Level Of Risk Aversion Research Articles

On the Combination of Naive and Mean-Variance Portfolio Strategies

We study how to best combine the sample mean-variance portfolio with the naive equally weighted portfolio to optimize out-of-sample performance. We show that the seemingly natural convexity constraint—the two combination coefficients must sum to one—is undesirable because it severely constrains the allocation to the risk-free asset relative to the unconstrained portfolio combination. However, we demonstrate that relaxing the convexity constraint inflates estimation errors in combination coefficients, which we alleviate using a shrinkage estimator of the unconstrained combination scheme. Empirically, the constrained combination outperforms the unconstrained one in a range of generally small degrees of risk aversion, but severely deteriorates otherwise. In contrast, the shrinkage unconstrained combination enjoys the best of both strategies and performs consistently well for all levels of risk aversion.

Optimal financing strategies for a risk-averse supplier under the CVaR criterion in a capital-constrained supply chain

We focus on analyzing the risk preferences of suppliers regarding two types of financing: partial credit guarantee (PCG) and trade credit financing (TCF). Using the conditional value-at-risk (CVaR) criterion, we study each supply chain member’s equilibrium solution and optimal financing strategy under the assumption of demand distributions with an increasing failure rate. Initially, we present an analytical solution of the risk-averse supplier’s wholesale price, and prove that the optimal wholesale price decreases in the risk-averse level and initial capital, and increases in the credit guarantee ratio. Furthermore, we derive optimal financing strategies for both the supplier and the retailer under various circumstances. However, it is important to note that the results reveal a potential trade-off associated with PCG. While risk-averse suppliers may be more inclined to provide PCG compared to risk-neutral suppliers, this financing option can negatively impact the supplier’s utility and the expected profits of the retailers. Finally, we illustrate how the optimal financing strategies shift in response to changes in the risk-averse level and credit guarantee ratio, and present the win-win situations for the supplier and the retailer.

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.

Pricing and Profit Distribution in Supply Chain Through Option Contracts

Supply chain can be simplified into two parts: upstream suppliers and downstream distributors. The authors use option contract to coordinate their relationship. But the instability of pure option contract where supplier and distributor deal only by contract makes it difficult for both sides to reach a consensus. They overcome the defect by combining operation model with wholesale price model, and the mix model can reach Pareto improvement because it will increase supplier and distributor's profit at the same time. The distribution of the increasing profit will be influenced by many internal factors. Among these internal factors, the risk aversion and bargaining power can affect the profit distribution between supplier and distributor to a large extent. This paper establishes the mathematical model and chooses risk aversion and bargaining power to analyze. They found that 1) the higher the risk aversion level of the distributor or supplier is, the more its profit will be, and 2) the one with more initiative in the negotiation will reap more profits from the other side in supply chain.

Index policy for multiarmed bandit problem with dynamic risk measures

The multiarmed bandit problem (MAB) is a classic problem in which a finite amount of resources must be allocated among competing choices with the aim of identifying a policy that maximizes the expected total reward. MAB has a wide range of applications including clinical trials, portfolio design, tuning parameters, internet advertisement, auction mechanisms, adaptive routing in networks, and project management. The classical MAB makes the strong assumption that the decision maker is risk-neutral and indifferent to the variability of the outcome. However, in many real life applications, these assumptions are not met and decision makers are risk-averse. Motivated to resolve this, we study risk-averse control of the multiarmed bandit problem in regard to the concept of dynamic coherent risk measures to determine a policy with the best risk-adjusted total discounted return. In respect of this specific setting, we present a theoretical analysis based on Whittle’s retirement problem and propose a priority-index policy that reduces to the Gittins index when the level of risk-aversion converges to zero. We generalize the restart formulation of the Gittins index to effectively compute these risk-averse allocation indices. Numerical results exhibit the excellent performance of this heuristic approach for two well-known coherent risk measures of first-order mean-semideviation and mean-AVaR. Our experimental studies suggest that there is no guarantee that an index-based optimal policy exists for the risk-averse problem. Nonetheless, our risk-averse allocation indices can achieve optimal or near-optimal policies which in some instances are easier to interpret compared to the exact optimal policy.

Determinants of Net Interest Margin in Indonesian Banking Industry: The Moderating Role of Central Bank Interest Rate

This article is to examine the relationship between net interest margin (NIM) and its determinants: risk averse level, liquidity risk, and credit risk. In addition, the article aims to investigate the moderating role of central bank interest rate on the relationship. We observe 17 Indonesian banks, which are listed on Indonesia Stock Exchange in 2017 – 2021 period. We run panel data regression model with 380 firm-year total observations. The Hausman test suggests that we use Random Effect Model (REM) for the parameter estimation. Our study confirms that NIM is determined by credit risk, liquidity risk, and risk averse level. Other than that, we found that central bank interest rate moderates well the relationship of NIM and its determinants by which the dynamic of the relationship of net interest margin and its determinants can be explained. The presence of the central bank interest rate consistently explains the dynamics of the relationship between NIM, risk averse level, liquidity risk, and credit risk. Our model accommodates the positive/negative relationship of NIM and its determinants: credit risk, liquidity risk, and risk averse level. These findings are expected to shed light on the mixed and inconsistent findings of previous research. The ongoing impact of the central bank interest rate consistently elucidates the dynamics between NIM, risk aversion level, liquidity risk, and credit risk. These findings are expected to bring clarity to the mixed and inconclusive results observed in prior research.

Economic evaluation of dynamic hedging strategies using high-frequency data

This paper assesses the value of volatility timing when high-frequency data are available for measuring the realized hedged portfolio variance. We derive an analytical solution to estimate the performance fee for switching from static to dynamic generalized autoregressive conditional heteroskedasticity (GARCH) strategies. We find that the benefits depend on the realized hedged portfolio variances and the risk aversion level of the hedger. We use data from US equity indices to demonstrate that the switching can benefit hedgers, even after the transaction costs are accounted for. Hedgers with higher levels of risk aversion can benefit more from implementing the volatility-timing strategy.

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.

Bowley Insurance with Expected Utility Maximization of the Policyholders

This article studies the Bowley solution for a sequential game within the expected utility framework. We assume that the policyholders are expected utility maximizers and there exists a representative policyholder who faces a fixed loss with given probability and no loss otherwise. This policyholder selects the optimal indemnity function in response to the pricing kernel set by the insurer. Knowing the policyholder’s choice of indemnity function, the insurer adjusts the pricing kernel to maximize its expected net profit. This pricing kernel is of our central interest in this article, and in our setting the pricing kernel can be evaluated via the safety loading factor in an expected value premium principle. For a wide class of utility functions, we show that the optimal safety loading factor increases with respect to both the policyholder’s risk aversion level and the probability of zero loss. We also show that the insurance contract corresponding to the Bowley solution is Pareto dominated in the sense that both parties’ interests can be further improved, which shows the inefficiency of the Bowley solution. Some numerical examples are presented to illustrate the main results, and it is shown that both the policyholder and insurer can strictly benefit from the Bowley solution.

Gender and Risk Aversion: Evidence from a Natural Experiment

The theoretical literature on risk aversion and Expected Utility Theory is extensive; however, the analysis of this behaviour with natural experiments could be more comprehensive. In this paper, we use data from 120 episodes of the Portuguese version of the TV game show The Price is Right, namely from The Wheel game, to explore risk aversion as well as the impact of gender in decision-making. The Wheel game has straightforward rules and huge expected payoffs. All contestants have access to the same information and distributions of uncertainty, making it a unique field laboratory to conduct an experimental test of rational decision theory. The objective is to infer the risk aversion levels of decision-makers from their choice to turn the wheel and the influence of gender on risk attitudes. There is a widespread view that women are more risk-averse than men. However, we could not reject the hypothesis that women and men have the same level of risk aversion. Nevertheless, we have evidence that contestants are more risk-averse than risk-seeking. The omission bias, loss aversion and regret can explain that behaviour.

Prediction-based mean–variance portfolios with risk budgeting based on neural networks

The integration of return prediction and portfolio models has been widely investigated and proven effective. The derived prediction-based portfolios can be classified into five main categories. This paper comprehensively compares the out-of-sample performance of these categories based on the traditional mean–variance (MV) model for the first time, where different neural networks are used for return prediction. The MV model and equal-weighted portfolio are used as comparisons. Due to the high volatilities of these prediction-based portfolios, the risk budgeting method is applied to advance these portfolios, where stocks with higher predicted volatilities own higher proportions of portfolio risk contribution. The equal risk contribution is used as the benchmark. Further, the out-of-performance of these advanced portfolios is investigated after deducting transaction fees since their high turnovers can seriously affect the obtained profits in practical investments. Empirical tests are implemented with component stocks of the Shanghai Stock Exchange (SSE) 50 index, where daily data range from 2011 to 2021. Experimental results indicate that the prediction-based MV portfolio built with forecasted errors (MV-FE) is significantly superior to the others under different risk aversion levels, followed closely by the prediction-based MV portfolio built with forecasted returns (MV-FR). In particular, medium risk aversion is more suitable for these portfolios. Moreover, the risk budgeting method considerably reduces the volatilities of these portfolios and improves their risk-adjusted returns. The risk budgeting method is more efficient than the equal risk contribution method. After subtracting their transaction fees of turnovers, the MV-FR portfolio with risk budgeting, i.e., the MV-FR-RB portfolio, outperforms the others and still obtains satisfying performance. Also, there is little difference between the MV-FR and MV-FE portfolios with risk budgeting.

Mitigasi Resiko: Studi Kasus Keikutsertaan Petani dalam Program Asuransi Usahatani Padi

Sustainability of agricultural sector, especially within rice commodity which still becomes the main food for most of Indonesian’s, needs attention. Rice farming faces variety of risks and uncertainty conditions such as floods, droughts, and pest attacks. Global climate change in recent years has exacerbated the effects of natural hazards. Agricultural insurance become one solution to overcome farming risk. But in fact, farmers participation level still low. The purpose of this study is to identify the factors that influence farmers' decisions to participate in Rice Farming Insurance (AUTP) as a government-facilitated risk mitigation measure. The study was conducted in Wuluhan Sub-District, Jember Regency with total sample of 130 farmers who were randomly selected. Primary data was collected through interviews by filling out a structured questionnaire. The collected data was processed using logistic regression analysis to determine factors that significantly influence farmers' decisions. The results showed that the risk aversion level, age, experience, land area and crop failure occurence due to pests attack had significance wald test value (partial test) of 0.000; 0.098; 0.022; 0.002; and 0.024. So it can be said that the five variables significantly influence the farmers' decision to participate in AUTP at the confidence level of 95% and 90%.

Robust portfolio optimization: a stochastic evaluation of worst-case scenarios

This article presents a new approach for building robust portfolios based on stochastic efficiency analysis, by using the Chance Constrained Data Envelopment Analysis (CCDEA) model and periods of market downturn, i.e. worst-state market. The model is able to accommodate investors who exhibit different risk behaviors and the empirical analysis is done on assets traded on the Brazil Stock Exchange, B3 (Brasil, Bolsa, Balcão). The results confirm that the proposed model achieved portfolios that at the same time reduced systematic risk and maximized portfolio returns when working with worse market state data and higher levels of risk aversion. A higher level of risk aversion also led to better risk-return ratios, which can be seen in higher Sharpe ratio values.

Information sharing in the presence of retailer's risk aversion and altruism

AbstractIn highly uncertain times, firms are increasingly exhibiting risk aversion to uncertain demand and altruism to supply chain partners to reduce risk and maintain stability. To deal with high demand fluctuations, numerous firms are adopting information‐sharing strategies. We study how a retailer's risk aversion and altruism affect her demand information‐sharing decision by constructing game‐theoretic models. We first show that information sharing makes double marginalization (DM) stronger and hurts the retailer and generates an information‐sharing DM effect. The retailer's risk aversion strengthens this effect, while her altruism weakens the effect. Meanwhile, information sharing generates an uncertainty reduction effect on the risk‐averse retailer by reducing volatility and an altruism improvement effect on the altruistic retailer by increasing the manufacturer's profit. Whether information sharing benefits the retailer depends on her level of risk aversion and altruism. The retailer prefers voluntary sharing when both her risk aversion and altruism are high.

The generalist CEO pay premium and CEO risk aversion

AbstractWe investigate whether and how managerial risk aversion influences the structuring of the generalist pay premium. We use three candidate variables for managerial risk aversion: the CEO's equity portfolio vega, decision horizon, and wealth. We find that equity‐based pay differs between generalist CEOs (who have broader experience spanning several industries) and non‐generalist CEOs (who tend to have narrower, specialized industry‐specific experience) based on their risk appetite. Further, how the generalist pay premium is structured depends on the risk aversion measure. Because generalist CEOs tend to be better paid than their counterparts, understanding their risk aversion level will help companies improve the structure of these CEOs’ compensation packages, and ultimately those of all CEOs. When vega is the risk aversion measure, CEOs get compensated for bearing risk. However, generalist CEOs with high vegas suffer a “risk discount” in their compensation. These generalist CEOs are associated with lower cash, equity, and total pay. CEOs with longer decision horizons get more equity and less cash, but this structuring does not extend to the generalist CEOs. Finally, wealthier CEOs get more cash and total pay. However, generalist CEOs who are wealthier get less cash pay.

Optimization-based tail risk hedging of the S&P 500 index

In this paper, we present a mixed risk-return optimization framework for selecting long put option positions for hedging the tail risk of investments in the S&P 500 index. A tractable formulation is developed by constructing hypothetical portfolios that are constantly rolling put options. Variance and sample CVaR are used as risk measures. The models are tested against out-of-sample historical S&P 500 index values as well as the values of the index paired with long put options of varying strike prices. The optimized hedged portfolio could provide sufficient protection in market downturns while not losing significant return the long horizons. This is achieved by dynamically adjusting the put option compositions to market trends in a timely manner. Allocations to different put options are analyzed in various market trends and investor risk aversion levels. The strategy overcomes the traditional drawbacks of protective put strategies and outperforms both directly investing in the underlying asset and holding a constant long position in a particular put option.

Day-ahead to intraday energy scheduling operation considering extreme events using risk-based approaches

Demand response programs, energy storage systems, electric vehicles, and local electricity markets are appropriate solutions to offset the uncertainty associated with the high penetration of distributed energy resources. It aims to enhance efficiency by adding such technologies to the energy resource management problem while also addressing current concerns using smart grid technologies and optimization methodologies. This paper presents an efficient intraday energy resource management starting from the day-ahead time horizon, which considers the uncertainty associated with load consumption, renewable generation, electric vehicles, electricity market prices, and the existence of extreme events in a 13-bus distribution network with high integration of renewables and electric vehicles. A risk analysis is implemented through conditional value-at-risk to address these extreme events. In the intraday model, we assume that an extreme event will occur to analyze the outcome of the developed solution. We analyze the solution’s impact departing from the day-ahead, considering different risk aversion levels. Multiple metaheuristics optimize the day-ahead problem, and the best-performing algorithm is used for the intraday problem. Results show that HyDE gives the best day-ahead solution compared to the other algorithms, achieving a reduction of around 37% in the cost of the worst scenarios. For the intraday model, considering risk aversion also reduces the impact of the extreme scenarios.

Dual use insurance for annual forage producers: comparing risk management alternatives

PurposeThe purpose of this paper is to evaluate the Dual Use (DU) Option – a crop insurance policy created by the 2018 Farm Bill – relative to other policies available to dual-purpose annual forage producers. The new policy combines existing rainfall-based policies for annual forage crops and multi-peril policies for grain, allowing coverage for multiple crop uses on the same acres during the same growing season.Design/methodology/approachThe paper uses a simulation model to examine crop insurance choices for a typical Texas dual-purpose wheat farm. The certainty equivalent (CE) of wealth is used to rank choices within and between three insurance plans and to analyze the effects of those choices over a range of producer risk aversion levels and for three cases of yield expectations.FindingsThe DU Option is more preferred as risk aversion increases, but it is not universally preferred. Therefore, while the policy can be a viable risk management tool, certain restrictions may be limiting its effectiveness.Practical implicationsThe findings of this paper can help explain farm-level decision making related to dual-purpose annual forage crop insurance program choices.Originality/valueThis paper contributes to the literature by documenting a new crop insurance program made available in the 2018 Farm Bill and provides insights into producers' possible choices by evaluating extensive scenarios.

Robust consumption for individuals with pessimistic survival beliefs

As some literature reveal, young people generally keeps pessimistic survival beliefs, and individuals usually make decisions based on their expectations instead of the experience data. Motivated by this idea, we study the optimal consumption problem for individuals with ambiguity aversion about their mortality. Closed-form robust consumption policy before retirement is obtained by solving the HJB equation by employing the dynamic programming principle. The result tells us that pessimistic survival beliefs, together with interest rate, risk attitude, and expectations for the retirement, play an important role on the consumption and saving decision-making. Reducing interest rates can promote consumption, but the impact is not significant. Both mortality and risk aversion level have significant impacts on the consumption. Too much pessimistic survival beliefs or too much low risk aversion level would lead to overconsumption.

Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals.