Risk-averse Model Research Articles

Investment Portfolio Optimization in Renewable Energy Stocks in Indonesia Using Mean-Variance Risk Aversion Model

Climate change is a phenomenon that has been occurring for quite some time. However, the increasingly felt impacts of climate change necessitate human action to mitigate these effects. One way to address this issue is by transitioning from conventional or non-renewable energy sources to renewable energy. This step undoubtedly has implications for various aspects, such as investments. Naturally, investors are beginning to turn their attention to the field of renewable energy as a new target. Investments are inherently associated with risks and returns One approach to maximizing returns is through portfolio optimization. One well-known method in portfolio optimization is the Mean-Variance method, also known as the Markowitz method, as it was first introduced by Harry Markowitz. In this research, an optimal portfolio is generated with weights of 0.1470 for ADRO; 0.1939 for MEDC; 0.2143 for ITMG and 0.4449 for RAJA. With this composition of optimal portfolio weights, the expected return is obtained at 0.002252, and the return variance is 0.000496.

Green financing strategies under risk aversion and manufacturer competition

In order to address the financial constraints of enterprises to promote green R&D and industrial green transformation, this study investigated a competitive supply chain consisting of a retailers, a general manufacturer, and a capital-constrained green manufacturer under risk aversion and capital shortage. It established models with and without capital constraints, retailer financing, and bank financing for the green manufacturer and retailer under risk aversion, and explored how the green competitive supply chain could obtain optimal financing strategies. The research findings are as follows: (1) When the financing interest rates are equal, the green manufacturer should prefer the retailer financing model, and regardless of the financing method, the increase in interest rates is extremely detrimental to retailers. (2) The increase in the degree of risk aversion of the green manufacturer is not conducive to the long-term development of competitors and itself, but is beneficial to the retailer, but it will cause further instability in the market. However, the increase in the degree of risk aversion of the retailer is only detrimental to itself and beneficial to the supply chain partners.

Supplier selection and order allocation problems considering regional and supplier disruptions with a risk-averse strategy

This paper studies optimal supplier selection and order allocation problems considering regional and supplier disruptions. We suggest a pricing policy that takes into account disruption probability, which reflects the trade-off relationship between cost and risk. We present a risk-neutral model considering the expected cost and a risk-averse model considering the conditional value-at-risk (CVaR) measure. We also present a weighted CVaR model that considers various tolerance levels simultaneously. Several multi-objective models that consider both risk-averse and risk-neutral models are proposed. We develop methodologies to solve multi-objective models by applying a convex combination or a modified augmented Tchebycheff distance. Finally, we show the performance of solution approaches through numerical experiments and present the supplier dependency ratio, which can construct an appropriate portfolio. We offer a Pareto frontier as the result of the multi-objective model and derive managerial insights, suggesting a decision-making criterion between disruption risk and the expected cost.

Relations between risk-averse models in extended two-stage stochastic optimization

We consider an extended two-stage risk-averse stochastic optimization problems in several formulations. Risk-aversion is reflected by risk constraints in form of stochastic-order relations, which are imposed in a time-consistent manner. The problem is analyzed under convexity assumptions. The main goal of this study is to establishing relations between the extended two-stage problem with stochastic-order constraints on the recourse function on the one hand and the two-stage problems with alternative models of risk such as utility functions, distortions, or coherent measures of risk on the other hand.

Long-Term Hydropower Planning for Ethiopia: A Rolling Horizon Stochastic Programming Approach with Uncertain Inflow

All long-term hydropower planning problems require a forecast of the inflow during the planning period. However, it is challenging to accurately forecast inflows for a year or more. Therefore, it is common to use stochastic models considering the uncertainties of the inflow. This paper compares deterministic and stochastic models in a weekly rolling horizon framework considering inflow uncertainty. The stochastic model is tested in both a risk-neutral and a risk-averse version. The rolling horizon framework helps make periodic decisions and update the information in each rolling week, which minimizes the errors in prolonged forecasts. The models aim to utilize the water stored in the rainy season throughout the year with minimum load shedding while storing as much water as possible at the end of the planning horizon. The Conditional Value at Risk (CVaR) risk measure is used to develop the risk-averse stochastic model. Three different risk measures are investigated to choose the risk measure that yields the best outcome in the risk-averse problem, and the two best measures are compared to a deterministic and risk-neutral model in a weekly rolling horizon framework. The results show that the risk-neutral and best risk-averse models perform almost equally and are better than the deterministic model. Hence, using a stochastic model would be an improvement to the actual planning performed in the Ethiopian and other African countries’ power systems.

Addressing insurance price discrimination in an era of diversity, equity, and inclusion

AbstractThe early 2020s diversity, equity, and inclusion movement has prompted debate about banning the use of suspect insurance pricing variables because they discriminate against protected classes, such as gender. This paper demonstrates how banning an insurance pricing variable currently used in insurance pricing models can result in regulatory adverse selection if the ban heterogeneously combines policyowners with different expected losses into the same risk class, contrary to risk‐based pricing. The paper begins by describing several recent regulatory and judicial decisions to ban insurance pricing variables. It next describes the process used by insurers to set insurance prices, followed by a discussion of applicable insurance discrimination laws. Using a simple risk aversion model, the paper next examines whether a ban on gender‐based auto insurance pricing in California in 2019 results in regulatory adverse selection. The paper concludes by describing possible alternative pricing variables available to auto insurers if gender‐based pricing is banned.

A cooperation approach for nexus among biofuel, compost, and water in waste supply chain under risk aversion: A case study

Waste mismanagement has caused environmental pollution, while there is an opportunity to reduce this environmental degradation by converting waste into value-added products. These value-added products from waste have mutual effects from the perspective of water-energy-waste-carbon nexus and a cooperation approach could lead to better management. We develop a Stackelberg game model, where the municipality as a leader intervenes in the waste supply chain with penalties and subsidies to control environmental pollution, water consumption, and energy consumption. The collector collects waste from two channels municipal waste bins and waste purchasing. Buyable waste and recyclable waste from bins are sold to applicants. The collector sells the rest of the bins' waste to electricity and biofuel production (EB) plant and compost plant. A risk-aversion model has been considered for the EB due to the uncertainty of the amount of recyclable waste, and the model has been verified based on Tehran City.

Robust Wireless Rechargeable Sensor Networks

Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.

Demand Volatility and Firm Export Margins: Evidence from Egypt

The study explains the export behavior of Egyptian firms under demand volatility in destination countries using detailed customs data and high-dimensional fixed effects. It finds that demand volatility negatively affects both the intensive and extensive export margins. The effects are particularly evident for large firms which reduce their export sales — especially over time — to more volatile destinations/products, are more likely to exit from exporting more volatile products and are less (more) likely to enter (exit) more volatile destinations. These findings confirm recent literature that emphasizes the greater elasticity of large firms to foreign demand shocks. They are also in line with risk aversion models in which the average risk premium increases with firm size. Given the disproportionate adverse impacts on large exporters, we find that higher demand volatility leads to lower aggregate exports, especially to geographically close and low trade costs countries. Accordingly, uncertainty in demand lessens the positive effect of lower trade barriers on exports.

Are upper-secondary track decisions risky? Evidence from Sweden on the assumptions of risk-aversion models

Relative risk aversion (RRA) models explain social class inequalities in education with risk avoidance, i.e., the risky choice assumption (RCA). This assumption concerns risks related to more ambitious educational choices and has been subject to little explicit scrutiny. In this paper, we test whether or not vocational education is a safety net that protects from labor market marginalization. We present an empirical assessment of upper-secondary track choices in Sweden, contrasting the vocational and the academic tracks for those not pursuing tertiary educational degrees. We use Swedish administrative data for all siblings born 1972–1980 and fit sibling fixed effects models netting out unobserved time-constant confounders. The only evidence in favor of the RCA is that when considering selection, graduates of the academic track without a tertiary degree initially face higher risks of not being stably employed and registered as unemployed in their early 20s than their counterparts from vocational education. However, the academic tracks significantly protect men from the threat of entering unskilled routine occupations. We conclude that the support for the RCA is scant at best.

An Uncertain Mean-AVaR Portfolio Selection via an Artificial Neural Network Scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based mean-average value at Risk (AVaR) in uncertain environment. The portfolio optimization problem is designed in two cases: risk-taker and risk-averse models. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.

Two-region Perimeter Control Based on Risk-averse Model Predictive Control

The perimeter control is effective in alleviating traffic congestion. However, previous studies about perimeter control based on macroscopic fundamental diagram didn't consider the influences of the system uncertainty even though this uncertainty may lead to significant congestion. This paper presents a two-region perimeter control method based on a risk-averse model predictive control considering uncertainty. The advantage of this paper is that we design a risk index for uncertainty and design a controller which takes the risk index as an optimization objective. A scenario tree is used to model the uncertainty of the two-region dynamic equations. Average value at risk mapping is employed to calculate the systematic risk due to uncertainty. Then, the optimization objective of a multi-stage risk is used in MPC based on the scenario tree. Finally, the multi-stage risk is formulated as a solvable form. Simulation results show that the proposed perimeter control method reduces the total travel time of the two-region road network with uncertainty and significantly reduces traffic congestion compared to stochastic model predictive control method.

A study of emergency medical service system based on a two-stage risk-averse model

An efficient EMS system provides out-of-hospital emergency medical services for sick and injured patients and maximizes the ability to respond quickly to calls, transfer, provide timely treatment and save lives in the event of an emergency. In this paper, an EMS system consisting of multiple EMS stations and demand sites is studied. Rescue supplies such as ambulances are stored at each EMS site to meet the uncertain demand from the demand location. The planning and allocation of emergency medical service sites and site storage ambulance resources under uncertainty of emergency needs is studied.

Mean-AVaR in credibilistic portfolio management via an artificial neural network scheme

ABSTRACT This paper focuses on the computation issue of portfolio optimisation with scenario-based mean-Average Value at Risk (AVaR) in credibilistic environment. The portfolio optimisation problem is designed in two cases: risk taker model and risk-averse model. The main idea is to replace the portfolio selection models with linear programming (LP) problems. Since the computing time required for solving LP greatly depends on the dimension and the structure of the problem, the conventional numerical methods are usually less effective in real-time applications. One promising approach to handle online applications is to employ recurrent neural networks based on circuit implementation. Hence, according to the convex optimisation theory and some concepts of ordinary differential equations, a neural network model for solving the LP problems related to portfolio selection problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with fuzzy returns. Some illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.

Single machine scheduling with release dates: A distributionally robust approach

In the present study, a single machine scheduling problem with job release dates is considered, where the job processing time is depicted by an ambiguity set, with the mean, the mean absolute deviation, and the support set information given. To the best of our knowledge, this is the first time that a distributionally robust optimization method is applied to the single machine scheduling with release dates. Based on the risk attitude of the decision maker, two distributionally robust optimization models are proposed, namely, the risk-neutral and risk-averse scheduling. The two models concern with the worst-case performance and the robust conditional value-at-risk with respect to the total flow time, respectively. Since the proposed robust optimization models are intractable in nature, we reformulate them as two separate mixed-integer linear programming models, which can then be solved via off-the-shelf solvers. Interestingly, it is proved that the mean absolute deviation can be ignored in the risk-neutral robust model. Thus a more succinct reformulation is constructed, which can be rapidly solved to optimum. Furthermore, extensive numerical experiments are performed with the stochastic programming model as a benchmark. And it is observed that the risk-averse model outperforms the others on both the average and worst-case metrics in most cases. Meanwhile, the risk-neutral model is more preferable on the average metric when the jobs are sparsely released.

Why does the Fed move markets so much? A model of monetary policy and time-varying risk aversion

We show that endogenous variation in risk aversion over the business cycle can jointly explain financial market responses to high-frequency monetary policy shocks with standard asset pricing moments. We newly integrate a work-horse New Keynesian model with countercyclical risk aversion via habit formation preferences. In the model, a surprise increase in the policy rate lowers consumption relative to habit, raising risk aversion. Endogenously time-varying risk aversion in the model is crucial to explain the large fall in the stock market, the cross-section of industry returns, and the increase in long-term bond yields in response to a surprise policy rate increase.

A risk-averse approach for joint contract selection and slot allocation in liner container shipping

Liner shipping companies need to satisfy the shipping demand from both the long-term contracts and the spot market. In general, the container shipping demand of the long-term contract has more stable volumes but lower freight rates compared with the spot market demand. It is thus a critical problem for the shipping companies how to provide discriminative shipping services to these two types of shipping demand through contract selection and container slot allocation to simultaneously increase the shipping profit and control its variations resulting from the demand variations. To handle this problem, a two-stage stochastic programming model is constructed in this paper which adopts the Conditional-Value-at-Risk (CVaR) to measure the profit variations. In the first stage, the shipping company selects the long-term contracts from a candidate contract set to sign with customers under the uncertainties in the show-up rates of containers in long-term contracts, and the volumes and the freight rates of the spot market demands. In the second stage, after all uncertainties have been realized, the shipping company determines the slot allocation for both the contract and spot demands. With the introduction of the CVaR, classical Benders decomposition cannot be directly applied to solve the risk-averse model. This paper thus develops a risk-averse Benders decomposition method tailored for the model. Numerical experiments are also conducted to verify the effectiveness of the solution method and provide some meaningful managerial insights.

Financial Risk Avoidance Based on the Sensor Network and Edge Computing

In order to improve the effect of financial risk aversion, this paper studies the financial risk aversion system combined with the edge computing method of the sensor network and proposes a sensor data anomaly detection algorithm based on the offset distance. Moreover, this paper divides the sensor data into several sliding windows according to the time series, analyzes the offset between the data object and other data in the sliding window by calculating the offset distance, and uses the abnormal level to indicate the possibility of data abnormality. In addition, this paper analyzes the single-layer linear network model for the data with high abnormal level and constructs a financial risk aversion model based on the edge computing of the sensor network. The simulation test results show that the financial risk aversion model based on sensor network and edge computing proposed in this paper meets the actual needs of financial risk analysis.

Integration of hydrogen storage system and solar panels in smart buildings

In the residential area, the cost of the electricity is at the maximum level during the peak demand periods. Regarding this, the peer-to-peer (P2P) energy trading strategy is a hopeful and practical solution to deal with this problem. Also, in the P2P home energy management system, the buildings have become more flexible by sharing their powers to meet their electricity demands. In this work, we have considered three buildings, in which two of them are equipped with solar panels, and in the third one, the hydrogen source is established. In the proposed residential zone, the considered buildings are connected in order to perform P2P transactions to reduce the energy billing. The operator of the system is wholly investigated under uncertain conditions based on the downside risk constraint (DRC) method in both risk-averse and risk-neutral models. With the help of a P2P energy trading strategy as well as renewable energy resources, we can provide electricity to customers in remote areas where it is not possible to supply electricity or is not economically beneficial. Based on the obtained results, the function of the proposed system has manifested in an acceptable manner. For instance, in the solar panel function in the second building, the amount of generated solar power is 30 kW in the risk-averse model, whereas, in the risk-neutral model, it is 18 kW. On the other hand, the level of consumed electricity by the electrolyzer system is generally lower in the risk-averse model than risk-neutral model in the second season.

Risk analysis of integrated biomass and concentrated solar system using downside risk constraint procedure

In the electric power industry, solar energy and bioenergy are two energy resources that produce near-zero emissions. In order to generate dispatchable electricity power and make increments in the role of the ever-growing renewable-dominated networks, the concentrated solar energy connected with biomass boiler and thermal storage unit could be used. In this regard, the model of day-ahead and intra-day dispatch is represented for an integrated biomass-concentrated solar (IBCS) system with the aim of maximizing the system profitability by a cooperative association of the elements of the system. Furthermore, to capture the impact of uncertainties on the system function, the investigation of the IBCS system is carried out based on a stochastic optimization procedure called the downside risk constraint (DRC) method, which is a scenario-based procedure. In this method, the function of the elements is explored thoroughly regarding the risk-averse and risk-neutral (under risky condition) strategies (risk is not considered). The simulation results demonstrated that the proposed model of the dispatch can achieve considerable profit for the decision-maker of the system. For instance, the obtained profit in the risk-averse model is 27074.5 $, while in the risk-neutral model, it is 31,347 $. This reveals that the system operator needs to increase %14 of its expenditure in the risk-averse strategy to increase the system's security.