World Congress on Risk 2015
19-23 July, 2015, Singapore

Online Program

Session Schedule & Abstracts

* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.

Common abbreviations

Wednesday 22-07-2015

Business and Financial Risk Analysis

Room: Discovery   13:30–15:00

Chair(s): Ajita Atreya

1    Endogenous Risk and Business Cycle Regimes. Gan Vincent B.Y, Putra Business School (200)

Abstract: In this paper, we propose a dynamic measure of systemic risk, the Systemic Marginal Leverage Index or SysLev. It is symmetrical, proportional, scale invariant, naturally bounded and time dynamic and consistent. These key properties allow the observation of the evolution of systemic risk and the identification of crucial periods of “phase transition” in the real economy. The effectiveness of SysLev as an early warning indicator is evaluated and compared to the St. Louis Federal Reserve Financial Stress Index (STLFSI). Empirically, Markov switching is employed to evaluate the performance of SysLev as an early warning indicator. We find that SysLev outperforms STLFSI. We find that the impact of commercial banks adjusting their balance sheet in concert is highly asymmetric compared to asset prices and financial markets. Our analysis of “phase transitions” using Markov switching models show that SysLev was able to identify August 2008, October 2008 and April 2010 as critical periods. Further empirical analysis was performed to assess the performance of SysLev as an early warning indicator. We also assessed the impact of aggregate changes in the commercial banking sector’s balance sheet, that is, systemic marginal leverage, on the real economy. We model the relationship between the aggregate commercial banking sector’s balance sheet adjustments and the real economy as two and three regime Markov switching processes. Overall, we find that in comparison to STLFSI, our SysLev index outperforms the composite index by accurately capturing and issuing useful early warning signals as early as one year prior to the GFC as well as being able to detect the Dot-com crisis and the housing market correction. The evidence suggests that rapid systemic endogenous balance sheet adjustments of the commercial banking sector can affect the state and the magnitude as well the direction of changes in the real economy much more than asset prices and financial markets. Our results are robust to both constant and time varying transition probabilities.

2    Good Enterprise Risk Management Practices. Agarwal R, University of Edinburgh; Ansell J, University of Edinburgh (94)

Abstract: This research entails a debate between ERM and Antifragility with an aim to explore situations and approaches leading to best practices. ERM gains from the opportunities and attempts to reduce the risk of failure by interplay of risks. Apparently, Taleb (2013) have presented an alternative approach to risk management which is beyond robustness and resilience, known as ‘antifragility’. Antifragility claims to gain from error of others and has not considered risk appetite. This research establishes a contact with more than 50 senior executives in insurance companies to know their experience to handle situations existent in current dynamic financial markets. A comparative analysis has been drawn to address the issues highlighted in the literature which are still prevalent in the practice. The arguments between Antifragility and ERM discuss the scope of application, suitability of size of firms, use of model and risk appetite statement to explore best practices. Our finding suggests that corporate are using ‘knowledge and experience’ to take competitive advantage than using rational trial and error suggested in Antifragility to gain from disorder of market.

3    A Decision Analytical Methodology for Measuring the Sustainability of Automotive Value Chains. Stoycheva S, University Ca' Foscari Venice; Stocchetti A, University Ca' Foscari Venice (369)

Abstract: There are few industries as large, diverse and influential as the automotive industry. However, today the overall performance of the industry is characterised by overproduction and is dominated by mature markets in most developed countries. As a result sustainable development becomes a critical prerequisite for the competitiveness of the industry, which tries to respond to an increasing market demand for new solutions capable of minimizing environmental impacts in economically efficient ways. Although sustainable development is often considered as an ultimate value system to give orientation for decision making, managers face difficulties in accounting for the complexity in balancing social, ethical and environmental issues along with economic factors within the development of products or services. On one hand, decision makers have cognitive limitations to deal with complex decision problems. On the other hand, there is no universally accepted method to quantify all aspects of product sustainability. It is clear that there is need for a rigorous holistic approach allowing to i) better understand the complex and multifaceted nature of the sustainability aspects in product development, and ii) to develop tools to facilitate the complex decision making involved in it. To address these needs we have identified sustainability criteria specific for supply chains of products of the automotive industry by means of content analysis. The summarized criteria have been incorporated in a mathematical decision analytical model for measuring product sustainability. In order to account for the complex interplay of the numerous indicators and the values of various stakeholders, this practical approach is based on a quantitative Multi Criteria Decision Analysis (MCDA) method. MCDA has proven to be a useful tool well equipped to accommodate the aggregation of heterogeneous information with stakeholder perspectives in order to facilitate trade-offs among competing product alternatives.

4    Risk analysis for research and sustainable development of project portfolios. Virine L., Intaver Institute Inc. (288)

Abstract: The goal of risk analysis of project portfolios is to identify and prioritize risks, determine status of projects and portfolio with risks, rank projects within a portfolio, and identify impact of mitigation efforts. Project portfolios in the areas of research and sustainable development are significantly different from traditional portfolios. In research and sustainable development portfolios, the technological environment and risks associated with it are shifting very rapidly. Since such projects are mostly dealing with previously unknown information, many risks cannot be identified upfront and depend on previous activities. Risks’ probabilities and impacts change significantly over time. The paper presents a methodology of risk analysis for research and sustainable development project portfolios. Risk analysis is performed using the project portfolio model, which is comprised of project and portfolio schedules and a risk register. The risks from the risk register can be assigned to different projects within a portfolio and to different activities for each project. The relationships between risks can be defined such as what-if or correlation links. Risks’ mitigation and response plans may have action items that can have a relationship with project activities. Finally, the portfolios often have what-if relationship between activities or projects. The next step of the process is to perform Monte Carlo simulations on each project. The ranking or risks is performed based upon their impact on projects. The uncertainties are rolled up to the upper level of the portfolio hierarchy while taking into account project priorities. Projects within a portfolio will be prioritized based on their risk exposure. If the risk profile of a particular projects changes, it will be reflected in project portfolio. The methodology is actively used in different organizations including NASA, FAA, US Department of Energy, US Department of Health, USDA, Lockheed Martin, and many others.

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