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We consider continuous-time mean-field stochastic games with strategic complementarities. The interaction between the representative productive firm and the population of rivals comes through the price at which the produced good is sold. Existence of minimal and maximal equilibria is shown via lattice-theoretical arguments, and sufficient conditions for deriving comparative statics of the equilibria are presented. A detailed numerical study, based on iterative schemes converging to the maximal and minimal equilibria, allows us to analyze in relevant financial examples of how the emergence of multiple equilibria is related to the intensity of strategic interactions. [ABSTRACT FROM AUTHOR]

We introduce a non-zero-sum game between a government and a legislative body to study the optimal level of debt. Each player, with different time preferences, can intervene on the stochastic dynamics of the debt-to-GDP ratio via singular stochastic controls, in view of minimizing non-continuously differentiable running costs. We completely characterise Nash equilibria in the class of Skorokhod-reflection-type policies. We highlight the importance of different time preferences resulting in qualitatively different type of equilibria. In particular, we show that, while it is always optimal for the government to devise an appropriate debt issuance policy, the legislator should optimally impose a debt ceiling only under relatively low discount rates and a laissez-faire policy can be optimal for high values of the legislator’s discount rate. [ABSTRACT FROM AUTHOR]

Purpose: This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways. Design/methodology/approach: This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking. Findings: Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty. Practical implications: This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions. Social implications: Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain. Originality/value: The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) models give each decision making unit freedom to assign values to the input-output weights maximising its efficiency score. However, there is no guarantee that the optimal weights obtained from a DEA model are always positive which poses a problem, in both theory and practice. This paper offers new linear programming models to deal with the zero weights problem without restricting the weights or placing prior value judgments on them while maintaining the original DEA frontier. These models generate a profile of weights with the maximum number of positive weights applicable to both envelopment and multiplier DEA settings. These linear programs still allow weight flexibility and are independent of the solver. We show how these models can be modified to provide unit-specific positive weights. We illustrate the relevance of our approach using artificial and real world data. [ABSTRACT FROM AUTHOR]

Constructing the optimal portfolio by determining and selecting the best combinations of multiple portfolios is computationally challenging due to its exponential complexity. This paper considers the above issue and demonstrates an efficient portfolio selection method based on the sparse minimax Sharpe ratio model involving pre-selected stocks by an unsupervised machine learning approach. Different clustering techniques, such as k-means, fuzzy c-means, and ward linkage, have been used to cluster the stock market data into a finite number of clusters created based on their return rates and related risk levels. Several validity indices have been applied to arrive at the most appropriate number of groups to opt into the portfolio. Further, the sparse minimax Sharpe ratio model is implemented for the selection of the most efficient portfolio. Finally, the efficacy of the developed technique is justified and validated by illustrating a numerical example based on the historical dataset taken from the Bombay stock exchange (BSE), India. [ABSTRACT FROM AUTHOR]

In this paper, we study a robust, dynamic, continuous-time optimal consumption and portfolio allocation problem for investors with recursive preferences who have access to both stock and derivatives markets. We assume the stock price process follows a stochastic volatility model, with instantaneous precision as the unique state variable, allowing for discontinuities in all the dynamics. We obtain a closed-form approximate solution up to a system of ODEs to the optimization problem for a non-unitary value of the elasticity of intertemporal substitution of consumption, being able to derive an exact solution as a particular case. Our theoretical findings show that the optimal policies are remarkably affected by the ambiguity-aversion parameters to diffusive and jump risks. A detailed numerical analysis confirms the effectiveness of our theoretical results on real data. Finally, we prove that investors who do not believe in ambiguity may suffer considerable wealth losses. [ABSTRACT FROM AUTHOR]

In the logistics of prototype parts in the automotive industry, unique parts are stored in warehouses and provided to assembly service providers to ensure a timely assembly of the associated prototype vehicles. As the allocation of the assembly orders to the assembly service providers may change before the start of the assembly, the shipments are planned at short notice. This article considers the short-term task of planning shipments of individual prototype parts from multiple warehouses to multiple assembly service providers. The current literature on planning shipments focuses mainly on parts available in batch sizes larger than one. However, as prototype parts are individual, storing them in adequate warehouses is crucial. A mixed-integer linear programming model for planning transshipments of parts between the warehouses and shipments to the assembly service providers to minimize overall costs is presented. The model enables the utilization of spare capacities of planned transports for a proactive transshipment of parts between warehouses. To this end, the future shipment costs of parts to assembly service providers are approximated to determine suitable warehouses. A numerical study shows that the proactive approach leads to a considerable cost reduction. [ABSTRACT FROM AUTHOR]

We offer a simple analysis of the problem of choosing a statistical experiment to optimize the induced distribution of posterior medians or, more generally, q‐quantiles for any q ∈ (0,1). We show that a single experiment—the q‐quantile matching experiment—implements all implementable distributions of posterior q‐quantiles, with different distributions spanned by different selections from the sets of posterior q‐quantiles. A dense subset of implementable distributions of posterior q‐quantiles can be uniquely implemented by perturbing the q‐quantile matching experiment. A linear functional is optimized over distributions of posterior q‐quantiles by taking the optimal selection from each set of posterior q‐quantiles. The q‐quantile matching experiment is the only experiment that simultaneously implements all implementable distributions of posterior q‐quantiles. [ABSTRACT FROM AUTHOR]

Compared to the distributions of earnings, the distributions of wealth in the US and many other countries are strikingly concentrated on the top and skewed to the right. To explain the income and wealth inequality, we provide a tractable heterogeneous‐agent model with incomplete markets in continuous time. We separate illiquid capital assets from liquid bond assets and introduce jump risks to capital income, which are crucial for generating a thicker tail of the wealth distribution than that of the labor income distribution. Under recursive utility, we derive optimal consumption and wealth in closed form and show that the stationary wealth distribution has an exponential right tail that closely approximates a power‐law distribution. Our calibrated model can match the income and wealth distributions in the US data including the extreme right tail of the wealth distribution. [ABSTRACT FROM AUTHOR]

This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.

The paper explains the origin of the financial crisis in one country and its spread to other countries—contagion, in a dynamic model of international capital inflow. The origin of the crisis is rooted in this model in the common international loan market, and crisis can occur even when the countries are not interconnected via bi-lateral or multi-lateral trade and commerce, when connected via trade or commerce the contagion hits with a stronger effect. The paper introduces country-level heterogeneity of risk of default for individual countries in the borrowing group which generates interesting cases of the contagion effect. A change in the risk of default of an individual country adversely affects the total supply in the international loan market leading to crises in other countries via increased international interest rates. It has an enhanced effect when the risk of default depends on the lagged domestic productivity of the borrowing country. The model can explain various episodes of financial crisis in a common framework and can be usefully employed for policy formulation. [ABSTRACT FROM AUTHOR]

In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, divergence risk measures, uncertainty on path spaces, moment constraints, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting. [ABSTRACT FROM AUTHOR]

We investigate whether a benchmark and non-constant risk aversion affect the probability density distribution of optimal wealth at retirement. We maximize the expected utility of the ratio of pension wealth at retirement to an inflation-indexed benchmark. Together with a threshold and a lower bound, we are able to generate closed-form solutions. We find that this non-constant risk aversion type of utility could shift the probability density distribution of optimal wealth more towards the benchmark, and that the probability of achieving a certain percentage of the desired benchmark could be increased. The probability density distribution generated under constant relative risk aversion (CRRA) risk preference is more widely spread along the benchmark. [ABSTRACT FROM AUTHOR]

This paper addresses the retirement income planning problem from the perspective of the four main building blocks of retirement income: state pension, mortality credits, investment strategies, and drawdown schedules. We detail how these building blocks interact to form a retiree's overall retirement income portfolio, and what trade-offs and interactions must be considered. We find that while access to each building block increases the retiree's certainty equivalent consumption, the most substantial contributor to this increase is from utilization of the mortality credit building block (i.e. annuities). [ABSTRACT FROM AUTHOR]

We revisit a service provider's problem to match supply and demand via an online appointment system such as a doctor in the health care sector. We identify in a survey that an extensive set of available appointments leads to significantly less demand because customers infer a lower quality of the service, as part of an observational learning process. We capture the quality inference effect in a multinomial logit framework and present a Markov decision process for solving the problem of releasing available slots of the appointment system to optimality aiming at maximizing the expected profits. We further evaluate several simple decision rules and provide management insights on which rule to apply under different generic scenarios. Different from current literature, offering all available appointments may lead to suboptimal results when accounting for the quality inference effect. The profit-maximizing strategy then is to offer a subset of the available appointments. [ABSTRACT FROM AUTHOR]

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We investigate the optimal investment-and-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques, we convert the original optimization problem involving different filtrations into an equivalent stochastic control problem under the observation filtration, i.e. the so-called separated problem. The Markovian structure of the separated problem allows us to apply a classical approach to stochastic optimization based on the Hamilton–Jacobi–Bellman equation, and to provide explicit formulas for the value function and the optimal investment-and-reinsurance strategy. We finally discuss some comparisons between the optimal strategies pursued by a partially informed insurer and that followed by a fully informed insurer, and we evaluate the value of information using the idea of indifference pricing. These results are also supported by numerical experiments. [ABSTRACT FROM AUTHOR]

This study explores the economics of culling decisions in cow-calf operations in the Southern U.S. with a novel application of a dynamic mathematical programing model. The results provide an optimal culling strategy under the base model and a range of optimal strategies that vary with respect to different components such as fertility probabilities, prices, replacement costs, and pregnancy checking. The results suggest that producers should cull all cows that are older than age 10 and cows that fail to calve once they reach the age of 7. The sensitivity analysis underlines the impact of market conditions, replacement costs, and pregnancy check use on the optimal culling decisions. [ABSTRACT FROM AUTHOR]

We extend Stiglitz (J Public Econ, 17, 213–240,1982) to a dynamic and stochastic optimal taxation model, while assessing the effects of exogenous wage regulations. We show that when wage regulations are imposed, consumption inequality rises more than when they are removed. The results lead to an important policy implication that in the face of exogenous wage regulations, the social planner who can design optimal tax schedule prefers to widen the consumption inequality. From this perspective, the drive to enhance production efficiency outweighs the motivation for redistribution. A conventional wisdom that full employment commitment and minimum wage legislation should serve as a buffer for the minority may not be taken for granted. In the qualitative analysis, we find that as wage regulations tighten, the lower bound of the marginal tax rate on capital rises. Our quantitative result validates that, showing that the marginal tax rate on capital increases to varying degrees in response to wage requirements of varying tightness. [ABSTRACT FROM AUTHOR]

This paper examines the relative cost efficiency of U.S. small banks after the 2008 Global Financial Crisis (2008 GFC). Using financial information from 10,495 of the same small banks operating from 2010 to 2021, we examine the after-effects of the recent global financial crises on the U.S. small banks. The study uses Data Envelopment Analysis (DEA) to calculate the overall efficiency using yearly and pooled data. The overall efficiency measure is then decomposed into allocative, technical, pure-technical, and scale efficiency to better understand the sources of small banks’ inefficiencies. The results indicate that the overall efficiency of small banks operating in the U.S. after the 2008 GFC has been continuously low until 2021. The source of the low level of overall efficiency has been the low level of technical efficiency rather than allocative efficiency. In turn, the basis of the low level of technical efficiency has been pure technical and scale efficiency. Understanding the origins of cost inefficiencies in small banks has implications for micro and macro policymaking. Examining the underlying causes of cost inefficiencies in small banks after the financial crisis can inform policymakers in devising strategies to improve banks’ cost efficiency.

Purpose: The authors construct two time-varying political connection (PC) indexes to measure a firm's political tendencies toward ruling and opposing parties and analyze whether a firm with ruling party tendencies obtains better bank loan contracts compared to the contracts obtained by a firm with opposing party tendencies and a firm with fixed PC tendencies. Design/methodology/approach: Linguistic text mining is used to construct the two time-varying PC indexes from news sources that reflect the tone and frequencies of characteristic texts to determine a firm's tendencies to favor the ruling or opposing parties. Findings: The results show that varying PC firms connected to the ruling party receive preferential loan contracts when their political tendencies increase but varying PC firms connected to the opposition party do not. In contrast, fixed PC firms gain similar benefits only when the connection is determined in the presidential election year but not in other years. Firms supporting two parties receive minimal financial rewards in terms of loan terms. Originality/value: In past studies, once a firm is identified as having a connection with a political party, it is assumed to have PC throughout the sample period (i.e. fixed PC firms). The authors lift this assumption and examine how varying PC affect bank loan contracts. The two time-varying PC indexes can identify a firm's more immediate party tendencies and more precise effects of a firm's party tendencies on bank loan contracts. [ABSTRACT FROM AUTHOR]

In this paper, we develop a framework to analyze stochastic dynamic optimization problems in discrete time. We obtain new results about the existence and uniqueness of solutions to the Bellman equation through a notion of Banach contractions that generalizes known results for Banach and local contractions. We apply the results obtained to an endogenous growth model and compare our approach with other well‐known methods, such as the weighted contraction method, countable local contractions, and the Q‐transform. [ABSTRACT FROM AUTHOR]

AbstractThis study employs a non-linear state space model and applies Extended Kalman Filter to analyze the effects of shocks to output, investment and government consumption for the US economy within a stochastic intertemporal current account framework for the particular period 1989Q2-2019Q4. It is shown that consumption and current account dynamics are successfully explained and the real interest rate, which is needed to rationalize the consumption and income relationship, is found to be fairly reasonable. The parameter estimates of the macroeconomic variables in the model indicate high accuracy of forecast according to the MAPE criteria for model evaluation. The intertemporal substitution and compensation effects allow aggregate consumption function to absorb exogenous shocks and reflect the smooth transition of the economy to the reestablishment of equilibrium position. [ABSTRACT FROM AUTHOR]

We refer to the discrete-time market model under ambiguity introduced in [Cinfrignini, A., Petturiti, D. and Vantaggi, B., Dynamic bid–ask pricing under Dempster-Shafer uncertainty. J. Math. Econ., 2023a, 107, 102871], formed by a frictionless risk-free bond and a non-dividend paying stock with bid-ask spread. For a European derivative, we generalize the classical binomial pricing formula by allowing for bid-ask prices and investigate the properties of the ensuing replicating strategies. Next, for an American derivative, we propose a backward bid-ask pricing procedure and prove that the resulting discounted price processes are the bid-ask Choquet-Snell envelopes of the discounted payoff process, respectively. Moreover, for an American call option, we prove a generalization of the well-known Merton's theorem [Merton, R.C., Theory of rational option pricing. Bell J. Econ. Manage. Sci., 1973, 4, 141–183] holding for both the bid and the ask price processes. Finally, we introduce a market consistent calibration procedure and show the use of the calibrated model in bid-ask option pricing. [ABSTRACT FROM AUTHOR]

Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. [ABSTRACT FROM AUTHOR]

Compared with the extensive empirical literature on contrarian strategy, we develop a dynamic mean-variance model with geometric value-reversion asset prices, which implies a contrarian strategy. The model is solved (semi) explicitly under three asset price evaluations: constant valuation, exponential-varying valuation, and geometric average valuation. From a mathematical perspective, it is nontrivial to solve the extended HJB equations under stochastic opportunities. We demonstrate that our strategy exhibits the same monotonicity as that of the traditional constant relative risk-averse utility, and the welfare loss of using the dynamic mean-variance criterion is rather small, supporting that our model is a good approximation to the constant relative risk-averse utility. Empirical tests show that our strategies can help an investor achieve a less volatile wealth trajectory. [ABSTRACT FROM AUTHOR]

In this paper we explore optimal liquidation in a market populated by a number of heterogeneous market makers that have limited inventory-carrying and risk-bearing capacity. We derive a reduced form model for the dynamics of their aggregated inventory considering a proper scaling limit. The resulting price impact profile is shown to depend on the characteristics and relative importance of their inventories. The model is flexible enough to reproduce the empirically documented power law behavior of the price impact function. For any choice of the market makers characteristics, optimal execution within this modeling approach can be recast as a linear-quadratic stochastic control problem. The value function and the associated optimal trading rate can be obtained semi-explicitly subject to solving a differential matrix Riccati equation. Numerical simulations are conducted to illustrate the performance of the resulting optimal liquidation strategy in relation to standard benchmarks. Remarkably, they show that the increase in performance is determined by a substantial reduction of higher order moment risk. [ABSTRACT FROM AUTHOR]

This paper analyzes panic buying of storable consumer products accompanied by disasters, using a novel consumer‐search theoretic equilibrium model where consumers follow (S,s) inventory policies. We show that, even if consumers are fully rational, an anticipated temporary increase in consumer shopping costs (as well as conventional demand and supply shocks) can trigger an upward spiral of hoarding demand and result in serious shortages. Due to congestion externalities, panic buying leads to the misallocation of storable products and substantial welfare loss. The model is calibrated using survey data and reveals that the timing of recognizing the shopping‐cost rise is crucial for the severity of panic buying. Some policy options, such as purchase quotas and future sales‐tax reductions, are suggested to mitigate panic buying. [ABSTRACT FROM AUTHOR]