Society For Risk Analysis Annual Meeting 2017

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

Government Investment & Finance Strategies for Risk Management

Room: Salon E   3:30 pm–5:00 pm

Chair(s): Saurabh Mishra

Sponsored by Risk and Development and Economics and Benefits Analysis Specialty Groups

T4-E.1  3:30 pm  Government support of investment projects as an instrument of risk management. Novikova TS*; Novosibirsk State University, Department of Economics

Abstract: Stimulation of investments with different types and levels of risk requires using of different institutional instruments and methods of evaluation. Government support for separate investment projects may reduce their risks and facilitate the implementation of projects with high economic but low financial efficiency. This paper proposes a methodology of risk management based on risk redistribution as a result of government support in terms of financial and economic efficiency. Two interrelated project models (financial and economic) with extended blocks of risk analysis were developed. To determine financial effects of the government support, indicators of net present value were used. The empirical part of the research is based on the analysis of real innovation projects in Novosibirsk region of Russia: 3 mega-projects of different catalysts, 18 micro-innovative projects of the Siberian Branch of the Russian Academy of Science, and a project of nanoceramic materials. For mega-projects, an extreme degree of support was used in the form of the direct investment financing, related to R&D with the highest level of risk. For other projects different versions of risk redistribution were analyzed under the appropriate forms of the government support and the corresponding change in the institutional framework of projects. For the project of nanoceramic materials, redistribution effects were estimated taking into account different ways a company uses R&D benefits, as well as various options for contractual relations between project participants and the corresponding change in its institutional framework. This was done on the example of investment in human capital. It demonstrates that government support has a positive effect only within the well-proven institutional design that profoundly balances the set of measures of risk management. It reduces their risks, improves the efficiency and promotes interest for private participants in the implementation of the PPP projects.

T4-E.2  3:50 pm  Selecting Investment Strategies for Disaster Risk Reduction in Developing Countries: The case of Flood Protection in the Rio Rocha Basin. Corderi Novoa D*, Hori T, Yarmin L; Inter-American Development Bank

Abstract: Despite the increasing evidence of the return to disaster risk reduction, investments to reduce risks have often been inadequate in developing countries. Previous analysis on risk reduction measures have focused on the evaluation of specific measures in isolation, failing to provide an assessment of the potential tradeoffs and interaction among alternative measures. The lack of understanding about the returns of alternative investment options has precluded policy-makers in developing countries to make investment decisions in a context of limited public funding. This article presents an integrated assessment model to help decision makers prioritize investments to reduce flood risks from a range of measures available. We develop a multi-disciplinary approach to evaluate investment alternatives. Combining probabilistic risk assessments, hydrologic, engineering and cost-benefit analysis with optimization techniques, we consider a wide array of measures and their interactions over a multi-year planning horizon. Our model incorporates the estimated costs and benefits of measures into a mixed integer linear program to explore the desirability of alternative flood reduction investment strategies using different performance criteria for a set of future scenarios. The model analyzes the optimal combination of measures and their sequencing within a planning horizon. We present the results of the local study and model application in the Rio Rocha Basin of Bolivia.

T4-E.3  4:10 pm  Country-based Assessment of Global Risk Profiles using Ensemble Deep Learning. Mishra S*, Ayyub B; University of Maryland College Park and International Finance Corporation

Abstract: There are both systemic and idiosyncratic rare events that constitute the risk profile of a country. Complexities in knowledge fusion, computational limits and interdependency among domains including economic, environmental, political, cultural or behavioral fluctuations pose a challenge for current models to inform the source of risk. Accommodating this multidimensionality constitutes the image of global risk profiles. However, earlier work has not approached country and location risk from the perspective of probabilistic reasoning. This paper provides an intelligent system architecture for tracking the current state and predictions for location risk monitoring. Using a newly constructed 3-dimensional multi-hazard risk space of high-dimension real-time data, we employ Dynamic time warping (DTW) to perform risk ontology classification. We compare results across various machine learning models and propose an ensemble deep neural network for global risk profiling using multivariate panel-time series classification. The results illustrate that the proposed ensemble deep learning model yields enhanced sense-making capabilities for executive decision making. Results show prospects for deep resource optimization given the portfolio mix of corporate investors and policymakers.

T4-E.4  4:30 pm  The Saga Continues: Insight Into The Greek Debt Crisis Through a Repeated Game. Welburn JW*, Hausken KH; RAND Corporation

Abstract: The Greek debt crisis began in 2010 and has continued through three rounds of bailout programs sponsored by the European Union, the European Commission, and the International Monetary Fund (the so-called troika). The crisis highlights several economic policy problems including commitment and moral hazard. That is, approving one bailout may provide temporary relief but risks committing the troika to future bailouts and creating a moral hazard which encourages excessive fiscal risk taking. We characterize the repeated negotiations between Greece and the troika as a two-player repeated game between a country and a financial authority. In each period the country faces the decision to repay debt, borrow, default, or seek a bailout. If a bailout is sought, a negotiation ensues where failed negotiations leave the country with the choice of either borrowing from credit markets or default. Players make decisions using beliefs formalized as probability distributions over uncertain characteristics of the opposing player and the world using the Harsanyi doctrine. Using the two-player repeated game, we explore how approving one bailout commits a financial authority to future bailouts and how repeated bailouts affect the probability of default. We use solutions to this game to shed light on the likelihood of a fourth bailout program in the Greek debt crisis and whether or not the bailouts contribute to eventual recovery or default. Our findings produce new insights for economic policy and managing default crises.

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