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The purpose of this paper is to illustrate the importance of modeling parameter risk in premium risk, especially when data are scarce and a multi‐year projection horizon is considered. Internal risk models often integrate both process and parameter risks in modeling reserve risk, whereas parameter risk is typically omitted in premium risk, the modeling of which considers only process risk.

The authors present a variety of methods for modeling parameter risk (asymptotic normality, bootstrap, Bayesian) with different statistical properties. They then integrate these different modeling approaches in an internal risk model and compare their results with those from modeling approaches that measure only process risk in premium risk.

The authors show that parameter risk is substantial, especially when a multi‐year projection horizon is considered and when there is only limited historical data available for parameterization (as is often the case in practice). The authors' results also demonstrate that parameter risk substantially influences risk‐based capital and strategic management decisions, such as reinsurance.

The authors' findings emphasize that it is necessary to integrate parameter risk in risk modeling. Their findings are thus not only of interest to academics, but of high relevance to practitioners and regulators working toward appropriate risk modeling in an enterprise risk management and solvency context.

To the authors' knowledge, there are no model approaches or studies on parameter uncertainty for projection periods of not just one, but several, accident years; however, consideration of multiple years is crucial when thinking strategically about enterprise risk management.

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the recent regulatory tightening in the EU urges financial market participants to disclose sustainability-related financial risk, without providing any methodological guidance. The purpose of the study is the identification and explanation of the methodological limitations in the field of transitory risk modeling and the logic step to advance toward a stochastic approach.

The study reviews the literature on deterministic risk modeling of transitory risk exposure for real estate highlighting the heavy methodological limitations. Based on this, the necessity to model transitory risk stochastically is described. In order to illustrate the stochastic risk modeling of transitory risk, the empirical study uses a Markov Switching Generalized Autoregressive Conditional Heteroskedasticity model to quantify the carbon price risk exposure of real assets.

The authors find academic as well as regulatory urgency to model sustainability risk stochastically from a conceptual point of view. The own empirical results show the superior goodness of fit of the multiregime Markov Switching Generalized Autoregressive Conditional Heteroskedasticity in comparison to their single regime peer. Lastly, carbon price risk simulations show the increasing exposure across time.

The practical implication is the motivation of the stochastic modeling of sustainability-related risk factors for real assets to improve the quality of applied risk management for institutional investment managers.

The present study extends the existing literature on sustainability risk for real estate essentially by connecting the transitory risk management of real estate and stochastic risk modeling.

This paper seeks to present a conceptual modeling approach, which is new in the domain of information systems security risk assessment.

The approach is helpful for performing means‐end analysis, thereby uncovering the structural origin of security risks in information systems, and how the root‐causes of such risks can be controlled from the early stages of the projects.

Though some attempts have previously been made to model security risk assessment in information systems using conventional modeling techniques such as data flow diagrams and UML, the previous works have analyzed and modeled the same just by addressing “what” a process is like. However, they do not address “why” the process is the way it is.

The approach addresses the limitation of the existing security risk assessment models by exploring the strategic dependencies between the actors of a system and analyzing the motivations, intents and rationales behind the different entities and activities constituting the system.

This study aims to examine the theoretical foundations for multivariate portfolio optimization algorithms under illiquid market conditions. In this study, special emphasis is devoted to the application of a risk-engine, which is based on the contemporary concept of liquidity-adjusted value-at-risk (LVaR), to multivariate optimization of investment portfolios.

This paper examines the modeling parameters of LVaR technique under event market settings and discusses how to integrate asset liquidity risk into LVaR models. Finally, the authors discuss scenario optimization algorithms for the assessment of structured investment portfolios and present a detailed operational methodology for computer programming purposes and prospective research design with the backing of a graphical flowchart.

To that end, the portfolio/risk manager can specify different closeout horizons and dependence measures and calculate the necessary LVaR and resulting investable portfolios. In addition, portfolio managers can compare the return/risk ratio and asset allocation of obtained investable portfolios with different liquidation horizons in relation to the conventional Markowitz´s mean-variance approach.

The examined optimization algorithms and modeling techniques have important practical applications for portfolio management and risk assessment, and can have many uses within machine learning and artificial intelligence, expert systems and smart financial applications, financial technology (FinTech), and within big data environments. In addition, it provide key real-world implications for portfolio/risk managers, treasury directors, risk management executives, policymakers and financial regulators to comply with the requirements of Basel III best practices on liquidly risk.

The proposed optimization algorithms can aid in advancing portfolios selection and management in financial markets by assessing investable portfolios subject to meaningful operational and financial constraints. Furthermore, the robust risk-algorithms and portfolio optimization techniques can aid in solving some real-world dilemmas under stressed and adverse market conditions, such as the effect of liquidity when it dries up in financial and commodity markets, the impact of correlations factors when there is a switching in their signs and the integration of the influence of the nonlinear and non-normal distribution of assets’ returns in portfolio optimization and management.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Information sharing enhances the supply chain profitability significantly, but it may result in adverse impacts also (e.g. leakages of secret information to competitors, sharing of wrong information that result into losses). So, it is important to understand the various risk factors that lead to distortion in information sharing and results in negative consequences. Information risk identification and assessment in supply chain would help in choosing right mitigation strategies. The purpose of this paper is to identify various information risks that could impact a supply chain, and develop a conceptual framework to quantify them.

Bayesian belief network (BBN) modeling will be used to provide a framework for information risk analysis in a supply chain. Bayesian methodology provides the reasoning in causal relationship among various risk factors and incorporates both objective and subjective data.

This paper presents a causal relationship among various information risks in a supply chain. Three important risk factors, namely, information security, information leakages and reluctance toward information sharing showed influence on a company’s revenue.

Capability of Bayesian networks while modeling in uncertain conditions, provides a prefect platform for analyzing the risk factors. BBN provides a more robust method for studying the impact or predicting various risk factors.

The major contribution of this paper is to develop a quantitative model for information risks in supply chain. This model can be updated when a new data arrives.

The purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.

The method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.

The outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.

Although the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.

By using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.

The purpose of this paper is to address the limitations of conventional risk matrix based tools such that both positive and negative connotation of uncertainty could be captured within a unified framework that is capable of modeling the direction and strength of causal relationships across uncertainties and prioritizing project uncertainties as both threats and opportunities.

Theoretically grounded in the frameworks of Bayesian belief networks (BBNs) and interpretive structural modeling (ISM), this paper develops a structured process for assessing uncertainties in projects. The proposed process is demonstrated by a real application in the construction industry.

Project uncertainties must be prioritized on the basis of their network-wide propagation impact within a network setting of interacting threats and opportunities. Prioritization schemes neglecting interdependencies across project uncertainties might result in selecting sub-optimal strategies. Selection of strategies should focus on both identifying common cause uncertainty triggers and establishing the strength of interdependency between interconnected uncertainties.

This paper introduces a novel approach that integrates both facets of project uncertainties within a project uncertainty network so that decision makers can prioritize uncertainty factors considering the trade-off between threats and opportunities as well as their interactions. The ISM based development of the network structure helps in identifying common cause uncertainty triggers whereas the modeling of a BBN makes it possible to visualize the propagation impact of uncertainties within a network setting. Further, the proposed approach utilizes risk matrix data for project managers to be able to adopt this approach in practice. The proposed process can be used by practitioners while developing uncertainty management strategies, preparing risk management plans and formulating their contract strategy.

An acute need exists for a practical quantitative risk management‐based real estate investment underwriting methodology that clearly helps guide decision making and addresses the shortcomings of discounted cash flow (DCF) modeling by evaluating the full range of probable outcomes. This paper seeks to address this issue.

The simulation‐based excess return model (SERM) is an original methodology developed based on an application of Monte Carlo simulation to project risk assessment combined with the widely practiced DCF modeling. A case study is provided where results of the modeling are compared with traditional DCF risk models and with prior projects with known outcomes.

This paper lays out a practical method for stochastic quantitative risk management modeling for real estate development projects and illustrates that for identical projects risk‐adjusted returns derived with the use of SERM may differ significantly from returns provided by traditional discounted cash flow analysis. SERM corrects serious shortcomings in the DCF methodology by incorporating stochastic tools for the measurement of the universe of outcomes. It further serves to condense the results of Monte Carlo simulations into a simplified metric that can guide practitioners and which is easily communicational to decision makers for making project funding decisions.

SERM offers a simple, practical decision‐making method for underwriting projects that addresses the limitations of the prevailing methodologies via: stochastic assessment of the range of outcomes; interdependence of input variables; and objective risk premium metrics.

This paper presents an original methodology for making project‐funding decisions for real estate development projects that is based on Monte Carlo simulation combined with DCF analysis. The methodology presented here will have value for real estate developers, investors, project underwriters, and lenders looking for a practical and objective method for project valuation and risk management than is offered by traditional DCF analysis. A review of literature did not reveal analogous methodologies for risk management.