An agent-based model for financial vulnerability

This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the ensuing agent-based model for analyzing the vulnerability of the financial system to asset- and funding-based fire sales. The model captures the dynamic interactions of agents in the financial system extending from the suppliers of funding through the intermediation and transformation functions of the bank/dealers to the financial institutions that use the funds to trade in the asset markets. The model replicates the key finding that it is the reaction to initial losses, rather than the losses themselves, that determine the extent of a crisis. By building on a detailed mapping of the transformations and dynamics of the financial system, the agent-based model provides an avenue toward risk management that can illuminate the pathways for the propagation of key crisis dynamics such as fire sales and funding runs.     

From agent-based modeling to actor-based reactive systems in the analysis of financial networks

We present a new framework for the analysis of financial networks, called Actor-based Reactive Systems (ARS), that pushes further the Agent-Based approach (ABM) by resorting to ideas coming from the study of distributed systems in computer science. Two distinctive features, namely a fundamentally different management of time and a fully decentralized control logic, have a profound impact in terms of expressiveness of analysis, flexibility of modeling, and efficiency of experimentation. To illustrate the feasibility of the framework, we develop a realistic case study by analyzing the systemic risk of a model of the European banking network with a nontrivial contagion procedure, that combines an initial asset shock with the negative feedback loop triggered by asset fire sales. We show that, compared to ABMs, ARSs bring about finer-grained analyses, with a greater degree of heterogeneity and adaptivity of economic agents. Moreover, the very low computational cost and the detailed account of the system’s execution support the design and the development of very flexible stress tests to rapidly experiment with many hypothetical scenarios in a test-oriented style.

     

Information versus imitation in a real-time agent-based model of financial markets

This paper presents an agent based model of a financial market with a real-time engine, whose operation replicates the official time schedule of Borsa Italiana S.p.A. Simulated time series are compared with empirical data at different time scales (ticks, 1 s, 1 min, 5 min) in order to check the compliance of the model with some stylized facts. The modeled market structure is a dynamic multiplex with two layers: the first one is a star network, whose hub is the market maker (i.e., the owner of the venue holding the order book), where transactions are executed; the second one is designed according to different topologies, representing social interactions, where investors decide their behavior according to informative flows. The effect of imitation on market stability is discussed and some policy implications are provided.

     

Convex incentives in financial markets: an agent-based analysis

We investigate whether convex incentive contracts are a source of instability of financial markets as indicated by the results of a continuous double-auction asset market experiment performed by Holmen et al. (J Econ Dyn Control 40:179–194, 2014). We develop a model to replicate the setting of the experiment and perform an agent-based simulation where agents have linear or convex incentives. Extending the simulation by varying features of actual asset markets that were not studied in the experiment, our main results show that increasing the number of convex incentive contracts increases prices and volatility and decreases market liquidity, measured both as bid–ask spreads and volumes. We also observe that the influence of risk aversion on traders’ decisions decreases when there are convex contracts and that increasing the differences in initial wealth among the traders has similar effects as increasing number of convex incentive contracts.     

An agent-based early warning indicator for financial market instability

Inspired by the Bank of America Merrill Lynch global breath rule, we propose an investor sentiment index based on the collective movement of stock prices in a given market. We show that the time evolution of the sentiment index can be reasonably described by the herding model proposed by Kirman in his seminal paper “Ants, rationality and recruitment” (Kirman in Q J Econ 108:137–156, 1993). The correspondence between the index and the model allowed us to easily estimate its parameters. Based on the model and the empirical evolution of the sentiment index, we propose an early warning indicator able to identify optimistic and pessimistic phases of the market. As a result, investors and policy-makers can set different strategies anticipating financial market instability. Investors can reduce the risk of their portfolio while policy-makers can set more efficient policies to avoid the effects of financial instability on the real economy. The validity of our results is supported by means of a robustness analysis showing the application of the early warning indicator in eight different worldwide stock markets.

     

Systemic financial risk indicators and securitised assets: an agent-based framework

The paper presents an agent-based model of a credit economy which includes a securitisation process and a bailout mechanism for bank bankruptcies. Within this framework, banks are able to sell mortgages to a financial vehicle corporation, which finances its activity by creating mortgage-backed securities and selling them to a mutual fund. In turn, the mutual fund collects liquidity by selling shares to households and remunerates them with a monthly interest. The impact of this mechanism is analysed by means of computational experiments for different levels of banks’ securitisation propensity. Furthermore, we study a set of systemic risk indicators which have the aim of assessing the imbalances in the financial system. Two of them are the mortgage-to-GDP ratio and the capital adequacy ratio, which are constructed to detect only the on-balance sheet changes in banks’ credit exposure. We consider two additional indicators, similar to the previous ones with the only difference that they are also able to account for the off-balance sheet items. Moreover, we adopt an indicator, the so-called “virtuous–unvirtuous cycle” indicator, which, besides off-balance assets, targets also the GDP. The results show that higher securitisation propensity weakens the financial stability of banks with relevant effects on different sectors of the economy. Most importantly, the analysis of systemic risk reveals the important issue of designing suitable systemic risk indicators for predicting incoming financial crises, finding that an essential feature of these indicators should be to integrate banks’ off-balance sheet assets.

     

An agent-based model of payment systems

We lay out and simulate a multi-agent, multi-period model of an RTGS payment system. At the beginning of the day, banks choose how much costly liquidity to allocate to the settlement process. Then, they use it to execute an exogenous, random stream of payment orders. If a bank's liquidity stock is depleted, payments are queued until new liquidity arrives from other banks, imposing costs on the delaying bank. We study the equilibrium level of liquidity posted in the system, performing some comparative statics and obtaining insights on the efficiency of alternative system configurations.     

Systemic risk mitigation in financial networks

We propose a multi-period clearing framework, where the level of systemic risk is mitigated through the provision of liquidity assistance. The interbank liability network evolves stochastically over time, and assets of defaulted banks are sold to qualified banks within the network through a first-price sealed-bid auction. We find that policies targeting systemically important banks are more effective in core-periphery network structures, whereas those maximizing the total liquidity in the system are preferred in random network configurations. We assess sensitivity of systemic risk to variations in interbank liabilities as well as to their correlation structure.     

Forecasting systemic impact in financial networks

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. So-called systemic risk betas account for a company’s position within the network of financial interdependencies, in addition to its balance sheet characteristics and its exposure to general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast the systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients.     

Recent advances in multiple-objective design strategies

Many experiments have several goals and it is important to incorporate these goals at the onset of the study. Design strategies for accomplishing this task are reviewed with examples from the biological sciences.     

The economics of netting in financial networks

Journal of Economic Interaction and Coordination - Can the netting of on-balance-sheet interbank assets and liabilities be useful in thwarting financial contagion during a systemic crisis episode?...     

A network agent-based model of ethnocentrism and intergroup cooperation

Quality & Quantity - We present a network agent-based model of ethnocentrism and intergroup cooperation in which agents from two groups (majority and minority) change their communality (feeling...     

Itchy feet vs cool heads: Flow of funds in an agent-based financial market

Investors tend to move funds when they are unhappy with their current portfolio managers׳ performance. We study the effect of the size of this flow of funds in an agent-based model of the financial market. The model combines the discrete choice approach from agent-based modelling, where all capital is mobile, with the evolutionary finance framework where all growth is endogenous. Our results show that, if investors exhibit recency bias in evaluating portfolio managers׳ performance, even a small amount of freely flowing capital has a huge impact on the market dynamics and the survival of noise traders. We also find that investors׳ intensity of choice is a driving force for excess volatility and extreme price movements when the size of the flow of funds is large.     

A functional perspective on financial networks

The financial sector is a critical component of any economic system, as it delivers key qualitative asset transformation services in terms of liquidity, maturity and volume. Although these functions could in principle be carried out separately by specialized actors, in the end it is their systemic co-evolution that determines how the aggregate economy performs and withstands disruptions. In this paper we argue that a functional perspective on financial intermediation can be usefully employed to investigate the functioning of financial networks. We do this in two steps. First, we use previously unreleased data to show that focusing on the economic functions performed over time by the different institutions exchanging funds in an interbank market can be informative, even if the underlying topological structure of their relations remains constant. Second, a set of alternative artificial histories are generated and stress-tested by using real data as a calibration base, with the aim of performing counterfactual welfare comparisons among different topological structures.     

普鲁兰多糖应用进展研究状况

普鲁兰是一种有许多特点的生物聚合物,具有成膜性好、无毒、安全等特点,现已广泛用于食品加工业、环保行业、化妆品行业等,应用前景非常广阔。     

An agent-based model of the observed distribution of wealth in the United States

Pareto cautiously asserted that the wealth and income distributions which bear his name are universal, basing his argument on observations of this distribution in many different types of economies. In this paper, we present an agent based model (and a scalable approximation of it) in a closely related spirit. The central feature of this model is that wealth enables an individual to secure more wealth. Specifically, the important and novel feature of this model is its ability to simultaneously produce both the Pareto distribution observed in empirical data for the top 10% of the population and the exponential distribution observed for the lower 90%. We show that the model produces these distributions of wealth when initialized with an equitable distribution. Then, using historical data, we initialize the model with US wealth shares in 1988 and show that the model tracks wealth share changes from 1988 to 2012. Simulations to 2088 project that the top 0.01% of the population will possess more than 70% of the total wealth in the economy.     

Learning to save in a voluntary pension system: toward an agent-based model

Mandatory pension systems partially replace old-age income, therefore the government matches additional life-cycle savings in a voluntary pension system. Though the individual saving decisions are apparently independent, the earmarked taxes (paid to finance the matching) connect them. Previous models either neglected the endogenous tax expenditures (e.g. Choi et al., in: Wise (ed) Perspectives in the economics of aging, University of Chicago Press, Chicago, pp 81–121, 2004) or assumed very sophisticated saving strategies (e.g. Fehr et al. in FinanzArchiv Pub Finance Anal 64:171–198, 2008). We create twin models: myopic workers learn (i) from farsighted workers using public information (analytic model) and (ii) also from each other (agent-based model). These models provide more realistic results on saving behavior and the impact of matching on the income redistribution than the earlier models.

     

Capability-based governance patterns over the product life-cycle: an agent-based model

In recent literature, there is disagreement over the temporal pattern of vertical governance of firms over the product life-cycle. We use a novel neo-Schumpeterian agent-based simulation model to investigate emerging patterns of vertical governance for different levels of imitability and substitutability of capabilities. We find that, in the mature phase of the product life-cycle, firms generally prefer vertical specialization. However, in the early phase, imitability and substitutability, in interplay, determine the governance form preferred. High imitability frustrates appropriation and thereby discourages integration for synergistic advantages. However, firms need not vertically specialize: under low substitutability, incompatibilities reduce the advantages of specialization. When both substitutability and imitability are low, firms can appropriate the value of their inventions and there is no combinatorial advantage of specialization, so firms predominantly integrate. If substitutability is high and imitability is low, the combinatorial advantage of specialization balances with the synergistic advantage of integration.     

Discovering latent commercial networks from online financial news articles

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.