Different Levels Of Risk Aversion Research Articles

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Human decision-making in a non-ergodic additive environment

Ergodicity Economics presents an intriguing perspective on the validity of the ergodic hypothesis within economic models and the influence of ergodicity breaking on human decision-making processes. Prior research has illuminated the impact of ergodicity breaking within multiplicative settings. However, the implications of ergodicity breaking within additive dynamics, especially in situations carrying a ‘risk of ruin’ or a complete loss, remain largely unexplored. In our research, we introduce the concept of ‘risk of ruin’ into our decision-making model to examine the effects of non-ergodicity in additive dynamics. Our theoretical framework and experiments show that human decision-makers are sensitive to non-ergodicity within purely additive dynamics. This sensitivity manifests itself in significantly different levels of risk aversion depending on the distance and associated likelihood of ruin. These findings underscore the critical role of time averages in human decision-making, suggesting that humans are less irrational than conventionally assumed in behavioural models rooted in expected values. Drawing on evidence from Ergodicity Economics, incorporating non-ergodicity has the potential to illuminate common trends in decision-making within compounding systems, like multiplicative growth dynamics. Our research underscores a similar potential for understanding decision-making patterns within additive dynamics when the risk of ruin is present.

Risk aversion and flexibility options in electricity markets

Flexibility options, such as demand response, energy storage and interconnection, have the potential to reduce variation in electricity prices between different future scenarios, thereby reducing investment risk. Investing in flexibility options can also lower the need for generation capacity. However, there are complex interactions between different flexibility options. In this paper, we investigate the interactions between flexibility and investment risk in electricity markets. In particular, we focus on the investment strategies of risk-averse decision-makers. We employ a large-scale stochastic transmission and generation expansion model of the European electricity system, providing it with uncertain future scenarios based on ENTSOs’ Ten-Year Network Development Plan. Using this model, we first investigate the effect of risk aversion on investment decisions. We find that the interplay of parameters leads to (i) more investment in a less emission-intensive energy system if planners are risk averse (hedging against CO2 price uncertainty), (ii) constant total installed capacity, regardless of the level of risk aversion (i.e. planners do not hedge against demand and uncertainties when deploying renewable energy sources) and (iii) an allocation of investments that disregards cost differences for load shedding across Europe. Second, we examine the individual effects of three flexibility elements on optimal investment levels at different levels of risk aversion: demand response, investment in additional interconnection capacity and investment in additional energy storage. We show that flexible technologies have a higher value for risk-averse decision-makers, although the effects are nonlinear. Finally, we investigate the interactions between the flexibility elements. We find that risk-averse decision-makers show a strong preference for transmission-grid expansion once flexibility is available at a low cost.

Simulating risk management strategies for specialized farming systems: the potential impact of the EU income stabilization tool

PurposeThis study assesses the potential impact of the European Income Stabilization Tool (IST – a whole farm income risk management [RM] tool) within a farm cooperative specializing in vineyards and operating in a small area of production. The authors assess the conditions under which IST could improve the well-being of the associated farmers and, at the same time, improve financial sustainability. Financial aspects are of particular relevance since the characteristics of the cooperative cause the management of the tool to become potentially risky.Design/methodology/approachThe analysis relies on a balanced panel dataset to report the production and economic characteristics of individual associated farms. This is the basis for simulating the implementation of the IST as described in the current European regulation. The expected utility approach is then used to assess the potential impact on farmers' well-being under different levels of risk aversion and premiums. The analysis of the IST annual cash flow allows for an accurate assessment of its financial sustainability.FindingsThe results suggest that the IST can improve farmers' well-being under plausible levels of risk aversion and premiums, making most farmers willing to support its implementation. Furthermore, the tool could be financially sustainable even if implemented in a specialized and geographically concentrated group of farms. In addition, the results suggest that the use of strategies such as the IST could help cope with negative annual balances by treating the financial sustainability of the fund.Originality/valueThe analysis adds to previous research on the IST by accounting for farmers' risk aversion. Furthermore, it is the first analysis that simulates the implementation of this tool in a sector-specific and concentrated group of farms. The results provide useful evidence for those subjects planning to implement the IST in small and specialized farming systems.

Optimal Portfolios of National Currencies, Commodities and Fuel, Agricultural Commodities and Cryptocurrencies during the Russian-Ukrainian Conflict

This study sets out to explore the impacts of the Russian-Ukrainian conflict on worldwide financial markets by considering a large array of national currencies, precious metals and fuel, agricultural commodities and cryptocurrencies. Estimations span the period since the Russian invasion until the takeover of the Ukrainian city of Mariupol. Optimal portfolios are constructed for separate categories of financial assets for different levels of risk-aversion by investors. The Chinese yuan, gold, corn, soybeans, sugar and Bitcoin prove to be safe haven investments while the Japanese yen, natural gas, wheat and the combination of Bitcoin and Ethereum offer profit opportunities for risk-seekers. Notably, the agricultural commodities’ portfolio is the best performing while the cryptocurrency portfolio generates the worst risk-return trade-off. National currencies could act as safe havens in the place of gold when all types of assets can be combined. Natural gas is revealed to be the most reliable profit generator. Overall, high risk appetite does not result in large improvement in portfolios’ returns. This study sheds light on investors’ optimal decision-making during elevated geopolitical uncertainties and provides a compass for improving welfare.

Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market

Grain marketing is complex because important decisions are made on the timing of sales and the quantities sold at every trading activity. The literature suggest various grain-hedging strategies, however these strategies are not adaptable to changing market conditions or are difficult for a producer to implement. To address these limitations, our study developed tailor-made moving average crossover (MAC) strategies that are adaptable to changing market conditions and can be easily followed by risk neutral and risk averse grain producers. The study used daily closing prices for the white maize May futures contract for the period 2009/2010 to 2019/2020. An optimization model was solved using the evolutionary algorithm embedded in Excel® to identify the optimal MAC strategy that maximizes the margin above marketing cost for a risk aversion level. The results showed that optimal MAC strategies differ amongst producers with different levels of risk aversion. Furthermore, it was found that the risk-averse producers perform best by marketing their grain early in the marketing season. Meanwhile, the risk-neutral producers perform better by spreading their marketing activities throughout the season. The results further showed that the optimal MAC strategies performed better than the previously proposed routine strategies. The conclusion is therefore that an optimal MAC strategy outperforms routine strategies because of its ability to adapt to changing market conditions, while still being easy to implement.

A Behavior-Simulated Spherical Fuzzy Extension of the Integrated Multi-Criteria Decision-Making Approach

Since its inception in 1965, fuzzy sets have been developed for many years and are widely used in multi-criteria decision making (MCDM) problems. Recently, spherical fuzzy sets (SFS), one of the most recent fuzzy sets, have been applied to extend and reinforce MCDM methods. To contribute to this development, the aim of this study is to propose a novel SFS extension of the integrated MCDM method that takes into account the psychological behavior of decision makers. In the proposed approach, the evaluation criteria are first weighted by the spherical fuzzy Decision-Making Trial and Evaluation Laboratory (SF DEMATEL) method based on symmetrical linguistic comparison matrices. Another notable advantage of this process is determining the interrelationship between the evaluation criteria. In the next stage, the spherical fuzzy Interactive Multi-Criteria Decision-Making method in the Monte Carlo simulation environment (SF TODIM’MC) was applied to evaluate the alternatives. This method allows the process of evaluating alternatives to be performed continuously with different psychological behavioral parameters, which are considered as asymmetric information. As a result, the influence of the decision maker’s psychological behavior on the evaluation results is analyzed comprehensively. The robustness of the proposed approaches is verified through their application to prioritizing post-COVID-19 operational strategies in the Vietnam logistics sector. Numerical results have provided a cause-and-effect relationship between the negative effects of the pandemic and their weights. Furthermore, the results of prioritizing the operational strategies in the simulated environment provide rankings corresponding to different levels of risk aversion. Based on the results, the proposed spherical fuzzy approach is promising for expert-based decision-making problems under psycho-behavioral influence.

Handling Uncertainty in Cost-Effectiveness Analysis: Budget Impact and Risk Aversion

Methods to handle uncertainty in economic evaluation have gained much attention in the literature, and the cost-effectiveness acceptability curve (CEAC) is the most widely used method to summarise and present uncertainty associated with program costs and effects in cost-effectiveness analysis. Some researchers have emphasised the limitations of the CEAC for informing decision and policy makers, as the CEAC is insensitive to radial shifts of the joint distribution of incremental costs and effects in the North-East and South-West quadrants of the cost-effective plane (CEP). Furthermore, it has been pointed out that the CEAC does not incorporate risk-aversion in valuing uncertain costs and effects. In the present article, we show that the cost-effectiveness affordability curve (CEAFC) captures both dimensions of the joint distribution of incremental costs and effects on the CEP and is, therefore, sensitive to radial shifts of the joint distribution on the CEP. Furthermore, the CEAFC also informs about the budget impact of a new intervention, as it can be used to estimate the joint probability that an intervention is both affordable and cost-effective. Moreover, we show that the cost-effectiveness risk-aversion curve (CERAC) allows the analyst to incorporate different levels of risk-aversion into the analysis and can, therefore, be used to inform decision-makers who are risk-averse. We use data from a published cost-effectiveness model of palbociclib in addition to letrozole versus letrozole alone for the treatment of oestrogen-receptor positive, HER-2 negative, advanced breast cancer to demonstrate the differences between CEAC, CEAFC and CERAC, and show how these can jointly be used to inform decision and policy makers.

Sequencing mixed-model assembly lines with risk-averse stochastic mixed-integer programming

Sequencing decisions in mixed-model assembly lines are complicated by various uncertainty factors. This paper addresses a real-life uncertainty factor identified in a manufacturer of large vehicles, by modelling unreliable part delivery and quality. Stochastic optimisation is applied to find sequencing policies that improve the on-time performance of its mixed-model assembly lines. As schedulers have different levels of risk aversion, a risk-averse programme is further presented to protect against the decision maker’s chosen fraction of worst scenarios. Computational studies with Progressive Hedging as the solution method, and its lower bounding approach, demonstrate the high quality of resulting sequencing decisions and the time efficiency of the solution method.

Portfolio Diversification using Cryptocurrency

This paper aims to find the effectiveness of Cryptocurrency on well-formed portfolio with assets like Commodities, Exchange Traded Fund (ETFs), Stock assets and currency value of INR. There are several ways to determine the effectiveness in diversification. In this paper we use SOLVER, Modern Portfolio Theory and system of comparing Portfolio with FOREX & Commodity, Portfolio with Stock assets, Portfolio with ETFs, and lastly, combining all the asset classes in a portfolio with cryptocurrency. The portfolios optimized by maximizing the Portfolio return rate, as for other assets indicate a comparatively low performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness. In light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies.

Chance‐constrained optimal inflow control in hyperbolic supply systems with uncertain demand

SummaryIn this article, we address the task of setting up an optimal production plan taking into account an uncertain demand. The energy system is represented by a system of hyperbolic partial differential equations and the uncertain demand stream is captured by an Ornstein‐Uhlenbeck process. We determine the optimal inflow depending on the producer's risk preferences. The resulting output is intended to optimally match the stochastic demand for the given risk criteria. We use uncertainty quantification for an adaptation to different levels of risk aversion. More precisely, we use two types of chance constraints to formulate the requirement of demand satisfaction at a prescribed probability level. In a numerical analysis, we analyze the chance constrained optimization problem for the Telegrapher's equation and a real‐world coupled gas‐to‐power network.

Cross Hedging Effectiveness of Real Estate Securities Exchange Traded Funds

We investigate hedging effectiveness of four REIT Exchange Traded Funds in hedging seven real estate securities returns for the United States, Europe, and Asia through R-Squared, hedge ratio, variance reduction (VR) and utility maximization metrics. The analyses cover different full sample and sub-sample periods from 2000 to 2010 (inclusive) to document the performances of the hedge during periods of high and low volatility. The results show that the ETFs were very effective in hedging EREIT and all the sampled real estate securities in Europe and Asia. Second, VECM hedge marginally outperformed OLS hedge. Third, we find that hedging effectiveness evidenced by VR does not necessarily equate to economic viability. Therefore VR must be used together with UIs to determine viability of hedging effectiveness. Finally, given different levels of risk aversion vis-à-vis expected utility, it is advisable/inadvisable to hedge during high/low volatility periods.

Risk-averse optimization of crop inputs using a deep ensemble of convolutional neural networks

Machine Learning algorithms have emerged in precision agriculture as a promising approach for increasing productivity. However, the diffusion of this technology is still limited by the lack of clear applicability for crop input management and by the farmer’s perception of risk. In this work, we tackle both problems by incorporating uncertainty quantification into our previously proposed Convolutional Neural Network (CNN) model for yield prediction, and by proposing a risk-averse optimization algorithm on top of it. We redesign our CNN architecture under the Deep Ensemble framework, so the predictive model outputs a probability distribution instead of a single value. Then, a gradient-based optimization algorithm uses this model to find the maps of crop inputs that maximize the expected net revenue while satisfying risk constraints. We show that the new model not only provides uncertainty quantification but also increases the predicted performance of its former version. Experiments with the optimization algorithm show an increase up to 6.4% in the expected net revenue when compared with the status quo management, and provide a flexible setup to match different levels of risk aversion.

Effectivity and Cost Efficiency of a Tax on Nitrogen Fertilizer to Reduce GHG Emissions from Agriculture

The use of nitrogen (N) fertilizer substantially contributes to greenhouse gas (GHG) emissions due to N2O emissions from agricultural soils and energy-intensive fertilizer manufacturing. Thus, a reduction of mineral N fertilizer use can contribute to reduced GHG emissions. Fertilizer tax is a potential instrument to provide incentives to apply less fertilizer and contribute to the mitigation of GHG emissions. This study provides model results based on a production function analysis from field experiments in Brandenburg and Schleswig-Holstein, with respect to risk aversion by calculating certainty equivalents for different levels of risk aversion. The model results were used to identify effective and cost-efficient options considering farmers’ risk aversion to reduce N fertilizer, and to compare the potential and cost of GHG mitigation with different N fertilizer tax schemes. The results show that moderate N tax levels are effective in reducing N fertilizer levels, and thus, in curbing GHG emissions at costs below 100 €/t CO2eq for rye, barley and canola. However, in wheat production, N tax has limited effects on economically optimal N use due to the effects of N fertilizer on crop quality, which affect the sale prices of wheat. The findings indicate that the level of risk aversion does not have a consistent impact on the reduction of N fertilizer with a tax, even though the level of N fertilizer use is generally lower for risk-averse agents. The differences in N fertilizer response might have an impact on the relative advantage of different crops, which should be taken into account for an effective implementation of a tax on N fertilizer.

Optimization of special cryptocurrency portfolios

PurposeThis paper aims to elaborate on the optimization of two particular cryptocurrency portfolios in a mean-variance framework. In general, cryptocurrencies can be classified to as coins and tokens where the first can be thought of as a medium of exchange and the latter accounts for security or utility tokens depending upon its design.Design/methodology/approachAgainst this backdrop, this empirical study distinguishes, in particular, between pure coin and token portfolios. Both portfolios are optimized by maximizing the Sharpe ratio and, subsequently, compared with alternative portfolio strategies.FindingsThe empirical findings demonstrate that the maximum utility portfolio of coins, with a risk aversion of λ = 10, outweighs alternative frameworks. The portfolios optimized by maximizing the Sharpe ratio for both coins and tokens indicate a rather poor performance. Testing the maximized utility for different levels of risk aversion confirms the findings of this empirical study and confers them more robustness.Research limitations/implicationsFurther investigation is strongly recommended as tokens represent a new phenomenon in the cryptocurrency universe, for which only a limited amount of data are available, which restricts the sampling. Furthermore, future study is to include more sophisticated optimization models using different constraints in portfolio creation.Practical implicationsIn light of the persistently substantial volatility in cryptocurrency markets, the empirical findings assert that portfolio managers are advised to construct a global minimum variance portfolio. In the absence of sophisticated optimization models, private investors can invest according to the market values of cryptocurrencies. Despite minor differences in the risk and reward ratios of the portfolios tested, tokens tend to be more speculative, especially, if the Tether token is excluded, which may require enhanced supervision and investor protection by regulating authorities.Originality/valueAs the current literature investigates on diversification effects of blended cryptocurrency portfolios rather than making an explicit distinction, this paper reflects one of the first to explore the investability and role of diversifying coins and tokens using a classic Markowitz approach.

Selection Determinants for Private Equity Investments

This study examines a global universe of public companies and identifies some properties of companies to become takeover targets. The analysis shows that low headcount-related ratios, low advanced performance ratios, and low leverage ratios are most likely to be associated with becoming a takeover candidate for private equity investors. Further, grouping private equity investors into different levels of risk aversion leads to an even more granular description of potential selection determinants of specific types of private equity investors. TOPICS:Wealth management, private equity Key Findings • The key properties of public companies to become takeover targets by PE investors are found to be low leverage, low personnel-related profitability, low cash ratio, low return on equity and return on assets, and the potential to improve the existing inefficiencies. • Long-term-oriented PE investors look for companies with a strong growth potential and a high asset base. • Risk-seeking PE investors focus on factors like return on equity and return on assets, but also look for a high trading turnover of the target company’s stock.

Beklentile Dayalı Riske Maruz Değer Kriterli Gazete Satıcısı Modeli

In this paper, we use Expectile-based Value at Risk (EVaR) measure as an objective function to determine newsvendor solutions with risk considerations. When the newsvendor makes a decision with risk-driven behavior, the optimal order quantity deviates from the classical expected profit-maximizing quantity. Our EVaR minimization model extends the classical newsvendor model through a one-parameter risk measure and facilitates trade-off analysis between the capital to be held in reserve and the expected profit. To illustrate the proposed model, we present optimal solutions based on different levels of risk aversion and risk-taking for three different demand distributions.

Are There Different Evidence Thresholds for Genomic Versus Clinical Precision Medicine? A Value of Information-Based Framework Applied to Antiplatelet Drug Therapy

BackgroundThe threshold of sufficient evidence for adoption of clinically- and genomically-guided precision medicine (PM) has been unclear. ObjectiveTo evaluate evidence thresholds for clinically guided PM versus genomically guided PM. MethodsWe develop an “evidence threshold criterion” (ETC), which is the time-weighted difference between expected value of perfect information and incremental net health benefit minus the cost of research, and use it as a measure of evidence threshold that is proportional to the upper bound of disutility to a risk-averse decision maker for adopting a new intervention under decision uncertainty. A larger (more negative) ETC value indicates that only decision makers with low risk aversion would adopt new intervention. We evaluated the ETC plus cost of research (ETCc), assuming the same cost of research for both interventions, over time for a pharmacogenomic (PGx) testing intervention and avoidance of a drug-drug interaction (aDDI) intervention for acute coronary syndrome patients indicated for antiplatelet therapy. We then examined how the ETC may explain incongruous decision making across different national decision-making bodies. ResultsThe ETCc for PGx increased over time, whereas the ETCc for aDDI decreased to a negative value over time, indicating that decision makers with even low risk aversion will have doubts in adopting PGx, whereas decision makers who are highly risk-averse will continue to have doubts about adopting aDDI. National recommendation bodies appear to be consistent over time within their own decision making, but had different levels of risk aversion. ConclusionThe ETC may be a useful metric for assessing policy makers' risk preferences and, in particular, understanding differences in policy recommendations for genomic versus clinical PM.

Macro Asset Allocation with Social Impact Investments

Using a unique dataset of 50 listed companies that meet the majority of the OECD requirements for social impact investments, we construct a social impact finance stock index and investigate how investing in social impact firms can contribute to portfolio risk-return performance. We build portfolios with three different methodologies (naïve, Markowitz mean-variance optimization, GARCH-copula model), and we study the performance in terms of returns, Sharpe ratio, utility, and forecast premium based on a constant relative risk aversion function for investors with different levels of risk aversion. Consistent with the idea that social impact investment can improve portfolio risk-return performance, the results of our macro asset allocation analysis show the importance of a large fraction of investor portfolios’ stake committed to social impact investments.

Risk assessment in poplar plantations: a case study from northern Iran

Linear risk programming was used to determine the optimum cultivation pattern to increase incomes of poplar farmers. Seven clones of Populus deltoides Bartr. ex Marsh. were examined in Guilan province, northern Iran. Growth and price data were taken from previous research at the Safrabaste Poplar Research Station and in interviews with farmers. The Lingo software was used to analyze the data in different forms of E. The results show that when risk was considered, the optimal solution included the clones Pd63/51-x1, -Pd72/51-x3, Pd73/51-x4 and Pd79/51-x6. There was a high growth fluctuations of the clones Pd69/55-x2, Pd77. 51-x5, and Pd caroliniensis-x7 and were not included in cultivation plans. Furthermore, the existing farm plans executed by local farmers, is neither profit-oriented nor efficient in terms of income risk management according to risk efficient frontier. These results could help farmers with different levels of risk-aversion to select proper planting plans.