互联网金融背景下我国上市商业银行的效率实证研究

本文通过数据包络分析(DEA)中的CRS模型和VRS模型,对我国16家上市商业银行于2007至2014年在互联网金融背景下的效率进行了测度。根据16家商业银行的综合效率、纯技术效率、规模效率的变动情况,来分析互联网金融对商业银行造成影响和冲击。结果表明,商业银行的整体效率波动与互联网金融环境的变化基本一致,并且,商业银行的规模效率变化与综合技术效率变化保持一致,股份制商业银行的规模效率一直高于国有商业银行。     

互联网金融创新与监管边界的演化博弈分析

互联网金融创新的本质是嵌入互联网基因创新金融的业务服务方式,但无法消除金融的固有风险,因此把握互联网金融创新与监管边界的平衡关系是互联网金融健康发展的关键.首先,在分析互联网金融创新主体与监管部门之间博弈行为的基础上,构建互联网金融创新发展中两者之间的动态博弈模型.其次,运用演化博弈方法探讨双方群体博弈行为规律,解释互联网金融创新发展与监管边界之间关系.最后,监管条例的出台是从没有监管边界、双方博弈边界到确认监管边界的动态博弈过程.     

模糊影响图评价算法在供应链金融信用风险评估中的应用

传统的银行信贷模式风险评价专注于个体企业的财务数据.供应链金融新融资模式下的信用风险评价不同于传统的融资模式风险评价,它的评价范围更宽,不确定性因素更加复杂.在分析供应链金融模式的信用风险评价体系的基础上,结合模糊集和影响图理论建立了模糊影响图评价模型,对评估中难以量化的问题进行模糊处理,对变量之间的模糊影响关系进行分析,最后计算出信用风险概率分布.方法定性与定量相结合,为供应链金融新模式下的风险评估提供了一种新思路.     

Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance

The primary objective in the discrimination problem is to assign a set of alternatives into predefined classes. During the last two decades several new approaches, such as mathematical programming, neural networks, machine learning, rough sets, multi-criteria decision aid (MCDA), etc., have been proposed to overcome the shortcomings of traditional, statistical and econometric techniques that have dominated this field since the 1930s. This paper focuses on the MCDA approach. A new method to achieve multi-group discrimination based on an iterative binary segmentation procedure is proposed. Five real world applications from the field of finance (credit cards assessment, country risk evaluation, credit risk assessment, corporate acquisitions, business failure prediction) are used to illustrate the efficiency of the proposed method as opposed to discriminant analysis.     

Supplier risk assessment based on trapezoidal intuitionistic fuzzy numbers and ELECTRE TRI-C: a case illustration involving service suppliers

Many companies today have embraced the concept of risk management, usually in the form of enterprise risk management or supply chain risk management. Both are based on a holistic view of risks. Hence, risks related to specific functions within a company must be considered more broadly than previously. Risks, however, involve uncertainty, and the less specific the context in which risks are viewed, the more uncertainty will be involved. One particular way to express uncertainty is through trapezoidal intuitionistic fuzzy numbers (TrIFNs). In this paper, risks that are relevant for supplier risk assessments are first collected from the literature. Then it is illustrated how the multi-criteria decision analysis method ELECTRE TRI-C can be used for sorting suppliers into risk categories, when the risks as well as some of the method’s parameters are expressed with TrIFNs. In order to do this, we make use of a small modification of an existing method for converting TrIFNs into crisp values. The approach is illustrated in a case problem based on a company that is looking for service providers (suppliers) of electrical maintenance. The problem involves 20 suppliers that are sorted into three risk categories based on evaluations from 27 criteria. Results from the case study point to two low risk suppliers. A further ad-hoc analysis suggests one of these to be less risky than the other.     

重大突发疫情下公众情绪演化模型与引导策略研究

重大突发疫情发生后,不同地区的公众在不同时期对风险认知不同,因此公众情绪类型及其变化呈现出不同的特点。现有研究多采用数据挖掘方式描述公众情绪的演化过程,并没有构建刻画公众情绪演化规律的数学模型,使得政府制定的疫情信息发布策略缺乏针对性,无法有效引导公众情绪。为此,本研究调查了新冠肺炎疫情下不同风险地区的公众情绪变化,发现公众对疫情的关注率很高,主流情绪表现为恐惧。通过等级比较法得到了不同风险地区公众在不同阶段的恐惧情绪水平,构建了公众恐惧情绪演化模型,并划分为潜伏期、爆发期和延续期三个时期。比较分析了公众恐惧情绪演化的共同特性和差异,为合理引导公众情绪提出信息发布策略,为落实防控措施、精准引导公众理性行为与避免出现极端情绪提供理论支撑,为重大疫情下情绪治理形成新的研究视角。     

SIR类型新型冠状病毒肺炎多阶段最优控制模型

新型冠状病毒肺炎(COVID-19)疫情在全球范围传播,给人们的健康带来了严重的威胁。面对疫情发展预期数据,我们需要在有限医疗资源的情况下确定疫情传播参数,以指导主要防疫措施的实施力度。本文采用SIR类型的模型描述新冠肺炎疫情发展,并建立多阶段最优控制模型确定疫情传播参数。为了高效确定参数取值,我们建立多项式时间可计算的半定规划近似模型。基于世界卫生组织发布的数据,我们求解近似模型,得到描述给定时段内美国新冠肺炎疫情发展态势的疫情传播参数,并分析疫情防控策略。     

网络舆情的研究与发展—以涉警网络舆情为例

网络舆情传播速度快、波及范围广,同时具有很强的突发性、不确定性、难以控制等特点,对相关部门进行涉警网络舆情干预和引导带来巨大挑战。目前有关网络舆情引导、管控机制、应对策略研究等成果丰富、但是对网络舆情危机的演变和干预多停留在定性分析之上、定量分析研究相对欠缺。现有网络舆情研究多针对舆情发展某一阶段进行研究,未结合网络舆情发展的多阶段属性展开全生命周期研究,为数不多的网络舆情多阶段干预决策机制研究也比较粗略,缺乏一套科学、规范的干预决策机制作为支撑。因此,结合网络舆情的多阶段属性、应用定量分析方法,是未来网络舆情干预决策机制研究的方向之一。本文通过对网络舆情发展过程的描述,给出一定量的状态表述和行动刻画,构建一个分类多阶段决策模型框架。     

贸易信用交易下的保险投资策略研究

贸易信用融资被广泛应用于解决中小企业融资困境,而保险正成为解决贸易信用融资风险的重要手段。本文站在核心企业角度,探究贸易信用融资保险的运营策略,运用Stackelberg博弈分析方法分别建立并比较了贸易信用融资、贸易信用融资保险、资金约束无融资、资金充足四类优化模型,探究了博弈均衡下的最优运营决策,并分析了零售商初始资金、生产商风险态度等关键参数的影响。研究表明:融资不仅对供应链有利,还能同时实现生产商及零售商共赢;当生产商风险厌恶程度、保险市场竞争程度较高,零售商初始资金较低时,融资保险能够为生产商及供应链创造价值,否则生产商应当放弃投保。研究结论为工业界合理且高效开展贸易信用融资保险运营提供了策略指导和管理启示。     

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance.     

Assessing the cost of capital for longevity risk

The cost of capital is a key element of the embedded value methodology for the valuation of a life business. Further, under some solvency approaches (in particular, the Swiss Solvency Test and the developing Solvency 2 project) assessing the cost of capital constitutes a step in determining the required capital allocation.Whilst the cost of capital is usually meant as a reward for the risks encumbering a given life portfolio, in actuarial practice the relevant parameter has been traditionally chosen, at least to some extent, inconsistently with such risks. The adoption of market-consistent valuations has then been advocated to reach a common standard.A market-consistent value usually acknowledges a reward to shareholders’ capital as long as the market does, namely if the risk is systematic or undiversifiable. When dealing with a life annuity portfolio (or a pension plan), an important example of systematic risk is provided by the longevity risk, i.e. the risk of systematic deviations from the forecasted mortality trend. Hence, a market-consistent approach should provide appropriate valuation tools.In this paper we refer to a portfolio of immediate life annuities and we focus on longevity risk. Our purpose is to design a framework for a valuation of the portfolio which is market-consistent, and therefore based on a risk-neutral argument, while involving some of the basic items of a traditional valuation, viz best estimate future flows and allocated capital. This way, we try to reconcile the traditional with a market-consistent (or risk-neutral) approach. This allows us, in particular, to translate the results obtained under the risk-neutral approach in terms of a properly redefined embedded value.     

基于贝叶斯学习的最优互联网化房地产金融合约

房地产金融的互联网化是当前金融市场创新的重要方向。本文采用动态道德风险理论研究了互联网化房地产金融合约的最优性及其经济特征,并以动态合约的视角考虑了参与方的贝叶斯学习对互联网化房地产金融最优性的影响。研究发现,为了激励资信良好的融资方努力工作且排除资信欠佳的融资方,互联网化房地产金融合约不仅需权衡激励成本和收益以确定适当的努力激励强度,而且还需考虑因融资方的信息优势带来的信息租金。     

Modelling dynamic portfolio risk using risk drivers of elliptical processes

The situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it difficult, for example, to find statistically significant temporal structures in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e. a large number of assets in the portfolio. This paper proposes a stochastic dynamic model which takes this situation into account. The modelling framework is based on multivariate elliptical processes which model portfolio risk via sub-portfolio specific volatility indices called portfolio risk drivers. The dynamics of the risk drivers are modelled by multiplicative error models (MEMs)-as introduced by Engle [Engle, R.F., 2002. New frontiers for ARCH models. J. Appl. Econom. 17, 425-446]-or by traditional ARMA models. The model is calibrated to Moody’s KMV Credit Monitor asset returns (also known as firm-value returns) given on a monthly basis for 756 listed European companies at 115 time points from 1996 to 2005. This database is used by financial institutions to assess the credit quality of firms. The proposed risk drivers capture the volatility structure of asset returns in different industry sectors. A characteristic cyclical as well as a seasonal temporal structure of the risk drivers is found across all industry sectors. In addition, each risk driver exhibits idiosyncratic developments. We also identify correlations between the risk drivers and selected macroeconomic variables. These findings may improve the estimation of risk measures such as the (portfolio) Value at Risk. The proposed methods are general and can be applied to any series of multivariate asset or equity returns in finance and insurance.     

Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach

This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited availability of risk factor event information and operational loss data, we rely on the elicitation of subjective probabilities, sourced from domain experts. Parameter sensitivity analysis is performed to validate and check the model's robustness against the beliefs of risk management and operational staff. To ensure that the domain's evolving risk profile is captured through time, a formal approach to organizational learning is investigated that employs the automatic parameter adaption features of the Bayesian network model. A hypothetical case study is then described to demonstrate model adaption and the application of the tool to operational loss forecasting by a business unit risk manager.     

A general model of insurance and investment risk diversification

This article presents a model of insurance and investment risk diversification. An in-depth analysis of the mathematical formulation of the risk is presented. In this regard, we introduce a new concept called the substitution principle to formulate the model rigorously. We show that, if the investment risks are normally non-linear, the insurance risks are linear in nature. This proves that the well-known diversification principle has to be viewed differently in finance and in insurance.     

Developing a risk-assesment methodology in the U.K.defence industry

As a leading defence equipment company, the Dynamics Divisionof British Aerospace Defence Company (BAeDef(DD)) has experiencedthe worst effects of post-Coldwar defence cuts, a global recession,and a determination by the U.K. Government to achieve greatervalue for money in defence procurement. The major tenet of thisnew approach to defence prwurement was 'eyes on, hands off',promoting a shift of the management of project risks from theMinistry of Defence (MOD) to industry. Initial industry reactionwas sceptical but bxame increasingly compliant as tenderingrequirements included visibility of contractors' risk assessment.This paper describes the risk-assessment methodology createdby BAeDef(DD) to meet these changed conditions. The human and organizational aspects of these developments onrisk have helped create a pro-active risk-management cultureat BAeDef(DD). Continuous assessment of project risks providesthe impetus for wider thinking beyond risk-taking or risk-aversionpreferences, leading to greater realism in planning. Decisionsto implement risk resolution measures require competence incost-benefit analysis. The conditions to foster this new behaviourare better-informed senior management, with the rejection ofbad news eliminated at all levels, and the laying of a pathto becoming a learning company.     

A new defined benefit pension risk measurement methodology

Defined benefit pension plan sponsors have taken on greater risks for sponsoring these plans in the last several years. Due to ever increasing concerns of longevity risk and the weak economic environment, sponsors are eager to understand their pension-related risks to facilitate optimal enterprise decision-making. Borrowing an analytical framework from the life insurance and annuity industry where the amount of risk is framed in terms of the total assets required to remain solvent over a one-year period with a high level of confidence, i.e., the economic capital approach, this paper develops a benchmark risk measure for pension sponsors by obtaining a total asset requirement for sustaining the pension plan. The difference between the total asset requirement and the actual trust assets thus provides a measure of sponsor assets at risk due to plan sponsorship. Two factor-based approaches are proposed for this calculation. The first approach develops a set of pension-specific factors as if the pension plan were a group annuity. The second approach directly simulates the risk drivers of the pension plan and develops a framework for obtaining factors and calculating the pension risk given a desired confidence level. Our approach is very easy to implement and monitor in practice.     

A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants

Modelling loss severity from rare operational risk events with potentially catastrophic consequences has proved a difficult task for practitioners in the finance industry. Efforts to develop loss severity models that comply with the BASEL II Capital Accord have resulted in two principal model directions where one is based on scenario generated data and the other on scaling of pooled external data. However, lack of relevant historical data and difficulties in constructing relevant scenarios frequently raise questions regarding the credibility of the resulting loss predictions. In this paper we suggest a knowledge based approach for establishing severity distributions based on loss determinants and their causal influence. Loss determinants are key elements affecting the actual size of potential losses, e.g. market volatility, exposure and equity capital. The loss severity distribution is conditional on the state of the identified loss determinants, thus linking loss severity to underlying causal drivers. We suggest Bayesian Networks as a powerful framework for quantitative analysis of the causal mechanisms determining loss severity. Leaning on available data and expert knowledge, the approach presented in this paper provides improved credibility of the loss predictions without being dependent on extensive data volumes.