The purpose of this study is to examine the connectedness of industry sectors on the Johannesburg Stock Exchange in a time-frequency domain. We use econophysics-based methods like the wavelet multiple correlation and wavelet scalogram difference to identify the evolution of the connectedness of the sectors over time and at different frequencies. The findings show that the sectors on the Johannesburg Stock Exchange are especially integrated at lower frequencies. Wavelet multiple correlation peaks in response to local and global shocks like the black-swan COVID-19 pandemic in 2020 and the downgrading of South African debt by Fitch in 2013. Though there are opportunities for sectoral diversification on the JSE, this fails when it is most needed, during crisis periods. Investors should therefore consider other asset classes that could serve as a haven in times of crisis. Though extant literature has examined sectoral dependencies on the stock markets of developed and developing countries, to the best of our knowledge, this is the first study to examine this connectedness in a South African context using multiple nonparametric methods that are robust to non-normality, presence of outliers as well as non-stationary data.

In this paper, we model an evolutionary noncooperative game between politicians and citizens that, given the level of infection, describes the observed variety of mitigation policies and citizens' compliance during the COVID-19 pandemic period. Our results show that different stable equilibria exist and that different ways/paths exist to reach these equilibria may be present, depending on the choice of parameters. When the parameters are chosen opportunistically, in the short run, our model generates transitions between hard and soft policy measures to deal with the pandemic. In the long-run, convergence is achieved toward one of the possible stable steady states (obey or not obey lockdown rules) as functions of politicians' and citizens' incentives.

This paper uses multivariate Hawkes processes to model the transactions behavior of the US stock market as measured by the 30 Dow Jones Industrial Average individual stocks before, during and after the 36-min May 6, 2010, Flash Crash. The basis for our analysis is the excitation matrix, which describes a complex network of interactions among the stocks. Using high-frequency transactions data, we find strong evidence of self- and asymmetrically cross-induced contagion and the presence of fragmented trading venues. Our findings have implications for stock trading and corresponding risk management strategies as well as stock market microstructure design.

How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the 'global' level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for insuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their investors and use either reinsurance contracts or cat bonds to hedge their tail risk. The model generates plausible time series of profits and losses and recovers stylized facts, such as the insurance cycle and the emergence of asymmetric firm size distributions. We use the model to investigate the problem of risk model homogeneity. Under the European regulatory framework Solvency II, insurance companies are required to use only certified risk models. This has led to a situation in which only a few firms provide risk models, creating a systemic fragility to the errors in these models. We demonstrate that using too few models increases the risk of nonpayment and default while lowering profits for the industry as a whole. The presence of the reinsurance industry ameliorates the problem but does not remove it. Our results suggest that it would be valuable for regulators to incentivize model diversity. The framework we develop here provides a first step toward a simulation model of the insurance industry, which could be used to test policies and strategies for capital management.

This paper applies the time varying parameter-vector autoregression model to explore the dynamic relationship between economic policy uncertainty, investor sentiment and financial stability in China in different periods and at different time points. The empirical results show that economic policy uncertainty has an obvious negative impact on investor sentiment before 2012 and financial stability in the short term, and the influence of economic policy uncertainty on investor sentiment is greater than that of economic policy uncertainty on financial stability. These influences were more significant during the period of the global financial crisis in 2008. Moreover, investor sentiment had a positive and gradually increasing effect on financial stability, while after 2010, the positive impact gradually weakened. Furthermore, economic policy uncertainty is negatively affected by financial stability, and the effect of financial stability on investor sentiment is positive. In terms of mediating effects, economic policy uncertainty has an indirect impact on financial stability through investor sentiment and vice versa. This paper provides a new solution to economic problems explored in behavioral finance research. Additionally, Chinese government agencies can achieve the goal of preventing financial crises and maintaining financial stability by monitoring investor sentiment and implementing targeted economic policies.

How will the novel coronavirus evolve? I study a simple epidemiological model, in which mutations may change the properties of the virus and its associated disease stochastically and antigenic drifts allow new variants to partially evade immunity. I show analytically that variants with higher infectiousness, longer disease duration, and shorter latent period prove to be fitter. "Smart" containment policies targeting symptomatic individuals may redirect the evolution of the virus, as they give an edge to variants with a longer incubation period and a higher share of asymptomatic infections. Reduced mortality, on the other hand, does not per se prove to be an evolutionary advantage. I then implement this model as an agent-based simulation model in order to explore its aggregate dynamics. Monte Carlo simulations show that a) containment policy design has an impact on both speed and direction of viral evolution, b) the virus may circulate in the population indefinitely, provided that containment efforts are too relaxed and the propensity of the virus to escape immunity is high enough, and crucially c) that it may not be possible to distinguish between a slowly and a rapidly evolving virus by looking only at short-term epidemiological outcomes. Thus, what looks like a successful mitigation strategy in the short run, may prove to have devastating long-run effects. These results suggest that optimal containment policy must take the propensity of the virus to mutate and escape immunity into account, strengthening the case for genetic and antigenic surveillance even in the early stages of an epidemic.

We use public goods games to experimentally investigate the effect of granularity (i.e., the degree of divisibility of the space of feasible contribution options) on participation (whether individuals contribute or not to the public good) and public goods provision (total contribution to the public good). Our results show that granularity has a significant effect on participation, mainly when coarser granularity eliminates the possibility of small contributions. However, this change in participation does not lead to a significant change in the total provision of the public good. These results are aligned with other experimental field results obtained in the context of donations and fundraising.

In this review paper, we aim to make the case that a concept from retail analytics and marketing-the -can provide promising new frameworks and support for agent-based modeling, with a broad range of potential applications to high-resolution and high-fidelity simulation of dynamic phenomena on urban high streets. Although not the central focus of the review, we consider agent-based modeling of retail high streets against a backdrop of broader debate about downtown vitality and revitalization, amid a climate of economic challenges for brick-and-mortar retail. In particular, we consider how agent-based modeling, supported by insights from consideration of indoor shopping, can provide planning and decision support in outdoor high street settings. Our review considers abstractions of customers through conceptual modeling and customer typology, as well as abstractions of retailing as stationary and mobile. We examine high-level agency of shop choice and selection, as well as low-level agency centered on perception and cognition. Customer journeys are most often trips through geography; we therefore review path-planning, generation of foot traffic, wayfinding, steering, and locomotion. On busy high streets, journeys also manifest within crowd motifs; we thus review proximity, group dynamics, and sociality. Many customer journeys along retail high streets are dynamic, and customers will shift their journeys as they come into contact with experiences and service offerings. To address this, we specifically consider treatment of time and timing in agent-based models. We also examine sites for customer journeys, looking in particular at how agent-based models can provide support for the analysis of atmospherics, artifacts, and location-based services. Finally, we examine staff-side agency, considering store staff as potential agents outdoors; and we look at work to build agent-based models of fraud from customer journey analysis.

The competition of interpersonal communication platforms is a complex process affected by various factors. This paper aims to simulate and analyze this process from a bottom-up perspective. Individual platform selection serves as the micro-foundation for the study. The evolution of online interpersonal communication networks, and innovations proposed by online interpersonal communication platforms, would also impact this process by affecting individual selection on those platforms. Three scenarios were designed for this study to simulate typical modes of competition. In this regard, the simulation results were compared to practical cases. Taken together, this bottom-up simulation model could reproduce and anticipate the applied competition process associated with such platforms. Based on this model, it was found that, in any case, one online interpersonal communication platform will eventually monopolize the market, either partly or entirely. The late entrant platform, comprising a major innovation, tends to fail when competing with the incumbent monopoly due to "network externalities." Even when two competing platforms continue to propose innovations, and they will alternately lead the competition due to those innovations, this type of replacement of their competitive positions in the market may only occur a few times and then disappear completely.

This paper extends the conventional DSGE literature by developing a New Keynesian DSGE model featuring imperfect financial markets with various friction costs, which allows for the study of macroeconomic dynamics under different levels of financial integration. We conduct Bayesian estimation and draw implications on the macroeconomic effects of gradual financial integration using the Chinese economy as an example. We find that macroeconomic fluctuations vary with different levels of financial integration and the specific relationship depends on the nature of exogenous shocks. Variance decomposition analysis shows that as financial integration increases, the contribution of foreign exchange shocks declines while that of domestic shocks increases. We also find that there is a notable enhancement of welfare associated with improvement in financial integration, and the effectiveness of monetary policy in emerging market economies would be weakened as financial integration increases.

We develop an agent-based model of vaccine decisions across a heterogeneous network model with urban and rural regions. In the model, agents make rational decisions to vaccinate or not, based on the relative private costs of vaccinations and infections as well as an estimated probability of infection if not vaccinated. The model is a methodological advance in that it provides an economic rationale for traditional threshold models of vaccine decision-making that are commonly used in agent-based network models of vaccine choice. In the model, more dense urban regions have more connections between agents than less dense rural regions. Higher density leads to higher levels of vaccine usage and lower rates of infection in urban regions within the model. This finding adds to the more commonly discussed socio-economic reasons for higher levels of vaccination usage in urban areas compared to rural areas. In addition to this direct contribution, the paper emphasizes the importance of endogenous decision-making in models of epidemiology. For instance, we find that networks that lead to larger epidemics in exogenous vaccination models lead to smaller epidemics in our model because agents use vaccinations to offset the additional risk introduced by these network structures. Endogenous agent responses to risk need to be incorporated into theoretical and empirical models of economic epidemiology.

Although various indicators of economic sentiment are often assessed in macroeconomic studies, the generating process of economic sentiment itself is still a puzzle. This paper offers pioneer evidence on the persistence of economic sentiment. Applying a battery of fractional integration tests on the European Economic Sentiment Indicator (ESI) of all individual EU member states, we reveal that ESI is dominantly a long-memory process. This finding is robust across several estimators, and it fairly contradicts the conventional wisdom of ESI as a purely transitory macroeconomic shock. Further on, this is true for both core EU economies and new member states, although the later ones exhibit slightly longer memory. Finally, we reveal that the end of the Great Moderation era has increased ESI's persistence, but the effect is only marginal. As it seems, a series of macroeconomic turbulences recorded after the global financial crisis has not initiated a significant shift in agents' collective memory and ESI will likely keep its pivotal role in governing business cycles in the future.

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

Overtourism is an increasingly relevant problem for tourist destinations, and some cities are starting to take extreme measures to counter it. In this paper, we introduce a simple mathematical model that analyzes the dynamics of the populations of residents and tourists when there is a competition for the access to local services and resources, since the needs of the two populations are partly mutually incompatible. We study under what conditions a stable equilibrium where residents and tourists coexist is reached, and what are the conditions for tourists to take over the city and to expel residents, among others. Even small changes in key parameters may bring about very different outcomes. Policymakers should be aware that a sound knowledge of the structural properties of the dynamics is important when taking measures, whose effect could otherwise be different than expected and even counterproductive.

The paper presents a medium-run growth model driven by autonomous demand, where aggregate demand and supply interact and unemployment is present and plays different roles. In particular, it generates a feedback from supply to aggregate demand rooted in the presence of heterogeneous consumers and an uncertain environment. Two are the main consequences of this approach. The first is that multiple equilibria can be generated. The second is that equilibria may have different stability properties. In this perspective, growth becomes a dynamic process where initial conditions matter and history plays an important role.

This paper considers various types of forecast heuristics to examine the effects of boundedly rational agents on macroeconomic dynamics. Given the baseline New Keynesian model, we seek to find the expectation formation process that is most suitable in describing economic adjustments over the business cycle. In particular, impulse response analysis is used to compare the performances of the macroeconomic model under bounded rationality and under rational expectations. The results show that the fluctuations in consumer confidence mainly explain the degree of persistence in consumption. We conclude that a model under bounded rationality with a heuristic-induced switching process can qualitatively provide a good fit to the data that is equivalent to a model under rational expectations.

We use a newly collected data set coupled with an agent-based model to study the spread of infectious disease in hospitals. We estimate the average and marginal infections created by various worker groups in a hospital as a function of their network position in order to identify groups most crucial in a hospital-based epidemic. Surprisingly, we find that many groups with primary patient care responsibilities play a small role in spreading an infectious disease within our hospital data set. We also demonstrate that the effect of different network positions can be as important as the effect of different transmission rates for some categories of workers.

Most of the theoretical contributions on the relationship between economy and environment assume the environment as a good distributed homogeneously among agents. The aim of this work is to relax this hypothesis and to consider that the environment can have a local character even if conditioned through externalities by the choices made at the global level. In this article, we adapt the classical framework introduced in John and Pecchenino (Econ J 104(427):1393-1410, 1994) to analyze the dynamic relationship between environment and economic process, and we propose an OLG agent-based model where each agent perceives her own level of environmental quality determined by her own decisions, and by the decisions of those living around her. Despite the attention devoted to local environmental aspects, network externalities (determined through the scheme of Moore neighborhoods) play a fundamental role in defining environmental dynamics and they may induce the emergence of cyclical dynamics. The occurrence of oscillations in the local environmental quality is partially mitigated by the presence of heterogeneity in individuals' preferences. Finally, when a centralized planner is introduced, the dynamics converge to stationary values regardless of the assumption on heterogeneity of agents.