Business fluctuations in a behavioral switching model: Gridlock effects and credit crunch phenomena in financial networks

In this paper we characterize the evolution over time of a credit network in the most general terms as a system of interacting banks and firms operating in a three-sector economy with goods, credit and interbank market. Credit connections change over time via an evolving fitness measure depending from lenders’ supply of liquidity and borrowers’ demand of credit. Moreover, an endogenous learning mechanism allows agents to switch between a loyal or a shopping-around strategy according to their degree of satisfaction. The crucial question we investigate is how financial bubbles and credit-crunch phenomena emerge from the implemented mechanism

From banks' strategies to financial (in)stability

This paper aims to shed light on the emergence of systemic risk in credit systems. By developing an interbank market with heterogeneous financial institutions granting loans on different network structures, we investigate which market architecture is more resilient to liquidity shocks and how the risk spreads over the modeled system. In our model, credit linkages evolve endogenously via a fitness measure based on different banks' strategies. Each financial institution, in fact, applies a strategy based on a low interest rate, a high supply of liquidity or a combination of them. Interestingly, the choice of the strategy influences both the banks' performance and the network topology. In this way, we are able to identify the most effective tactics adapt to contain contagion and the corresponding network topology. Our analysis shows that, when financial institutions combine the two strategies, the interbank network does not condense and this generates the most efficient scenario in case of shocks.The research leading to these results has received funding from the European Union, Seventh Framework Programme FP7, under grant agreement MATHEMACS, n0 : 318723 and FinMaP n0 : 612955. The authors are grateful for funding this research from the Universitat Jaume I under the project P11B2012 27

Interaction in agent-based economics: A survey on the network approach

In this paper we aim to introduce the reader to some basic concepts and instruments used in a wide range of economic networks models. In particular, we adopt the theory of random networks as the main tool to describe the relationship between the organization of interaction among individuals within different components of the economy and overall aggregate behavior. The focus is on the ways in which economic agents interact and the possible consequences of their interaction on the system. We show that network models are able to introduce complex phenomena in economic systems by allowing for the endogenous evolution of networks

From banks' strategies to financial (in)stability

This paper aims to shed light on the emergence of systemic risk in credit systems. By developing an interbank market with heterogeneous financial institutions granting loans on different network structures, we investigate what market architecture is more resilient to liquidity shocks and how the risk spreads over the modeled system. In our model, credit linkages evolve endogenously via a fitness measure based on different banks strategies. Each financial institution, in fact, applies a strategy based on a low interest rate, a high supply of liquidity or a combination of them. Interestingly, the choice of the strategy in uences both the banks' performance and the network topology. In this way, we are able to identify the most effective tactics adapt to contain contagion and the corresponding network topology. Our analysis shows that, when financial institutions combine the two strategies, the interbank network does not condense and this generates the most efficient scenario in case of shocks

From bond yield to macroeconomic instability: A parsimonious affine model

We present a hybrid Heston model with a common stochastic volatility to describe government bond yield dynamics. The model is analytically tractable and, therefore, can be efficiently estimated using the maximum likelihood approach and a specific expansion in order to cope with the curse of dimensionality. Twofold is the model contribution. First, it captures changes in the yield volatility and predict future yield values of Germany, French, Italy and Spain. The result is an early-warning indicator which anticipates phases of instability characterizing the time series investigated. Then, the model describes convergence/divergence phenomena among European government bond yields and explores the countries’ reactions to a common monetary policy described through the EONIA interbank rate. We also investigate the potential of this indicator on U.S. data (treasury bills).The research leading to these results has received funding from the European Union, Seventh Framework Programme FP7, under grant agreement FinMaP no. 612955

Bitcoin: Bubble that bursts or Gold that glitters?

This paper aims to shed light on the 2017 Bitcoin bubble. Firstly, by applying the dynamic time warping algorithm, we identify among several financial instruments a subsample of five assets with similar characteristics to the cryptocurrency bubble. Interestingly, among the fluctuations characterizing these assets, the algorithm shows a close affinity between the Bitcoin bubble and the 2000 NASDAQ Dotcom one. Once the subsample is identified, we study the (de)synchronization among these assets via the wavelet coherence approach. Although Bitcoin is poorly correlated with the other indices, given its scarce connection with the real economy, we observe switching phenomena among these instruments. A more careful study on these portfolio reallocations, conducted via an event study analysis, reveals that traders seem to redirect capital from stock markets and gold to Bitcoin in case of positive events of the cryptocurrency

A calibration procedure for analyzing stock price dynamics in an agent-based framework

In this paper we introduce a calibration procedure for validating of agent based models. Starting from the well-known financial model of (Brock and Hommes, 1998), we show how an appropriate calibration enables the model to describe price time series. We formulate the calibration problem as a nonlinear constrained optimization that can be solved numerically via a gradient-based method. The calibration results show that the simplest version of the Brock and Hommes model, with two trader types, fundamentalists and trend-followers, replicates nicely the price series of four different markets indices: the S&P 500, the Euro Stoxx 50, the Nikkei 225 and the CSI 300. We show how the parameter values of the calibrated model are important in interpreting the trader behavior in the different markets investigated. These parameters are then used for price forecasting. To further improve the forecasting, we modify our calibration approach by increasing the trader information set. Finally, we show how this new approach improves the model׳s ability to predict market prices

Bank's strategies during the financial crisis

In this paper we introduce a calibration procedure suitable for the validation of agent based models. Starting from the well-known financial model of Brock and Hommes 1998, we show how an appro- priate calibration technique makes the model able to describe price time series.The calibration results show that the simplest version of the Brock and Hommes model, with two trader types, fundamentalists and trend-followers, well replicates the price series of four sub-sectoral banking indexes, representing different geographical areas. Moreover, we show how the parameter values of the calibrated model are important to analyse the trader behavior on the different investigated markets

Taming financial systemic risk: models, instruments and early warning indicators

In recent decades, most advanced and developing economies have suffered—or are still suffering—from profound and repeated crises. The literature has reflected on the determinants of these perturbations by placing particular emphasis on the malfunctioning of either the real or financial sphere of the economy. The main research question has been to understand whether it was the real economy that perturbed finance sectors or, alternatively, the financial/credit market that depressed real production. Whatever the direction of the causality nexus and, consequently the origin of the attack, with some studies identifying the direction from real markets to financial sectors (see Bernanke and Gertler 1989; Greenwald and Stiglitz 1993; Delli Gatti et al. 2012) and others reversing it (see Christiano and Ikeda 2011; Brunnermeier et al. 2012), what is certainly undoubted is the self-reinforcing interaction between the two sectors, which translates into booms followed by busts. In light of this, part of the literature has not focused as much on the origin of crises, but rather on the mechanisms of shock propagation. In this regard, many studies have shown that a combination of forces is needed to generate shock transmission. Specifically, the literature on contagion has shown that agents’ interaction and the emerging network topology are key ingredients for the spread of systemic risk (see, for instance, Lux 2016; Lux and Montagna 2017). The interaction has in fact been recognized as generating two opposing effects: risk sharing, which decreases with connectivity, and systemic risk, which in contrast, increases with linkages (see, for instance, Allen and Gale 2000; Battiston et al. 2007, 2012a, b; Grilli et al. 2014; Iori et al. 2006; Mazzarisi et al. 2020; Tedeschi et al. 2012). Many other studies have confirmed the nonlinearity of this relationship. This body of work has also shown that other factors must be added to generate the catastrophic effects that characterized the 2007 financial collapse, namely the agents’ heterogeneity and financial fragility (see Aymanns et al. 2016; Bardoscia et al. 2017; Caccioli et al. 2011, 2014, 2015; Lenzu and Tedeschi 2012). In fact, as reported by Berardi and Tedeschi (2017) “on the one hand, the possible emergence of contagion depends crucially on the degree of heterogeneity. Indeed, when the agents’ balance sheets are heterogeneous, banks are not uniformly exposed to their counter-party. Therefore, if contagion is triggered by the failure of a big bank, which represents the highest source of exposure for its creditors, the situation is certainly worse than when agents are homogeneous [...]. On the other hand, the probability of default in credit markets is strictly linked to the presence of highly leveraged agents [...]. Indeed, when variations in the level of financial robustness of institutions tend to persist in time or to get amplified, financial linkages among financially fragile banks represent a propagation channel for contagion and a source of systemic risk.” Interestingly enough, this second element is very close in spirit to the Minskyan financial instability hypothesis, where endogenous shifts in the degree of financial fragility of agents generate business fluctuations and, possibly, the materialization of bankruptcy cascades (see Minsky 1964; Ferri and Minsky 1992)

Alternative approaches for the reformulation of economics

In the last decades most of advanced and developing economies have undertaken adeep structural transformation. This profound structural change, caused by the tran-sition from a manufacturing economy to a service-based one, is among the causes ofthe current crisis (see Delli Gatti et al.2012). The dereculation of the banking sys-tem with the consequent redirection of banking activity from the credit sector to thefinancial one,1and the liberalization of financial markets, the globalization and thedelocalisation of production with the resulting labor market flexibility are just some ofthe many transformations affecting the socio-economic system in the recent decades