10 - Dynamic risk analysis and risk model evaluation

This study examines the impact of organizational structure and board composition on risk taking in the U.S. property casualty insurance industry, addressing different risk‐taking behaviors from different perspectives. The risk‐taking measures include total risk, underwriting risk, investment risk, and leverage risk. The evidence shows that mutual insurers have lower total risk, underwriting risk, and investment risk than stock insurers. In terms of board composition variables, we find that some board composition variables not only have impact on risk‐taking behaviors but also affect different risk measures differently. Thus, using different risk measures is better than using one risk measure to assess risk‐taking behavior. Finally, we conclude that an insurer can control its total risk through management of underwriting, investment, and leverage risks that determine an insurer's risk profile.

This article briefly reviews the theory of decision making under uncertainty and the programming methods used to consider uncertainty in farm planning. A multiperiod MOTAD model is developed to analyze the risk and return of a farm's investment financing, production, and marketing plans. The results of an empirical model of a beginning farmer in the Corn Belt are briefly presented to illustrate the potential applicability of the multiperiod MOTAD model and demonstrate the type of information generated. Most farm management decisions are made under conditions of uncertainty. This uncertainty in agriculture arises from market forces, weather, disease, insect damage, and other factors that cannot be controlled or accurately predicted. However, many agricultural economic models used to study farm management problems are specified under assumed certainty. For example, conventional linear programming used extensively in farm planning analyses does not accurately accommodate uncertainty. Of course, assumed yields and prices can be changed in these models to see how the farm organization is affected by changes in these variables. But there is no measurement of the risk associated with each farm organization. As a result, the conventional linear programming solutions for farm organization have often been rejected because, these solutions may specify actions that lead to a higher degree of risk than many farm managers are willing to accept [3, 5]. The objective of this article is to present a multiperiod linear programming model that can be used to evaluate farm management decisions under uncertainty. The discussion will begin with a review of the concepts of decision making under uncertainty. Then numerical analysis techniques will be discussed with emphasis on dynamic risk models. Finally, recent numerical results obtained from the use of a multiperiod MOTAD (Minimization of Total Absolute Deviations) model will be presented to illustrate its potential in dynamic risk analysis. DECISION MAKING UNDER UNCERTAINTY We assume that the objective of the rational individual is to maximize utility. Utility is derived from present and future consumption. Consumption, in turn, is a function of income. Utility can then be expressed as a function of income as:

Investors having an understanding of investment statistics are important. Especially quantitative tools related to investment risk measurement. Value-at-Risk Adjusted is one of the investment risk measurement tools, which assumes that returns are not normally distributed.This paper intends to measure investment risk based onValue-at-Risk Adjustedor called Modified Value-at-Risk under the Capital Asset Pricing Model. It is assumed that the return of the market index has a non-constant average and there is a long memory effect. The average of the return of the market index is estimated using ARFIMA models.It is also assumed that the stock risk premium correlates with market risk premiums, and stock risk premiums some time before. The correlation will be analyzed using the ARMAX-GARCH model approach. The Modified Value-at-Risk was then formulated based on the Capital asset Pricing Model with the ARMAX-GARCH model approach.To measure the performance of Modified Value-at-Risk that has been formulated is done with back testing. Back testing is carried out based on the Lopez II method. As a case study, analyzed some data on 10 stocks traded on the capital market in Indonesia.The results of the analysis show that the market index return risk premium significantly follows the ARFIMA model, and the 10 share risk premium significantly follows the ARMAX-GARCH model. Based on the results of back testing calculations indicate that the Value-at-Risk Adjustedor Modified Value-at-Risk is very suitable to be used to measure investment risk in the 10 stocks analyzed.

Simple SummaryThe aim of this study was to perform an external validation in a U.S. screening cohort of a mammography-derived AI risk model that was originally developed in a European study setting. The AI risk model was designed to predict short-term breast cancer risk toward identifying women who could benefit from supplemental screening and/or a shorter screening interval due to their high risk of breast cancer. The AI risk model showed a discriminatory performance of AUC 0.68, comparable to previously reported European validation results (AUC = 0.73). The discriminatory performance of the AI risk model was non-significantly different by race (AUC for White women = 0.67 and for Black women = 0.70), p = 0.20. In relation to a clinically used lifestyle–family-based risk model, the AI risk model showed a significantly higher discriminatory performance (AUCs 0.68 vs. 0.55, p < 0.01).Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64–0.72) for all women, 0.67 (0.61–0.72) for White women, and 0.70 (0.65–0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

Many applicationers derive the variance-covariance matrix (VCM) for meanvariance optimization from some risk model or apply a simple historical estimate. A common problem to these approaches is the stability of the variancecovariance matrix. In turbulent market phases risk estimates from various risk models are well known to be unreliable. One reason for their poor risk forecasting ability is the fact that financial markets are subject to substantial structural change, applied risk models do not account for. In our paper we account for structural changes by deriving VCMs from time-varying estimates of the single factor model, i.e., the market model. We demonstrate the advantages of this approach with respect to risk estimation, portfolio selection and investment performance by means of simulated trading strategies. The problem of choosing the adequate risk model has come in mind of scientific researchers and practioners only recently. While research has focused on forecasting returns for a long time there is a lack of evidence in evaluating the performance of different risk models and the consequences for portfolio optimization. Next to the well known sensitivity of the mean-variance optimization with respect to assumed expected returns the benefits promised by this approach also heavily depend on the accuracy in estimating the VCM (see, for example, [1] and [4]). Given the well known difficulty of estimating expected returns the most important improvement on MV optimization can be made in the VCM estimation which is mainly based on financial econometrics. However, on the performance of alternative risk models and optimization procedures there is only limited scientific evidence, such as [3, 9, 10, 13, 18] among others. The capital asset pricing model (CAPM) due to [17] and [14] assumes stock returns to be a linear function of a single factor, namely the market return. Stock betas, i.e., stock return elasticities with respect to the market

Subject. The article is devoted to economic risk assessment. Objectives. The aim is to increase reliability and accuracy of economic risk quantification. Methods. I developed an economic and mathematical model of economic risk. To examine it, I used the apparatus of the spectral theory of random processes. Results. The spectral analysis enabled to replace the examination of the statistical model of economic risk by the study of its image with Fourier integral transform. As a result, I obtained a spectral risk indicator without disadvantages inherent in most traditional risk measures. The spectral approach helped measure risk in real time, which is necessary when organizing the risk management process. Verification of the spectral approach to risk assessment is considered on the case of innovative project. Conclusions. The study offers a solution to existing problems related to economic risk quantification, and presents a developed economic and mathematical model of economic risk. It proposes a spectral risk measure that takes into account the nature of the behavior of an economic variable, its possible deviation from expected value, and the uncertainty associated with the deviation. The paper also presents a unique computational program to calculate risk. The spectral risk analysis is of theoretical and practical value, as it enables to expand knowledge about risk, provides a theoretical underpinning of risk assessment, helps identify and eliminate the shortcomings inherent in known statistical measures.

The paper proposes an innovative system of supervision and data acquisition, oriented to fire safety through a dynamic risk analysis, called SCADRA (Supervisory Control and Dynamic Risk Analysis). SCADRA achieves operation and management benefits for safety, functionality and energy optimization, by means of dynamic control and monitoring measures. A “Smart Tunnel” can be defined as a model where technical systems are designed and installed in an advanced way, by using digital components and sensors. SCADRA has been developed to offer a complete tool (human machine interface HMI) for real time monitoring of tunnel safety by tunnel operators, integrated with the innovative dynamic risk analysis. SCADRA offers compensatory and/or supplementary safety measures for emergency management and supports fire rescue teams that are operating in tunnel in which some requirements couldn't be realized.

Modern NCDR and ACTION risk models outperform the GRACE model for prediction of in-hospital mortality in acute coronary syndrome in a German cohort

These days we are witnessing a deep change in the characteristics of the type of energy that our economies are supplied with. A clear trend is that sustainable and green energies are decisively replacing traditional fossil fuel-based sources of energy. For various reasons, this fundamental change implies an increasing risk in investments on portfolios heavily based on traditional energy industries. What is less known, is that these industries have returns that show a very low correlation with sustainable fossil fuel-free stock portfolios making them an appealing tool for portfolio managers to design properly diversified investments. In this study we examine this and related phenomena proposing statistical methods to implement the expected shortfall (ES), the challenging risk measure recently adopted by the financial regulator. We obtain evidence that a newly proposed backtesting procedure for the ES based on multinomial tests is an adequate and simple method to validate these risk measures when applied to a highly volatile stock index. Backtesting results of the ES show that flexible heavy-tailed distribution α–stable performs well for modelling the loss distribution. These results are even improved when the variances of fossil fuel price returns are included as external regressors in the GARCH model variance equation. In this case, the ES computed from the four considered loss distributions perform properly.

In this updated empirical analysis, investment risk models with infinite variance are more descriptive of distributions of individual property returns in the NCREIF database over the period 1980 to 2003 than Normally distributed risk models. Real estate investment risk is heteroskedastic, but the Characteristic Exponent of the investment risk function is nearly constant across time although differences among property types are evident. Accordingly, asset diversification is far less effective at reducing the impact of non-systematic investment risk on real estate portfolios than in the case of assets with Normally distributed investment risk. The patterns found in the U.S. are the same in Australia and the United Kingdom, and the Characteristic Exponents are virtually identical across all three countries.

Volatility as a measure of investment risk is widely accepted by academic researchers and industry professionals and has become ubiquitous in investment analysis. Furthermore, it is among the few financial variables that exhibit predictable time variation. Hence, there is an extensive amount of literature describing volatility models and assessing their forecasting power. This article provides a discussion of the prominent models and compares them in a unified notation framework. The empirical analysis shows that it is hard to outperform even simple trailing variance–type models. Autoregressive conditional heteroskedasticity (ARCH), generalized ARCH (GARCH), implied volatility, asymmetric, and seasonal models hardly improve forecasts despite added complexity. In this study, only momentum-based and intraday data–based models improved predictive accuracy significantly.

Dynamic risk analysis of oil depot storage tank failure using a fuzzy Bayesian network model

Risk measures and insurance premium principles

Tail risk of hedge funds: an extreme value application

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.