Exogenous Uncertainty Research Articles

Home country’s economic and political institutions: firms’ ownership decisions in cross-border acquisitions

The influences of a home country’s economic and political institutions on acquirers’ cross-border acquisitions (CBAs) ownership strategies remains unexplored. Acquirers face endogenous uncertainty (i.e., uncertainty that can be resolved in part by acquirers) when transferring headquarters resources to foreign target firms and exogenous uncertainty (i.e., uncertainty that cannot be resolved by acquirers) when there is an unpredictable policy change. We argue that well-developed economic and political institutions in a home country play a market-supporting and constraining role in mitigating endogenous and exogenous uncertainty respectively, enabling acquirers to seek high ownership stakes in CBAs. We also argue that the importance of a home country’s well-developed economic and political institutions for acquirers’ CBA ownership strategic decisions depends on mutual trade dependence between the acquirers’ home country and the target firms’ host countries and also on the economic capabilities of the acquirers developed in different industries and political capabilities developed in different host countries. To test these arguments, we analyze 133,623 CBAs between 2000 and 2020 and find support for the distinct roles played by a home country’s economic and political institutions.

Refinery planning optimization based on smart predict-then-optimize method under exogenous price uncertainty

Refinery planning plays a crucial role in optimizing refined resource allocation. However, the exogenous uncertainty represented in product prices often hinders refineries from making timely adjustments to their planning decisions and affecting overall profitability. To address this challenge, a smart predict-then-optimize method is designed for refinery planning under product price uncertainty. The temporal graph convolution network is employed to capture spatio-temporal features in product prices, resulting in enhanced prediction performance. Furthermore, the proposed method effectively integrates the stages of price prediction and refinery planning optimization, with the combined loss contributing to improved decision-making performance. The enhancements in predicting price uncertainty parameters and optimizing profits is demonstrated by multi-period and single-period refinery planning case.

Biographies of uncertainty regulation in the labor market and extension of working life in Chile

Abstract Despite lacking policies targeting the extension of working life, Chile is the Latin American country that has exhibited the largest increase in the labor force participation rate of people aged 65+ in the last two decades. In this research, following an analytical framework on regulation of endogenous uncertainty and relying on rich qualitative data (life story interviews of 90 older workers aged 60–86, across 21 cities and 6 regions), we approached the complexity of extended working lives in Chile by addressing an unexplored dimension. Specifically, we explore individuals’ agency over their employment trajectories (i.e., both in adulthood and old age) among those who remained active in the labor market after the legal retirement age. Our findings provide strong evidence that extended working lives not only result from precarious social conditions, but are also shaped by complex processes involving both expansive and adaptive individual agency in which people engaged throughout their life course. Therefore, the high exogenous uncertainty in the labor market should not merely be interpreted from the perspective of “precarity,” but also as a scenario that encouraged individuals to behave in a way that led them to engage in the labor force across their lives in accordance with their preferred level of endogenous uncertainty.

Simulation modeling of a synergetic chemical reactor control system

One of the promising ways to solve the problem of synthesizing control systems for nonlinear dynamic objects operating under conditions of exogenous uncertainties is the use of methods based on the synergetic principle of target self-organization for control purposes. The paper examines the problem of synthesizing effective control algorithms for a chemical reactor, which is the main element of petrochemical production, subject to the influence of various uncertain factors, when implementing a series-parallel reaction. The goal of the synthesized control system is to ensure a stable concentration of the target component at the output of the reactor under the influence of uncertain disturbances on the object, as well as to transfer the apparatus from one productivity to another (specified) while maintaining the required quality of the target component.

Uncertainty about the war in Ukraine: Measurement and effects on the German economy

We assemble a data set of more than eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.

Subjective perception of economic policy uncertainty and corporate social responsibility: Evidence from China

While existing literature explores factors affecting corporate social responsibility (CSR) from various perspectives, the role of economic policy uncertainty perception has never been studied to our knowledge. In this paper, we study the effect of Chinese firms' subjective perception of economic policy uncertainty (SEPU) on their CSR engagement. Our model proves that SEPU reduces the benefits of maintaining relationships between shareholders and stakeholders, thus decreases corporate CSR engagement. This negative relationship is more pronounced for firms that are highly influenced by the government. We empirically examine these two predictions using the sample of Chinese A-share listed companies. We address endogenous concerns by using the US-China trade war as an exogenous uncertainty shock, and including a Bartik instrumental variable.

Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast

More accurate multivariate load forecasting is conducive to integrated energy system planning optimization and economic operation. In this paper, based on the consideration of exogenous factors such as weather, endogenous factors are added into the prediction, and a short-term prediction model for integrated energy multivariate load based on endo-exogenous uncertainty is established using fine-tuned transfer learning. The LSTM pre-training model is first initially established using genetic algorithm and exogenous factors such as weather. Then the exogenous uncertainty of the pre-training model and the knowledge of endogenous uncertainty in the target model are combined using fine-tuned transfer learning to continuously optimize the basic parameters of the prediction model to obtain the final multivariate load prediction model. The performance of the four models is comprehensively evaluated after the historical load validation of the integrated energy system in Beijing, China. Compared with the traditional LSTM multivariate load forecasting model, the MAPE of the proposed model in this paper is reduced by 21.03 %, 13.00 %, and 24.39 % for electricity, heat, and cooling load forecasting, respectively.

A multistage distributionally robust optimization approach for generation dispatch with demand response under endogenous and exogenous uncertainties

AbstractDecision‐dependent (endogenous) uncertainties (DDUs), as a new type of uncertainties revealed recently, couple dispatch decisions with uncertainty parameters and thus render power system dispatch more challenging. However, most previous works handled various DDUs via stochastic programming (SP) or robust optimization (RO) in a two‐stage framework, which undoubtedly introduces the drawbacks of SP and RO, and cannot meet the nonanticipativity requirements in power scheduling. In this paper, a multistage distributionally robust optimization (DRO) method for generation dispatch with demand response (DR) is proposed considering the DDUs of deferrable loads and the decision‐independent (exogenous) uncertainties (DIUs) of wind power and regular loads. By analyzing the structure of decision‐dependency parameters, a novel data‐driven decision‐dependent ambiguity set is proposed, which provides a generic framework for formulating DDUs and DIUs simultaneously. Then a multistage DRO model with nested max‐min structure is developed to integrate the merits of DRO and nonanticipativity into generation dispatch. The proposed model is solved by tailored reformulation method and improved stochastic dual dynamic integer programming (SDDiP). Case studies illustrate the effectiveness of the proposed approach by comparing with the multistage SP, RO, and decision‐independent DRO methods.

Современные тренды российского финтеха

Commitment of leading Russian and foreign companies to digital business models confirms that fintech is a driver for the digital economy and socio-economic development. Under growing exogenous uncertainties and sanctions to the financial sector, assessing the current trends and prospects in fintech is of particular interest. The article uses statistical and analytical methods to evaluate the empirical data, gathered by the Federal State Statistics Service, the Bank of Russia, and different experts. Until 2022, the fintech market in Russia was the leader in attracting venture capital funding. The authors identified three waves of market shocks in the current development of the Russian fintech market. Fintech companies overcome each subsequent shock much faster than the previous one, which indicates a high inherent adaptability. The article highlights trends in business, fintech, information and communication technologies, typical for the modern Russian economy. Despite the high volatility of the current period, in terms of the fintech solutions development Russia remains competitive. The development of independent technologies and services by the IT and financial markets, preferentially regulated by the Bank of Russia, seems feasible and viable.

Multi-criteria robust optimization for bridge adaptation strategies targeting superstructure unseating under coastal natural hazards

The life-cycle management of low-lying coastal bridges exposed to coastal natural hazards (e.g., Hurricane Ivan, Katrina, and the recent Ian) has become an ongoing concern due to the detrimental impacts of these extreme events. The uncertain occurrence of multiple climate drivers and their accompanying influence on the frequency and intensity of coastal hazards further complicates the determination of bridge adaptations. Reliable modeling of the failure probability and consideration for the deep uncertainties associated with climate change are essential to determining retrofit action and implementation time. This study proposes a multi-criteria optimization framework for robust adaptation strategies of coastal bridges. The proposed framework can devise bespoke adaptation strategies towards the dominant superstructure unseating failure, which is enabled by: (a) pragmatic bridge vulnerability analysis rooted in the coupled fluid–structure dynamic numerical analysis; (b) multi-criteria optimization assessing the efficiency and priority of different adaptations; and (c) strategy robustness against the unknown occurrence of imminent climate futures. The exogenous uncertainties associated with future economics and climate scenarios are addressed via sensitivity analysis. To the best knowledge of the authors, this is the first time that robust adaptation strategies are developed for coastal bridges targeting hurricane-induced superstructure damages. These results not only measure the feasibility of various adaptation actions from both economic and engineering aspects but also provide a reference basis for decision-makers.

Correction to “Exogenous uncertainty and the identification of structural vector autoregressions with external instruments”

Correction to “Exogenous uncertainty and the identification of structural vector autoregressions with external instruments”

Investment decisions under uncertainty: Corporate venture capital as a real option

AbstractThis paper aims to assess the role of corporate venture capital (CVC) as option‐creating investments and how corporate investors practice CVC options in their investment decisions. Using a sample of 4206 US firms between 1998 and 2019, our results show that sector fit and geographic fit reduce uncertainty and impact investors' decisions on CVC investments. When exogenous uncertainty is mitigated, CVC‐backed companies experience higher investment amounts than independent venture capital funds (IVC)‐backed ones. The results evidence that follow‐on investments motivate IVC‐backed companies to exit via IPOs. At the same time, a prolonged duration for the organisational learning option leads CVC‐backed companies to exit via acquisition. This study helps investors to reduce uncertainty surrounding investments and improve the usefulness of their real options. We also provide implications for how policymakers target and develop an entrepreneurial ecosystem.

The Impact of Uncertainty Shocks to Consumption under Different Confidence Regimes Based on a Stochastic Uncertainty-in-Mean TVAR Model

Exogenous uncertainty shocks may have different effects on domestic and foreign consumption under different consumer confidence regimes. In this paper, we specify a threshold vector autoregressive (TVAR) model with different consumer confidence regimes to study the response of endogenous macroeconomic variables to exogenous shocks. The evidence shows that in China, compared to high consumer confidence, low consumer confidence dampens consumption both at home and abroad. However, low consumer confidence benefits exports, the source of foreign consumption. A forecast error variance decomposition analysis further confirms the difference in the effects under different consumer confidence regimes. A comparative analysis shows that consumer confidence is much more influential in the US than in China. Our findings differ from those of earlier works, as we introduce stochastic uncertainty to both the mean and heteroscedasticity and apply counterfactual analysis to show the hazard of ignoring stochastic uncertainty in the traditional threshold vector autoregression. Finally, from the ex ante and ex post perspectives, we provide managerial implications for the authorities to tackle economic issues based on different consumer confidence regimes.

Strategic interaction between institutional investors and supervision department: a theoretical analysis of low-price collusion in SBIC

We introduce evolutionary game method to analyze low-price collusion in inquiry market of Sci-Tech Innovation Board of China (SIBC) from the perspective of strategic interaction between large institutional investors (LIIs), small and medium-sized institutional investors (SMIIs), and supervision department (SD). The results show that supervision behaviors of SD, and quotation behaviors of institutional investors, are subject to supervision conditions. Under the condition that benefits of tough supervision are lower a lot than minimum benefits of light supervision (light supervision condition), SD will choose light supervision and institutional investors will turn to illegal quotation in response. Finally, a steady-state equilibrium with low-price collusion will form in SIBC’s inquiry market even with a large supervision penalty for illegal quotation. On the contrary, under the condition that benefits of tough supervision are higher a lot than maximum benefits of light supervision (tough supervision condition) and with a large penalty for illegal quotation, SD and institutional investors will choose tough supervision and legal quotation. Further numerical simulations under light supervision condition show that: (1) High-price culling rule will become a booster for low-price collusion and accelerate SMIIs’ evolutionary process to imitative quotation. (2) Blindly increasing penalties for illegal quotation or reducing the culling rate is not an appropriate approach to solve the problem of low-price collusion since it cannot shift supervision condition from light into tough and make SD supervise toughly. (3) Institutional investors’ choices of quotation strategies are more volatile and highly susceptible to supervision behaviors of SD when facing exogenous uncertainty. Therefore, the keys to solving the problem of low-price collusion are shifting supervision condition from light into tough through increasing incremental benefits of tough supervision, and providing institutional investors with a stable and predictable supervision policy. In conclusion, the creation of a fair inquiry market doesn’t only depend on restraint and punishment to institutional investors, but also requires the establishment of supervision mechanism those are compatible with market-based inquiry.

Incorporating ignorance within game theory: An imprecise probability approach

Ignorance within non-cooperative games, reflected as a player's uncertain preferences towards a game's outcome, is examined from a Bayesian point of view. This topic has had scarce treatment in the literature, which emphasises exogenous uncertainties caused by other players or nature and not by players themselves. That is primarily because a player's endogenous uncertainty over an outcome poses significant challenges and complex sequences of reciprocal expectations. Therefore, it is often ignored, and preferences are either assumed from a continuous domain or set using introspection, resulting in non-optimal models. We here explore a solution concept based on recent research in imprecise probabilities and de Finetti's approach to defining subjective probabilities, which utilises bets to assess beliefs. The resulting model allows players to be ignorant about their initial preferences and learn about them in repeated games. Furthermore, it permits improving the value of information in these situations. This model is proposed as a possible solution to the problem of utility inference in game-theoretic settings that include uncertainty over outcomes. We demonstrate it through motivating repeated-game problems modified to have uncertainty and through a simulation over a case of extreme ignorance.

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and machine learning for quantification of the uncertainty impacts on a national-level energy system from global and local perspectives. This approach allows in-depth correlation analysis highlighting potential synergies and risks in the energy transition, and can be easily applied for assisting policy making. The case study for Switzerland shows that both carbon neutrality (even negative emissions) and energy autonomy can be achieved by 2050, but the energy system’s configuration varies significantly under uncertainty. Through conditional distribution analyses, carbon capture and storage (CCS), Photovoltaic (PV), and wood gasification show the most strong correlation for decarbonization. This study is based on the whole uncertainty space taking into account heterogeneous uncertainties, leading to increased reliability compared to sensitivity analysis from single scenarios’ comparisons. The synergy between energy models and artificial intelligence (AI) is promising to be widely applied in energy planning area, particularly for emerging technologies with large uncertainty in development.

A capacitated lot-sizing problem in the industrial fashion sector under uncertainty: a conditional value-at-risk framework

In this study, we present a multi-product, multi-period inventory control problem under uncertainty in product demands that emerges in the fashion industry. A two-stage stochastic model is proposed to design a planning strategy where the total cost incurred by purchase orders, inventory and shortage is minimised. We incorporate the Conditional Value at Risk (CVaR) within the formulation to address exogenous uncertainty. An industrial case study involving a Mexican fashion retail company was considered to assess the performance of the two-stage stochastic model. Scenarios were considered using historical data provided by the company. A sensitivity analysis was also conducted on risk-aversion parameters to assess how the values of these parameters affect the behaviour of the proposed formulation. The results show that the proposed two-stage stochastic formulation is an efficient and practical approach to handle exogenous uncertainty in industrial-scale capacitated lot-sizing problems.

How does uncertainty affect corporate investment inefficiency? Evidence from Europe

We conduct a cross-country, firm-based analysis of the impact of uncertainty (World Uncertaınty Index–WUI) on corporate investment inefficiency. The analysis, conducted in the period 2012–2020 across 30 European region countries, demonstrates a negative impact of uncertainty on the absolute value of investment inefficiency. Specifically, there is a negative effect of uncertainty on investment inefficiency in cases of overinvestment, and a positive effect of uncertainty on investment inefficiency in cases of underinvestment. Thus, in both abnormal investment scenarios, uncertainty alleviates the investment inefficiency – namely, uncertainty may optimize investment decisions. The findings are robust to country, industry, and time fixed-effects; GMM; and different legal systems. Moreover, the findings shed important light on the exogenous uncertainty effect on asymmetric information and agency, generating investment decisions.

Hybrid Entrepreneurs’ Resource Allocation and the Role of Exogenous and Endogenous Uncertainties

While prior research has made valuable contributions towards understanding why most individuals enter entrepreneurship next to their wage job, evidence on how these hybrid entrepreneurs manage their business after entry is largely absent. We fill this gap by studying hybrid entrepreneurs’ resource allocation while being in hybrid entrepreneurship compared to that after transitioning to full-time entrepreneurship. Drawing on real options theory, we argue that hybrid entrepreneurs’ resource allocation towards R&D tasks and sales tasks depends on whether these tasks are related to endogenous or exogenous uncertainty. Our empirical analysis based on a matched panel-dataset encompassing 797 hybrid entrepreneurs yields support for our hypotheses. Our paper offers contributions to the hybrid and the broader entrepreneurship literature, as well as to research on real options theory.

A Simulation Optimization Approach for the Appointment Scheduling Problem with Decision-Dependent Uncertainties

The appointment scheduling problem (ASP) studies how to manage patient arrivals to a healthcare system to improve system performance. An important challenge occurs when some patients may not show up for an appointment. Although the ASP is well studied in the literature, the vast majority of the existing work does not consider the well-observed phenomenon that patient no-show is influenced by the appointment time, the usual decision variable in the ASP. This paper studies the ASP with random service time (exogenous uncertainty) with known distribution and patient decision-dependent no-show behavior (endogenous uncertainty). This problem belongs to the class of stochastic optimization with decision-dependent uncertainties. Such problems are notoriously difficult as they are typically nonconvex. We propose a stochastic projected gradient path (SPGP) method to solve the problem, which requires the development of a gradient estimator of the objective function—a nontrivial task, as the literature on gradient-based optimization algorithms for problems with decision-dependent uncertainty is very scarce and unsuitable for our model. Our method can solve the ASP problem under arbitrarily smooth show-up probability functions. We present solutions under different patterns of no-show behavior and demonstrate that breaking the assumption of constant show-up probability substantially changes the scheduling solutions. We conduct numerical experiments in a variety of settings to compare our results with those obtained with a distributionally robust optimization method developed in the literature. The cost reduction obtained with our method, which we call the value of distribution information, can be interpreted as how much the system performance can be improved by knowing the distribution of the service times, compared to not knowing it. We observe that the value of distribution information is up to 31% of the baseline cost, and that such value is higher when the system is crowded or/and the waiting time cost is relatively high. Summary of Contribution: This paper studies an appointment scheduling problem under time-dependent patient no-show behavior, a situation commonly observed in practice. The problem belongs to the class of stochastic optimization problems with decision-dependent uncertainties in the operations research literature. Such problems are notoriously difficult to solve as a result of the lack of convexity, and the present case requires different techniques because of the presence of continuous distributions for the service times. A stochastic projected gradient path method, which includes the development of specialized techniques to estimate the gradient of the objective function, is proposed to solve the problem. For problems with a similar structure, the algorithm can be applied once the gradient estimator of the objective function is obtained. Extensive numerical studies are presented to demonstrate the quality of the solutions, the importance of modeling time-dependent no-shows in appointment scheduling, and the value of using distribution information about the service times.