Relevant bibliographies by topics / Cost of risk

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cost of risk'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Cost of risk"

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Nowak, Edward. "COST RISK AND THE COST OF RISK." Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki Finansowe Ubezpieczenia 88 (2017): 511–18. http://dx.doi.org/10.18276/frfu.2017.88/1-49.

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Kim, Sun-Hwa, and Yong-Ki Jung. "Carbon Risk and the Cost of Debt." Korean Accounting Review 42, no. 2 (April 30, 2017): 169–213. http://dx.doi.org/10.24056/kar.2017.04.002.

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Quarnstrom, Fred. "RISK VERSUS COST?" Journal of the American Dental Association 137, no. 3 (March 2006): 288–90. http://dx.doi.org/10.14219/jada.archive.2006.0159.

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ANDREI, Florin. "Internal Control system: Cost of Risk vs. Risk Management. Case study: Romanian Banking System." Logos Universality Mentality Education Novelty. Section: SOCIAL SCIENCES 04, no. 01 (June 30, 2015): 133–43. http://dx.doi.org/10.18662/lumenss.2015.0401.12.

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MICHALAK, Aneta. "Cost of capital and risk in management and quality science." Scientific Papers of Silesian University of Technology. Organization and Management Series 2020, no. 142 (2020): 233–42. http://dx.doi.org/10.29119/1641-3466.2020.142.17.

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Webb, Kenneth H. "Cost-Effectiveness/Risk Factors." Pediatrics 106, no. 2 (August 1, 2000): 377.2–378. http://dx.doi.org/10.1542/peds.106.2.377-a.

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Shen, Yujing, and Randall P. Ellis. "Cost-minimizing risk adjustment." Journal of Health Economics 21, no. 3 (May 2002): 515–30. http://dx.doi.org/10.1016/s0167-6296(02)00005-x.

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Biery, Fred, David Hudak, and Shishu Gupta. "Improving Cost Risk Analyses." Journal of Cost Analysis 11, no. 1 (March 1994): 57–85. http://dx.doi.org/10.1080/08823871.1994.10462285.

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Frankel, Gerald. "Malpractice Risk and Cost." Journal of the American College of Surgeons 213, no. 3 (September 2011): 454. http://dx.doi.org/10.1016/j.jamcollsurg.2011.05.021.

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Michaels, Jack V. "Risk-Cost Model for Bounding System Acquisition Cost." Journal of Parametrics 7, no. 1 (March 1987): 13–37. http://dx.doi.org/10.1080/10157891.1987.10472794.

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Dissertations / Theses on the topic "Cost of risk"

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Shen, Zhihua. "Cost: a possible explanation for risk premium?" Thesis, Montana State University, 1995. http://etd.lib.montana.edu/etd/1995/shen/ShenZ1995.pdf.

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Transaction costs, information costs and defaults costs are suspected to partially explain differences in returns which were previously attributed to risk premiums in the financial markets. Two portfolios with identical costs are constructed from a Put, a Call, and underlying S&P 500 stocks, with the first Portfolio being hedged (Put-Call Parity) and the second Portfolio being unhedged (with systematic beta close to 2). The expected stock prices of S&P 500 were calculated based on 52-year historical data using several methods, and returns of the two portfolios were obtained and compared, using 935 observations of S&P 500 from January 2, 1992 through June 30, 1992. A linear regression model adjusted for cross-sectional heteroskedasticity and auto-correlation was used to estimate the expected risk premium rate. Transaction, information and default costs were statistically significant and estimated at 0.6% of value annually. These costs reduce the risk premium estimated by the Capital Asset Pricing Model.
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Omar, Sadek A. Thomas Randolph. "Identifying the risk in cost reimbursable contracts." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4843/index.html.

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Ren, Hong. "Risk management in construction cost and inflation." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332040.

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Aggarwal, Taroon. "Prediction markets for cost and risk assessment." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67211.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-99).
Several temporal and political factors can sometimes limit the effectiveness of traditional methods of project tracking and cost estimation. A large organization is susceptible to internal and external risks that are difficult to predict by a single person. Use of collective intelligence tools can help gather inputs from a crowd of people and help provide insight into future events with their collective wisdom. A prediction market is one such tool that provides an environment for traders to buy and sell contracts, whose values are tied to uncertain future events. Efficient prediction markets have been shown to outperform available polls and other forecasting mechanisms. This thesis focuses mainly on the features of a prediction market, its use in the context of a large organization and the steps needed for its implementation. We believe that prediction markets can be a useful supplementary tool along with the existing cost estimation and project management tools in a large organization. They can help aggregate information and identify any direct or indirect factors that can impact cost, or schedule estimates, or create risk for the completion of a project. Major design principles for implementation of prediction markets have been identified by the author based on seven mini case studies from different industries. The author also conducted three pilot studies at MIT and the observations from these have been used to identify best practices related to design and implementations of markets. We found increased involvement of participants and increased awareness in the projects to be one of the major benefits of prediction markets. From the case studies, research and data collected from simulations, we found positive evidence that prediction markets can supplement the use of current estimation and risk assessment methodologies when deployed correctly, and help keep a check on the pulse of an organization by preparing it for any future events or outcomes.
by Taroon Aggarwal.
S.M.in Engineering and Management
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Yeager, Elizabeth Anne. "Impact of risk on cost and revenue efficiencies." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/13161.

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Doctor of Philosophy
Department of Agricultural Economics
Michael Langemeier
This study focused on the inclusion of risk in efficiency measures to determine its impact on traditional efficiency scores. Previous research and theory suggests efficiency scores will be lower under risk and for risk averse individuals. Risk aversion may deter use of new production technologies and production levels may not be as high as under other risk preferences. Two data sets were used in the analysis. A panel data set of 256 farms from 1993-2010 was used to address the impact of risk measured as variability in outputs and downside risk on efficiency. A separate data set of 258 farms for 2008 was used with a corresponding risk preference score to determine the impact of risk preference on efficiency. The risk preference scores in the sample ranged from 5 to 86 where a smaller value represents stronger risk aversion. Data envelopment analysis was used to construct a nonparametric efficiency frontier and calculate cost- and revenue-based economic, overall, technical, allocative, and scale efficiency measures. Five inputs: labor, crop input, fuel, livestock input, and capital; and two outputs: crops and livestock were used in the analysis. The results focused on cost- and revenue-based economic efficiency. They showed that risk did affect average efficiency scores and is necessary to include in efficiency analysis. The average cost efficiency without risk was 0.6763. It increased to 0.7200 and 0.7018 respectively when cost efficiency was adjusted to recognize variability in outputs and downside risk. The average portion of cost inefficiency explained by variability in outputs was 28.06 percent. Downside risk explained 22.66 percent of cost inefficiency. The average revenue efficiency without risk was 0.7611 and increased to 0.8372 and 0.7811 when revenue efficiency was adjusted for variability in outputs and downside risk, respectively. Variability in outputs explained 42.53 percent and downside risk explained 30.58 percent of revenue inefficiency. The average cost efficiency for the 258 farms was 0.5691 and increased to 0.6043 with the consideration of risk preference scores. The average revenue efficiency was 0.6735 and increased to 0.6987 with risk preference scores. The efficient farms varied across cost and revenue efficiency, and the risk measures used. This lends support to the use of both input-oriented (cost) and output-oriented (revenue) efficiency measures as well as the use of multiple measures of risk.
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Hercock, Carol Ann. "Specialisation for fast locomotion : performance, cost and risk." Thesis, University of Liverpool, 2010. http://livrepository.liverpool.ac.uk/3453/.

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The racing Greyhound presents us with an opportunity to study the characteristics of a successful athlete and the costs and risks such specialisation entails. This thesis investigates the nature of the injuries suffered by racing Greyhounds and how adaptation of the musculoskeletal system to the unique pattern of stresses encountered during racing and training might impact upon the risk of injury. Racing Greyhounds sustain a number of musculoskeletal injuries. Several of these, notably fatigue fractures of distal limb bones, are very similar to those seen in human athletes and military recruits (Armstrong et al. 2004; Beck et al. 2000; Brukner et al. 1996; Kowal 1980; Matheson et al. 1987). The most common, often leading to the dog being euthanatised, is fracture of the right tarsus. Evaluation of tarsal fractures via radiography alone frequently resulted in an underestimation of the severity of the injuries, whereas the use of computed tomography provided a more detailed, accurate assessment. Evidence of asymmetric bone remodelling was found in the distal limb bones of racing Greyhounds. Rail‐side bones had significantly higher bone density and increased levels of bone resorption and formation markers compared to contralateral bones. Greyhound bones also have regional differences in trabecular architecture. In contrast, Staffordshire Bull Terrier (SBT) bones did not show these differences. Additionally, Greyhound distal limb tendons appear well adapted to withstand the high stresses of racing; they are stronger, stiffer, and in the pelvic limbs, return more elastic strain energy than the corresponding SBT tendons. Greyhounds had left‐to‐right asymmetries in the tensile properties of their pelvic limb tendons, which SBTs did not. SBTs are not bred for racing and are unlikely to encounter asymmetric stresses. Therefore, the adaptive changes observed in the Greyhound bones and tendons appear to result from the asymmetric stresses encountered by the Greyhounds during racing around ovoid tracks.
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Viduto, Valentina. "A risk assessment and optimisation model for minimising network security risk and cost." Thesis, University of Bedfordshire, 2012. http://hdl.handle.net/10547/270440.

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Network security risk analysis has received great attention within the scientific community, due to the current proliferation of network attacks and threats. Although, considerable effort has been placed on improving security best practices, insufficient effort has been expanded on seeking to understand the relationship between risk-related variables and objectives related to cost-effective network security decisions. This thesis seeks to improve the body of knowledge focusing on the trade-offs between financial costs and risk while analysing the impact an identified vulnerability may have on confidentiality, integrity and availability (CIA). Both security best practices and risk assessment methodologies have been extensively investigated to give a clear picture of the main limitations in the area of risk analysis. The work begins by analysing information visualisation techniques, which are used to build attack scenarios and identify additional threats and vulnerabilities. Special attention is paid to attack graphs, which have been used as a base to design a novel visualisation technique, referred to as an Onion Skin Layered Technique (OSLT), used to improve system knowledge as well as for threat identification. By analysing a list of threats and vulnerabilities during the first risk assessment stages, the work focuses on the development of a novel Risk Assessment and Optimisation Model (RAOM), which expands the knowledge of risk analysis by formulating a multi-objective optimisation problem, where objectives such as cost and risk are to be minimised. The optimisation routine is developed so as to accommodate conflicting objectives and to provide the human decision maker with an optimum solution set. The aim is to minimise the cost of security countermeasures without increasing the risk of a vulnerability being exploited by a threat and resulting in some impact on CIA. Due to the multi-objective nature of the problem a performance comparison between multi-objective Tabu Search (MOTS) Methods, Exhaustive Search and a multi-objective Genetic Algorithm (MOGA) has been also carried out. Finally, extensive experimentation has been carried out with both artificial and real world problem data (taken from the case study) to show that the method is capable of delivering solutions for real world problem data sets.
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Saunders, Brian J. "A Total Cost Approach to Supply Chain Risk Modeling." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/3179.

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The modern supply chain is long, complex, interconnected and global, and plays a fundamental role in business competitiveness. These conditions, along with various supply chain management trends in recent years have increased risks in supply chains which threaten supply chain performance. Greater impact, especially on cost, from an increased threat of supply disruptions is one area of particular concern. Companies today are struggling to find effective means to manage this increased risk and avoid adverse financial impacts. An approach to managing supply disruption risk in supply chains based on the minimization of the total cost of ownership (TCO) of the supply chain is explored in this thesis. Insights are provided into an appropriate view of supply chain risk and a general four step risk management process to guide the design and evaluation of a new risk management tool based on such an approach. A prototype of the new total cost-based, modeling and simulation tool was created in partnership with ProModel Corporation and a government contractor that requested to remain anonymous. A preliminary assessment of the effectiveness of this tool in minimizing TCO and providing an interface useable by non-modelers is provided. This study also reviews and compares a sample set of current supply chain risk management methods and tools and compares them with the new tool for relevance in aiding users in managing supply disruption risk. Based on literature findings and preliminary feedback from pilot contextual demonstrations of the tool, the total cost approach to risk modeling appears promising, although the execution needs to be improved with further enhancements made to the prototype tool. In this preliminary study and evaluation, sufficient evidence is not available to determine that the new prototype tool is any more effective than other currently available risk management tools to provide necessary information to make supply chain risk management decisions that minimize TCO of a supply chain. Suggestions for further development of the tool, especially for improvement of the total cost approach, are provided as well as a preliminary evaluation procedure and survey instruments for a more robust evaluation of the new tool.
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Papin, Timothée. "Pricing of Corporate Loan : Credit Risk and Liquidity cost." Phd thesis, Université Paris Dauphine - Paris IX, 2013. http://tel.archives-ouvertes.fr/tel-00937278.

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This PhD thesis investigates the pricing of a corporate loan according to the credit risk, the liquidity cost and the embedded prepayment option. A loan contract issued by a bank for its corporate clients is a financial agreement that often comes with more flexibility than a retail loan contract. These options are designed to meet clients' expectations and can include e.g., a prepayment option (which entitles the client, if he desires so, to pay all or a fraction of its loan earlier than the maturity). The prepayment is the main option and it will be study in this thesis. In order to decide whether the exercise of the option is worthwhile the borrower compares the remaining payments with the outstanding amount of the loan. If the remaining payments exceed the nominal value then it is optimal for the borrower to refinance his debt at a lower rate. For a bank, the prepayment option is essentially a reinvestment risk, i.e. the risk that the borrower decides to repay earlier his/her loan and that the bank cannot reinvest his/her excess of cash in a new loan with same characteristics.The valuation problem of the prepayment option can be modelled as an embedded compound American option on a risky debt owned by the borrower. We choose in this thesis to price a loan and its prepayment option by resolving the associated PDE instead of binomial trees (time-consuming) or Monte Carlo techniques (slow to converge).
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Brau, Rojas Agustin. "Controlled Markov chains with risk-sensitive average cost criterion." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/284004.

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Discrete controlled Markov chains with finite action space and bounded cost per stage are studied in this dissertation. The performance index function, the exponential average cost (EAC), models risk-sensitivity by means of an exponential (dis)utility function. First, for the finite state space model, the EAC corresponding to a fixed stationary (deterministic) policy is characterized in terms of the spectral radii of matrices associated to irreducible communicating classes of both recurrent and transient states. This result generalizes a well known theorem of Howard and Matheson, which treats the particular case in which the Markov cost chain has only one dosed class of states. Then, it is shown that under strong recurrence conditions, the risk-sensitive model approaches the risk-null model when the risk-sensitivity coefficient is small. However, it is proved and also illustrated by means of examples, that in general, fundamental differences arise between both models, e.g., the EAC may depend on the cost structure at the transient states. In particular, the behavior of the EAC for large risk-sensitivity is also analyzed. After showing that an exponential average optimality equation holds for the countable state space model, a proof of the existence of solutions to that equation for the finite model under a simultaneous Doeblin condition is provided, which is simpler than that given in recent work of Cavazos-Cadena and Fernandez-Gaucherand. The adverse impact of "large risk-sensitivity" on recently obtained conditions for the existence of solutions to an optimality inequality is illustrated by means of an example. Finally, a counterexample is included to show that, unlike previous results for finite models, a controlled Markov chain with infinite state space may not have ultimately stationary optimal policies in the risk-sensitive (exponential) discounted cost case, in general. Moreover, a simultaneous Doeblin condition is satisfied in our example, an assumption that enables the vanishing discount approach in the risk-null case, thus further suggesting that more restrictive conditions than those commonly used in the risk neutral context are needed to develop the mentioned approach for risk-sensitive criteria.
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Books on the topic "Cost of risk"

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Integrated cost-schedule risk analysis. Farnham, Surrey: Gower, 2011.

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L, Kirkham Richard, ed. Whole life-cycle costing: Risk and risk responses. Oxford: Blackwell, 2004.

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R, Brown Jeffrey. Mortality risk, inflation risk, and annuity products. Cambridge, MA: National Bureau of Economic Research, 2000.

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Postle, Meg. Cost-benefit analysis and chemical risk management. Ottawa: International Council on Metals and the Environment, 1997.

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Almeida, Heitor. The risk-adjusted cost of financial distress. Cambridge, MA: National Bureau of Economic Research, 2005.

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Almeida, Heitor. The risk-adjusted cost of financial distress. Cambridge, MA: National Bureau of Economic Research, 2005.

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Trendowicz, Adam. Software Cost Estimation, Benchmarking, and Risk Assessment. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30764-5.

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Barraclough, Mike. Guide to safer employment: Cost versus risk. Cambridge: Director Books, 1992.

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Jagannathan, Ravi. Consumption risk and the cost of equity capital. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Jagannathan, Ravi. Consumption risk and the cost of equity capital. Cambridge, MA: National Bureau of Economic Research, 2005.

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Book chapters on the topic "Cost of risk"

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Rao, T. V. S. Ramamohan. "Cost Sharing." In Risk Sharing, Risk Spreading and Efficient Regulation, 101–42. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2562-1_7.

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Porras, Eva R. "Country Risk." In The Cost of Capital, 165–92. London: Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230297678_6.

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Mishan, E. J., and Euston Quah. "Risk and certainty equivalence." In Cost-Benefit Analysis, 171–73. Sixth edition. | Milton Park, Abingdon, Oxon ; New York : Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9781351029780-34.

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Reimer, Kristina. "Theoretical Foundations of Credit Risk Fundamentals and Methods of Determining Credit Risk." In Asymmetric Cost Behavior, 39–61. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-22822-4_3.

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Roosen, Jutta. "Cost-Benefit Analysis." In Risk - A Multidisciplinary Introduction, 309–31. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04486-6_11.

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Reimer, Kristina. "Does Cost Stickiness Affect Credit Risk?" In Asymmetric Cost Behavior, 63–106. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-22822-4_4.

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Reimer, Kristina. "Does Cost Stickiness Affect Financial Risk?" In Asymmetric Cost Behavior, 107–27. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-22822-4_5.

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Querner, Immo. "Aggregate Risk-Reduction Cost." In An Economic Analysis of Severe Industrial Hazards, 185–91. Heidelberg: Physica-Verlag HD, 1993. http://dx.doi.org/10.1007/978-3-642-95898-4_9.

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Heidenreich, Paul A., and Harlan M. Krumholz. "Cost-Effectiveness of Risk Factors." In Atlas of Atherosclerosis, 259–72. London: Current Medicine Group, 2003. http://dx.doi.org/10.1007/978-1-4615-6484-3_13.

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Krumholz, Harlan M., and Lee Goldman. "Cost-Effectiveness of Risk Factors." In Atlas of Atherosclerosis, 237–50. London: Current Medicine Group, 2000. http://dx.doi.org/10.1007/978-1-4757-9310-9_13.

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Conference papers on the topic "Cost of risk"

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Talavera, R., and J. Jimenez. "Cost of Risk." In SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production. Society of Petroleum Engineers, 1998. http://dx.doi.org/10.2118/46651-ms.

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Manta, Andreea-Mariana, Cristina Dima, and Andreica Marin. "A New Cost in Measuring Investment Performance - Cost of Non – Intervention." In International Conference Risk in Contemporary Economy. Dunarea de Jos University of Galati, Romania Faculty of Economics and Business Administration, 2019. http://dx.doi.org/10.35219/rce2067053214.

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Latta, J. Carolyn. "REAL Cost-Risk: The Risk Management Imperative." In Space 2006. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7217.

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Graham, David. "Continuous Cost-Risk Management." In Space 2004 Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-6069.

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Yang, Ye, Barry Boehm, and Betsy Clark. "Assessing COTS integration risk using cost estimation inputs." In Proceeding of the 28th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1134285.1134345.

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Karakostas, C. Z., and V. K. Papanikolaou. "A low-cost instrumentation approach for seismic hazard assessment in urban areas." In RISK ANALYSIS 2014. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/risk140091.

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Li, Yajun, Michael A. Hicks, and Philip J. Vardon. "Cost-Effective Design of Long Spatially Variable Soil Slopes Using Conditional Simulation." In Geo-Risk 2017. Reston, VA: American Society of Civil Engineers, 2017. http://dx.doi.org/10.1061/9780784480700.038.

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Cretu, Ovidiu S. "Cost and Schedule Estimate: Risk Analysis for Transportation Infrastructures." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12804.

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This paper presents the Risk-Based Estimate Self-Modeling (RBES) which eliminates the need of specialized software by allowing a regular excel user to develop an integrated cost and schedule estimate with limited knowledge of risk analysis. The RBES has the flexibility of entering: • The baseline project cost and schedule along with the different degrees of uncertainty associated with them. • Simple independent risks with their cost and duration components. • Complex risks which are dependent of each other and/or correlated. The RBES integrates project cost and schedule when the project delivery goes as planned with up to 24 risks events. Each risk may have two components (cost, and schedule) and the RBES may compute the effect of risk dependency and risk correlation with respect to project cost and schedule. The outcome of the RBES consists of graphs and tables that present the project cost in current year dollars, and year of expenditure dollars plus the advertisement date, and the end construction date. Additionally, the RBES generates the following summary information: A tornado diagram of the most significant cost and schedule risks, A risk matrix for each of the 24 risks; and a risk map in order to help the management to quickly assess all project risks so that they may initiate the appropriate risk response actions.
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Smith, Alfred, and Shu-Ping Hu. "Cost Risk Analysis "Made Simple"." In Space 2004 Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-6070.

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De Nocker, L., S. Broekx, I. Liekens, D. Bulckaen, S. Smets, J. Gauderis, and W. Dauwe. "Cost-benefit analysis to select the optimal flood protection strategy along the Scheldt." In RISK ANALYSIS 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/risk060261.

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Reports on the topic "Cost of risk"

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Kujawski, Edouard, and Diana Angelis. Dynamic Cost Risk Assessment for Controlling the Cost of Naval Vessels. Fort Belvoir, VA: Defense Technical Information Center, August 2009. http://dx.doi.org/10.21236/ada512312.

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Fan, Victoria, Dean Jamison, and Lawrence Summers. The Inclusive Cost of Pandemic Influenza Risk. Cambridge, MA: National Bureau of Economic Research, March 2016. http://dx.doi.org/10.3386/w22137.

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Minsker, Barbara S. Cost-Effective Risk Management of Groundwater Contamination. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada393312.

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Almeida, Heitor, and Thomas Philippon. The Risk-Adjusted Cost of Financial Distress. Cambridge, MA: National Bureau of Economic Research, October 2005. http://dx.doi.org/10.3386/w11685.

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B. Mar. Quantifying Cost Risk Early in the Life Cycle. Office of Scientific and Technical Information (OSTI), November 2004. http://dx.doi.org/10.2172/840131.

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Jagannathan, Ravi, and Yong Wang. Consumption Risk and the Cost of Equity Capital. Cambridge, MA: National Bureau of Economic Research, January 2005. http://dx.doi.org/10.3386/w11026.

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Chapman, Robert E., and Chi J. Leng. Cost-effective responses to terrorist risk in constructed facilities. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7073.

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Hooper, Seth T. Enhancing the Enhanced Scenario-Based Method of Cost-Risk Analysis. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada555669.

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Wake, Cameron, Matt Magnusson, Christine Foreman, and Fiona Wilson. Carsey Perspectives: New Hampshire's Electricity Future; Cost, Reliability, and Risk. University of New Hampshire Libraries, 2017. http://dx.doi.org/10.34051/p/2020.286.

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Kramer, Berber, Rob Rusconi, and Joseph W. Glauber. Five years of regional risk pooling: An updated cost-benefit analysis of the African risk capacity. Washington, DC: International Food Policy Research Institute, 2020. http://dx.doi.org/10.2499/p15738coll2.134046.

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