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on Network Economics |
| By: | Celso Brunetti; Jeffrey H. Harris; Shawn Mankad |
| Abstract: | Network analysis has demonstrated that interconnectedness among market participants results in spillovers, amplifies or absorbs shocks, and creates other nonlinear effects that ultimately affect market health. In this paper, we propose a new directed network construct, the liquidity network, to capture the urgency to trade by connecting the initiating party in a trade to the passive party. Alongside the conventional trading network connecting sellers to buyers, we show both network types complement each other: Liquidity networks reveal valuable information, particularly when information asymmetry in the market is high, and provide a more comprehensive characterization of interconnectivity in the overnight-lending market. |
| Keywords: | Banking networks; Interconnectedness; Liquidity |
| JEL: | G10 G20 C10 |
| Date: | 2021–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2021-17&r= |
| By: | Barnett, William A.; Wang, Xue; Xu, Hai-Chuan; Zhou, Wei-Xing |
| Abstract: | We model hierarchical cascades of failures among banks linked through an interdependent network. The interaction among banks include not only direct cross-holding, but also indirect dependency by holding mutual assets outside the banking system. Using data extracted from the European Banking Authority, we present the interdependency network composed of 48 banks and 21 asset classes. Since interbank exposures are not public, we first reconstruct the asset/liability cross-holding network using the aggregated claims. For the robustness, we employ 3 reconstruction methods, called Anan, Hała and Maxe. Then we combine the external portfolio holdings of each bank to compute the interdependency matrix. The interdependency network is much more dense than the direct cross-holding network, showing the complex latent interaction among banks. Finally, we perform macroprudential stress tests for the European banking system, using the adverse scenario in EBA stress test as the initial shock. For different reconstructed networks, we illustrate the hierarchical cascades and show that the failure hierarchies are roughly the same except for a few banks, reflecting the overlapping portfolio holding accounts for the majority of defaults. Understanding the interdependency network and the hierarchy of the cascades should help to improve policy intervention and implement rescue strategy. |
| Keywords: | financial network, interdependent network, contagions, stress test, macroprudential |
| JEL: | D85 G01 G21 G32 G33 |
| Date: | 2021–06–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:108421&r= |
| By: | Ryan Kor; Junjie Zhou |
| Abstract: | We study the optimal welfare maximising intervention in a multiple activity network model taking into account both within-activity network spillovers and cross-activity interdependences. We analyse the effects of a restriction in the intervention space, by limiting the activities which the planner can intervene in, and show that the impact of a restriction greatly differs depending on the interdependency between activities. When the planner's budget is large, the percentage loss in welfare is significant when activities are complements, while there is asymptotically no loss in welfare when the activities are substitutes and the planner can intervene in at least two activities. When the budget is small, there are instead diminishing returns in the dimensions of intervention. For each case, we obtain approximations of the optimal interventions in terms of the eigenspaces of the network matrix, and provide a simple choice of intervention for the planner that achieves an asymptotically equal welfare. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.09410&r= |
| By: | Matjašič, Miha; Cugmas, Marjan; Žiberna, Aleš |
| Abstract: | This paper presents the R package blockmodeling which is primarily meant as an implementation of generalized blockmodeling (more broadly blockmodeling) for valued networks where the values of the ties are assumed to be measured on at least interval scale. Blockmodeling is one of the most commonly used approaches in the analysis of (social) networks, which deals with the analysis of relationships or connections, between the units studied (e.g., peoples, organizations, journals etc.). The R package blockmodeling implements several approaches for the generalized blockmodeling of binary and valued networks. Generalized blockmodeling is commonly used to cluster nodes in a network with regard to the structure of their links. The theoretical foundations of generalized blockmodeling for binary and valued networks are summarized in the paper while the use of the R package blockmodeling is illustrated by applying it to an empirical dataset. |
| Date: | 2021–06–06 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:b8cxp&r= |
| By: | Hideaki Aoyama |
| Abstract: | XRP is a modern crypto-asset (crypto-currency) developed by Ripple Labs, which has been increasing its financial presence. We study its transaction history available as ledger data. An analysis of its basic statistics, correlations, and network properties are presented. Motivated by the behavior of some nodes with histories of large transactions, we propose a new index: the ``Flow Index.'' The Flow Index is a pair of indices suitable for characterizing transaction frequencies as a source and destination of a node. Using this Flow Index, we study the global structure of the XRP network and construct bow-tie/walnut structure. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.10012&r= |
| By: | Yun Kuen Cheung; Stefanos Leonardos; Georgios Piliouras; Shyam Sridhar |
| Abstract: | We study a game-theoretic model of blockchain mining economies and show that griefing, a practice according to which participants harm other participants at some lesser cost to themselves, is a prevalent threat at its Nash equilibria. The proof relies on a generalization of evolutionary stability to non-homogeneous populations via griefing factors (ratios that measure network losses relative to deviator's own losses) which leads to a formal theoretical argument for the dissipation of resources, consolidation of power and high entry barriers that are currently observed in practice. A critical assumption in this type of analysis is that miners' decisions have significant influence in aggregate network outcomes (such as network hashrate). However, as networks grow larger, the miner's interaction more closely resembles a distributed production economy or Fisher market and its stability properties change. In this case, we derive a proportional response (PR) update protocol which converges to market equilibria at which griefing is irrelevant. Convergence holds for a wide range of miners risk profiles and various degrees of resource mobility between blockchains with different mining technologies. Our empirical findings in a case study with four mineable cryptocurrencies suggest that risk diversification, restricted mobility of resources (as enforced by different mining technologies) and network growth, all are contributing factors to the stability of the inherently volatile blockchain ecosystem. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12332&r= |
| By: | Beni Egressy; Roger Wattenhofer |
| Abstract: | We consider networks of banks with assets and liabilities. Some banks may be insolvent, and a central bank can decide which insolvent banks, if any, to bail out. We view bailouts as an optimization problem where the central bank has given resources at its disposal and an objective it wants to maximize. We show that under various assumptions and for various natural objectives this optimization problem is NP-hard, and in some cases even hard to approximate. Furthermore, we also show that given a fixed central bank bailout objective, banks in the network can make new debt contracts to increase their own market value in the event of a bailout (at the expense of the central bank). |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.12315&r= |
| By: | Kazi Abdul, Mannan; Khandaker Mursheda, Farhana; G M Omar Faruque, Chowdhury |
| Abstract: | This paper examines ethical and behavioral aspects of taxpayers, the financial condition of citizens, tax fairness, taxpayer services, complexities in the tax regime, tax rates, penalties and enforcement, and tax amnesties and the black economy. Primary data were collected by conducting a survey utilizing structured printed questionnaires. Secondary data were collected from project reports, government publications and documents, books, journals, reports, newspapers and electronic media. Empirical findings suggest that all these issues are associated with tax evasion in Bangladesh. We also find that eligibility in a social network increases the likelihood that others will take-up. This suggests that taxpayers affect each other’s decisions about tax avoidance, highlighting the importance of accounting for social interactions in understanding enforcement and tax avoidance behavior, and providing a concrete example of optimization frictions in the context of behavioral responses to taxation. The involvement and nexus of the three actors in tax policy formulation, implementation and compliance processes were examined. The empirical findings indicate the presence of this nexus which facilitates tax evasion. The high magnitude of tax evasion in Bangladesh is significantly acknowledged by respondents in the study. The empirical findings suggest that the absence of a participatory policy making process, lack of research into, and reform of, the tax system, short-term oriented and politically motivated tax policies, loopholes, anomalies and complexities of tax laws and policies are responsible for creating scope for tax evasion. |
| Keywords: | Taxation, Social Network, Tax Evasion, Tax Avoidance, Network Centrality, Optimal Auditing, Network Model |
| JEL: | H2 H20 H21 H22 H24 H26 H7 H75 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:108279&r= |
| By: | Florian Eckerli; Joerg Osterrieder |
| Abstract: | Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field. |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2106.06364&r= |