Expert judgement elicitation for risk assessments of critical infrastructures

Governmental bodies and companies are confronted with the problem of achieving rational consensus in the face of substantial uncertainties. The subject area of this special issue (risk and vulnerability assessments and management of critical infrastructures) might be a good example as are risk management of chemical installations and accident consequence management for nuclear power plants. Decisions with regard to infrastructures functioning and possible malfunctioning must be taken on the basis of predictions of technical and organizational system behaviour. These predictions use mathematical models containing scores of uncertain parameters. Decision makers want to take, and want to be perceived to take, these decisions in a rational manner. The question is, how can this be accomplished in the face of large uncertainties? One available source is experts in the many fields of interest within infrastructures. This paper describes the use of structured expert judgement in a formal manner. The paper refers to the Procedures Guide published by the European Union as EUR 18820. This Procedures Guide addresses two methods for using expert judgements developed at Delft University of Technology. The paired comparisons method is particularly suitable to identify the relative importance of attributes in the risk management arena, while the Classical Model, apt to arrive at subjective probability assessments, is particularly suitable to derive uncertainty distributions over model parameters. Examples will be referred to for further illustration of applications relevant in the field of risk assessment and risk management.     

Model risk of risk models

This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings; hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, further frustrating the reliability of risk readings. Finally, particular conclusions on the underlying reasons for the high model risk and the implications for practitioners and policy makers are discussed.     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

国外信用风险度量方法及其适用性研究

信用风险是银行面临诸多风险中最重要的风险,如何量化和控制信用风险一直是金融研究的热点领域之一。本文在对国外现代主流信用风险度量模型进行比较研究的基础上,分析了它们各自的特征和适用性,结合中资银行的特点,提出了适合我国国情的信用风险度量模型选取原则,基于我国现状建议在国内推广应用Logistic模型,以期对我国信用风险管理实务有一定的参考价值。     

Bond Systematic Risk and the Option Pricing Model

In this paper we examine the behavior of the systematic risk of corporate bonds. A model that assumes β is constant is compared with a model that allows systematic risk to vary in a manner consistent with the Black-Scholes-Merton Options Pricing Model. This procedure captures some fundamental properties of the movement of bond β and provides a starting point for improved models of the process generating bond returns.     

Model risk of contingent claims

Paralleling regulatory developments, we devise value-at-risk and expected shortfall type risk measures for the potential losses arising from using misspecified models when pricing and hedging contingent claims. Essentially, P&L from model risk corresponds to P&L realized on a perfectly hedged position. Model uncertainty is expressed by a set of pricing models, each of which represents alternative asset price dynamics to the model used for pricing. P&L from model risk is determined relative to each of these models. Using market data, a unified loss distribution is attained by weighing models according to a likelihood criterion involving both calibration quality and model parsimony. Examples demonstrate the magnitude of model risk and corresponding capital buffers necessary to sufficiently protect trading book positions against unexpected losses from model risk. A further application of the model risk framework demonstrates the calculation of gap risk of a barrier option when employing a semi-static hedging strategy.     

Die Ermittlung von Risikozuschlägen bei der Zeitwertbewertung von Verpflichtungen aus Schadenversicherungsgeschäften

The IASB proposes fair value accounting of insurance liabilities in the new IFRS on insurance contracts. These liabilities are not systematically traded in markets. Therefore the estimation of a fair value is only possible by simulating a market transaction. This simulation can be carried out by using financial models like the Capital Asset Pricing Model and the Economic Capital Model. In order to determine the fair value it has to be tested if those models can realistically calculate the insurance risk of the liabilities. This includes analysing the nature and extent of risk measurement as well as the assumptions the models are based on. The particular problem of the Capital Asset Pricing Model consists in measuring the risk by betas. An insurance beta can only be determined by relating it to other directly measurable betas. Those relationships can only be developed by putting forward special assumptions which increases the likelihood of a subjective valuation. The Economic Capital Model on the opposite is able to measure the insurance risk. The analysis of the models is carried out under simplified assumptions. Therefore it remains to be proven that the Economic Capital Model can also handle a more specific view of the insurance risk.     

Multivariate Tweedie lifetimes: the impact of dependence

Systematic longevity risk is increasingly relevant for public pension schemes and insurance companies that provide life benefits. In view of this, mortality models should incorporate dependence between lives. However, the independent lifetime assumption is still heavily relied upon in the risk management of life insurance and annuity portfolios. This paper applies a multivariate Tweedie distribution to incorporate dependence, which it induces through a common shock component. Model parameter estimation is developed based on the method of moments and generalized to allow for truncated observations. The estimation procedure is explicitly developed for various important distributions belonging to the Tweedie family, and finally assessed using simulation.     

Risikomanagement von Versicherungsunternehmen nach KonTraG

The KonTraG (law for control and transparency of firms) obliges corporations to install a risk management system. The common literature referring to the KonTraG only offers some requirements for risk management models. Yet, approaches for an implementation in the insurance practice cannot be found. On the other hand, there is a variety of general stochastic insurance models, but due to their high complexity a practical implementation for KonTraG purposes is doubtful. This article wishes to serve as a link by including KonTraG requirements in a theoretical model, which can easily be implemented in insurance practice with the help of modern IT. The main focus of this article is the development of a risk management model for property-liability and reinsurance companies.     

Modeling Operational Risk With Bayesian Networks

Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real‐time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested.     

Model risk of the implied GARCH-normal model

Model risk causes significant losses in financial derivative pricing and hedging. Investors may undertake relatively risky investments due to insufficient hedging or overpaying implied by flawed models. The GARCH model with normal innovations (GARCH-normal) has been adopted to depict the dynamics of the returns in many applications. The implied GARCH-normal model is the one minimizing the mean square error between the market option values and the GARCH-normal option prices. In this study, we investigate the model risk of the implied GARCH-normal model fitted to conditional leptokurtic returns, an important feature of financial data. The risk-neutral GARCH model with conditional leptokurtic innovations is derived by the extended Girsanov principle. The option prices and hedging positions of the conditional leptokurtic GARCH models are obtained by extending the dynamic semiparametric approach of Huang and Guo [Statist. Sin., 2009, 19, 1037–1054]. In the simulation study we find significant model risk of the implied GARCH-normal model in pricing and hedging barrier and lookback options when the underlying dynamics follow a GARCH-t model.     

Discretionary-accruals models and audit qualifications

The primary goal of this study is to evaluate the ability of the Cross-sectional Jones Model and the Cross-sectional Modified Jones Model to detect earnings management vis-à-vis their time-series counterparts by examining the association between discretionary accruals and audit qualifications. These two cross-sectional models have not been formally evaluated by prior research, and their use may offer certain advantages to investors and researchers over their time-series counterparts. A sample of 173 distinct firms with qualified audit reports and a matched-pair control sample with clean audit reports are used. Only the two cross-sectional models are consistently able to detect earnings management. One limitation of this study is that its findings merely indicate the superiority of the cross-sectional models vis-à-vis their time-series counterparts in an audit qualification setting, not validate either the former or the latter.     

Risk Management Lessons from the Credit Crisis

Risk management, even if flawlessly executed, does not guarantee that big losses will not occur. Big losses can occur because of business decisions and bad luck. Even so, the events of 2007 and 2008 have highlighted serious deficiencies in risk models. For some firms, risk models failed because of known unknowns. These include model risk, liquidity risk, and counterparty risk. In 2008, risk models largely failed due to unknown unknowns, which include regulatory and structural changes in capital markets. Risk management systems need to be improved and place a greater emphasis on stress tests and scenario analysis. In practice, this can only be based on position-based risk measures that are the basis for modern risk measurement architecture. Overall, this crisis has reinforced the importance of risk management.     

Tracking bond indices in an integrated market and credit risk environment

Abstract

The management of credit risky assets requires simulation models that integrate the disparate sources of credit and market risk, and suitable optimization models for scenario analysis. In this paper we integrate Monte Carlo simulation models for credit risk with scenario optimization, and develop a methodology for tracking broadly defined corporate bond indices. Testing of the models shows that the integration of the multiple risk factors improves significantly the performance of tracking models. Good tracking performance can be achieved by optimizing strategic asset allocation among broad classes of corporate bonds. However, extra value is generated with a tactical model that optimizes bond picking decisions as well. It is also shown that adding small corporate bond holdings in portfolios that track government bond indices improves the risk/return characteristics of the portfolios. The empirical results to substantiate the findings of this study are obtained by backtesting the model over a recent 30 month period.     

信用风险度量技术的最新发展及其在贷款定价中的运用

进入21世纪后,对产生于上个世纪的信用风险计量技术的持续研究进一步提高了风险计量的敏感度和准确度。本文剖析了主要的信用风险计量模型对风险变量的测算方法和适用性,探讨其最新发展,并分析了信用风险计量新技术在贷款定价的运用。     

A new family of equity style indices and mutual fund performance: Do liquidity and idiosyncratic risk matter?

We propose and test novel multifactor models of daily mutual fund performance. To this aim, we set up equity style indices and derive risk factors, which nest the established Fama and French (1992) and Carhart (1997) factors. We add two additional risk factors, namely idiosyncratic risk and Amihud (2002) liquidity. Our sample contains 528 actively managed mutual funds with European stock market focus during 2002 to 2009. Model estimation reveals that—while market excess return and size appear significant for the cross-section of all funds—the remainder factors explain the performance of subsets of funds. About one third of the funds exhibit significant factor sensitivities not only with respect to valuation or momentum, but also with respect to liquidity or idiosyncratic risk. No single risk factor is dominated and hence our six factor model may serve as a valid performance benchmark. In a four factor model setting, the Carhart model and a model with valuation replaced by liquidity perform best. Our results remain stable under various robustness checks. We further document that managers on average prefer liquid stocks, show no aggregate idiosyncratic risk preference and deliver results that are consistent with equilibrium models of fund performance.     

Knowledge mapping of model risk in banking

For years, bank management has relied on mathematical, statistical and financial models, which increasingly expose banks to model risk. The latter is also extended by the phenomenon of innovation: machine learning, artificial intelligence and big data make the models more and more sophisticated and difficult to manage. This study aims to clarify how the literature on model risk is evolving through a bibliometric survey to understand state of the art and identify the discussion topics, open questions and challenges for the future. The study results show that the literature on model risk is still quite young and sparse. The problems to be solved are conceptual, computational, and organizational. The considerations made lead to the question of whether adding further complexity to model risk management is a solution or whether, on the contrary, it creates new model risks.     

资金集中结算:制度完善与风险控制

本文从理论上阐明结算中心存在的产权基础,以及在此基础上观念的重塑。强调不同类型的集团管理模式下总部对财务管理的首要问题是风险控制和与集团整体利益相匹配的管理模式,而资金集中与结算是其重要的运作平台。并对两种主流结算模式下的风险控制制度进行了讨论。     

行业组合视角下的银行信贷优化管理——基于中国商业银行的优化模型设计与实证分析

本文在经济下行周期的背景下,从行业组合的视角探讨商业银行如何根据国家政策及自身战略积极布局信贷资产,从被动调控向主动管理转变,实现收益、风险及资本的优化.本文提出两个优化模型——基于最优增长率的均值方差基准模型和专家判断的主动配置模型,通过加入风险相关性、风险容忍度、经济资本等约束,为银行在不同风险偏好下积极配置资产提...