Nonzero-sum stochastic differential games with impulse controls offer a realistic and far-reaching modelling framework for applications within finance, energy markets and other areas, but the difficulty in solving such problems has hindered their proliferation. Semi-analytical approaches make strong assumptions pertaining very particular cases. To the author's best knowledge, the only numerical method in the literature is the heuristic one we put forward to solve an underlying system of quasi-variational inequalities. Focusing on symmetric games, this paper presents a simpler and more efficient fixed-point policy-iteration-type algorithm which removes the strong dependence on the initial guess and the relaxation scheme of the previous method. A rigorous convergence analysis is undertaken with natural assumptions on the players strategies, which admit graph-theoretic interpretations in the context of weakly chained diagonally dominant matrices. A provably convergent single-player impulse control solver, often outperforming classical policy iteration, is also provided. The main algorithm is used to compute with high precision equilibrium payoffs and Nash equilibria of otherwise too challenging problems, and even some for which results go beyond the scope of all the currently available theory.

We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missing in most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise, permafrost thaw and tipping points. We study optimal policies under cooperation and noncooperation between two regions (the North and the Tropic-South) in the face of risks and recursive utility. We introduce a new general computational algorithm to find feedback Nash equilibrium. Our results suggest that when the elements of climate science are ignored, important policy variables such as the optimal regional carbon tax and adaptation could be seriously biased. We also find the regional carbon tax is significantly smaller in the feedback Nash equilibrium than in the social planner's problem in each region, and the North has higher carbon taxes than the Tropic-South.

We consider an environment where a finite number of players need to decide whether to buy a certain product (or adopt a trend) or not. The product is either good or bad, but its true value is not known to the players. Instead, each player has her own private information on the quality of the product. Each player can observe the previous actions of other players and estimate the quality of the product. A player can only buy the product once. In contrast to the existing literature on informational cascades, in this work players get more than one opportunity to act. In each turn, a player is chosen uniformly at random from all players and can decide to buy or not to buy. Her utility is the total expected discounted reward, and thus myopic strategies may not constitute equilibria. We provide a characterization of structured perfect Bayesian equilibria (sPBE) with forward-looking strategies through a fixed-point equation of dimensionality that grows only quadratically with the number of players. In particular, a sufficient state for players' strategies at each time instance is a pair of two integers, the first corresponding to the estimated quality of the good and the second indicating the number of players that cannot offer additional information about the good to the rest of the players. Based on this characterization we study informational cascades for the two cases of infinitely patient and not infinitely patient players. We show that for not infinitely patient players, informational cascades happen with high probability for a large number of players. Furthermore, only a small portion of the total information in the system is revealed before a cascade occurs. In addition, we show that for infinitely patient players, bad informational cascades can be avoided in some states of the world.

This study is a detailed analysis of Speculation Game, a minimal agent-based model of financial markets, in which the round-trip trading and the dynamic wealth evolution with variable trading volumes are implemented. Instead of herding behavior, we find that the emergence of volatility clustering can be induced by the heterogeneous wealth distribution among traders. In particular, the spontaneous redistribution of market wealth through repetitions of round-trip trades can widen the wealth disparity and establish the Pareto distribution of the capital size. In the meantime, large fluctuations in price return are brought on by the intermittent placements of the relatively big orders from rich traders. Empirical data are used to support the scenario derived from the model.

Within the well-known framework of financial portfolio optimization, we analyze the existing relationships between the condition of arbitrage and the utility maximization in presence of insider information. That is, we assume that, since the initial time, the information flow is altered by adding the knowledge of an additional random variable including future information. In this context we study the utility maximization problem under the logarithmic and the Constant Relative Risk Aversion (CRRA) utilities, with and without the restriction of no temporary-bankruptcy. For the latter case we obtain an optimal strategy different from the one computed in Pikovsky and Karatzas. We give various examples for which the insider information create arbitrage, and for which the logarithmic maximization problem is bounded or unbounded. We conclude with an interesting result, showing that the insider information may not lead to any arbitrage.

Labour networks, where industries are connected based on worker transitions, have been previously deployed to study the evolution of industrial structure ('related diversification') across cities and regions. Beyond estimating skill-overlap between industry pairs, such networks characterise the structure of inter-industry labour mobility and knowledge diffusion in an economy. Here we investigate the structure of the network of inter-industry worker flows in the Irish economy, seeking to identify groups of industries exhibiting high internal mobility and skill-overlap. We argue that these industry clusters represent skill basins in which skilled labour circulate and diffuse knowledge, and delineate the size of the skilled labour force available to an industry.

Deploying a multi-scale community detection algorithm, we uncover a hierarchical modular structure composed of clusters of industries at different scales. At one end of the scale, we observe a macro division of the economy into services and manufacturing. At the other end of the scale, we detect a fine-grained partition of industries into tightly knit groupings. In particular, we find workers from finance, computing, and the public sector rarely transition into the extended economy. Hence, these industries form isolated clusters which are disconnected from the broader economy, posing a range of risks to both workers and firms. Finally, we develop a methodology based on industry growth patterns to reveal the optimal scale at which labour pooling operates in terms of skill-sharing and skill-seeking within industry clusters.

The family of admissible positions in a transaction costs model is a random closed set, which is convex in case of proportional transaction costs. However, the convexity fails, e.g. in case of fixed transaction costs or when only a finite number of transfers are possible. The paper presents an approach to measure risks of such positions based on the idea of considering all selections of the portfolio and checking if one of them is acceptable. Properties and basic examples of risk measures of non-convex portfolios are presented.

The portfolio are a critical factor not only in risk analysis, but also in insurance and financial applications. In this paper, we consider a special class of risk statistics from the perspective of regulator. This new risk statistic can be uesd for the quantification of portfolio risk. By further developing the properties related to regulator-based risk statistics, we are able to derive dual representation for them. Finally, examples are also given to demonstrate the application of this risk statistic.

We consider robust pricing and hedging for options written on multiple assets given market option prices for the individual assets. The resulting problem is called the multi-marginal martingale optimal transport problem. We propose two numerical methods to solve such problems: using discretisation and linear programming applied to the primal side and using penalisation and deep neural networks optimisation applied to the dual side. We prove convergence for our methods and compare their numerical performance. We show how adding further information about call option prices at additional maturities can be incorporated and narrows down the no-arbitrage pricing bounds. Finally, we obtain structural results for the case of the payoff given by a weighted sum of covariances between the assets.

In their seminal work on systemic risk in financial markets, Eisenberg and Noe proposed and studied a model with $n$ firms embedded into a network of debt relations. We analyze this model from a game-theoretic point of view. Every firm is a rational agent in a directed graph that has an incentive to allocate payments in order to clear as much of its debt as possible. Each edge is weighted and describes a liability between the firms. We consider several variants of the game that differ in the permissible payment strategies. We study the existence and computational complexity of pure Nash and strong equilibria, and we provide bounds on the (strong) prices of anarchy and stability for a natural notion of social welfare. Our results highlight the power of financial regulation -- if payments of insolvent firms can be centrally assigned, a socially optimal strong equilibrium can be found in polynomial time. In contrast, worst-case strong equilibria can be a factor of $\Omega(n)$ away from optimal, and, in general, computing a best response is an NP-hard problem. For less permissible sets of strategies, we show that pure equilibria might not exist, and deciding their existence as well as computing them if they exist constitute NP-hard problems.

With the rapid development of Internet finance, a large number of studies have shown that Internet financial platforms have different financial systemic risk characteristics when they are subject to macroeconomic shocks or fragile internal crisis. From the perspective of regional development of Internet finance, this paper uses t-SNE machine learning algorithm to obtain data mining of China's Internet finance development index involving 31 provinces and 335 cities and regions. The conclusion of the peak and thick tail characteristics, then proposed three classification risks of Internet financial systemic risk, providing more regionally targeted recommendations for the systematic risk of Internet finance.

In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in TSE is different depending on their attributes. Moreover, we indicate that for predicting the closing price only comments volume and for predicting the daily return both the volume and the sentiment of the comments could be useful. We demonstrate that Users' Trust coefficients have different behaviors toward the three stocks.

Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters or importers) and industries. A bipartite representation of trade, however, inevitably neglects transactions between industries. To fully capture the organization of the global value chain, we draw on the World Input-Output Database and construct a multi-layer network in which the nodes are the countries, the layers are the industries, and links can be established from sellers to buyers within and across industries. We define the buyers' and sellers' participation matrices in which the rows are the countries and the columns are all possible pairs of industries, and then compute nestedness based on buyers' and sellers' involvement in transactions between and within industries. Drawing on appropriate null models that preserve the countries' or layers' degree distributions in the original multi-layer network, we uncover variations of country- and transaction-based nestedness over time, and identify the countries and industries that most contributed to nestedness. We discuss the implications of our findings for the study of the international production network and other real-world systems.

A methodology is presented to rank universities on the basis of the lists of programmes the students applied for. We exploit a crucial feature of the centralised assignment system to higher education in Hungary: a student is admitted to the first programme where the score limit is achieved. This makes it possible to derive a partial preference order of each applicant. Our approach integrates the information from all students participating in the system, is free of multicollinearity among the indicators, and contains few ad hoc parameters. The procedure is implemented to rank faculties in the Hungarian higher education between 2001 and 2016. We demonstrate that the ranking given by the least squares method has favourable theoretical properties, is robust with respect to the aggregation of preferences, and performs well in practice. The suggested ranking is worth considering as a reasonable alternative to the standard composite indices.

Sustainable development is a worldwide recognized social and political goal, discussed in both academic and political discourse and with much research on the topic related to sustainable development in higher education. Since mental models are formed more effectively at school age, we propose a new way of thinking that will help achieve this goal. This paper was written in the context of Russia, where the topic of sustainable development in education is poorly developed. The authors used the classical methodology of the case analysis. The analysis and interpretation of the results were conducted in the framework of the institutional theory. Presented is the case of Ural Federal University, which has been working for several years on the creation of a device for the purification of industrial sewer water in the framework of an initiative student group. Schoolchildren recently joined the program, and such projects have been called university-to-school projects. Successful solutions of inventive tasks contribute to the formation of mental models. This case has been analyzed in terms of institutionalism, and the authors argue for the primacy of mental institutions over normative ones during sustainable society construction. This case study is the first to analyze a partnership between a Federal University and local schools regarding sustainable education and proposes a new way of thinking.