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Journal of Economic Interaction and Coordination

Erschienen in:

01.10.2015 | Regular Article

Markets connectivity and financial contagion

verfasst von: Ruggero Grilli, Gabriele Tedeschi, Mauro Gallegati

Erschienen in: Journal of Economic Interaction and Coordination | Ausgabe 2/2015

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Abstract

In this paper we investigate the sources of instability in credit and financial systems and the effect of credit linkages on the macroeconomic activity. By developing an agent-based model, we analyze the evolving dynamics of the economy as a complex, adaptive and interactive system, which allows us to explain some key events that occurred during the recent economic and financial crisis. In particular, we study the repercussions of inter-bank connectivity on banks’ performances, bankruptcy waves and business cycle fluctuations. Interbank linkages, in fact, not only allow participants to share risk but also create potential for one bank’s crisis to spread through the network. The purpose of the model is, therefore, to build up the dependence among agents at the micro-level and to estimate their impact on the macro stability.

Vorheriger Artikel Monetary policy experiments in an agent-based model with financial frictions

Nächster Artikel An agent based decentralized matching macroeconomic model

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Different interpretations of the current financial crisis have been shown. In a recent paper, for instance, Delli Gatti et al. (2012) propose an explanation of the crisis which emphasizes the sectoral dislocation following localized technical change in the presence of barriers to labor mobility.

The literature suggests that the SD of output growth rate is a good candidate for the macroeconomic uncertainty (see Ghosal and Loungani 2000; Baum et al. 2004).

To our knowledge, until now, several agent-based models have been developed with regard to single sectors of the economy (production, labor, credit, etc.), while the development of models of a multiple-market economy as a whole is still at the dawn (see for example Cincotti et al. 2010; Riccetti et al. 2011; Tedeschi et al. 2012 among the few attempts). Instead, the multiple nature of the links (financial and commercial) and the existence of direct links among all the different actors (bank-bank, bank-firms and firm-firm) would be extremely useful for understanding the propagation of systemic risk and joint failures, both among similar and different economic actors.

There are great variations between banks in the use they make of interbank market. In any case, this market should make funds available quickly and efficiently to banks which have lending opportunities and should enable the banking system to adapt much more speedily and smoothly to new demands than would otherwise be possible. Interbank market is, thus, the natural channel in order to avoid the liquidity difficulties which might otherwise exist among financial institutions (see BIS 1983).

In the analysis of interbank markets, it is difficult to discriminate between roles played by different banks. In practice, it is not easy to distinguish interbank activity that is pure trading from that related to customer business (see Majluf and Myers 1984; BIS 1983; Affinito 2012).

We are assuming that the firm \(i\) is rationed on the equity market and has to rely on the bank to obtain external finance.

In the formulation proposed by Delli Gatti et al. (2005), for instance, bankruptcy costs are increasing and quadratic in the level of output.

The demand of credit—or asked loan—\(L^d_{i,t}\) may be different from the granted loan \(L_{i,t}\) due to the trading mechanism on the credit and inter-bank market explained in Sect. 2.3.

This means, for example, out of 10 different requested loans with \(p^{j,i}_{t} = 0.1\), one loan will be given.

In our model the bank behaves as a lender in a Bernanke and Gertler (1989, 1990) world characterized by asymmetric information and costly state verification. See Bernanke et al. (1999) for a comprehensive exposition of the approach.

The need for an inter-bank market is related to the need for banks to adjust the volume of their assets and liabilities. In particular, large emphasis has been given to the deposits withdrawal (see, for instance, Diamond and Dybvig 1983; Iori et al. 2006). In our framework, the reasons for using this market arise from the banks need to adjust their assets in order to exploit lending opportunities. In our model, in fact, liabilities side, and in particular deposits, is determined as a residual. In a forthcoming paper, we extend the analysis allowing an endogenous deposits motion.

The advantage of our trading mechanism with respect to a supply demand in-balance approach is that exchanges are determined by the trading mechanism itself without any ad-hoc rules to reach an equilibrium. The supply demand in-balance approach shortcuts the study of the out-of-the-equilibrium dynamics by jumping to the stationary points. The trading mechanism we implement here, instead, enables us to understand how the economy behaves out of equilibrium. In particular, given the constraints on agent’s risk-aversion and the excess individual demand, we are able to model rationing on both markets.

In inter-bank markets maturities are short, normally between overnight and one years, although longer placements can be arranged (see BIS 1983; Affinito 2012; Dinger and Von Hagen 2007). Interestingly, the empirical analysis generally shows that long-term interbank exposures result in lower risk for borrowing banks (see Dinger and Von Hagen 2007). Following this view, in this paper, we model longer maturities.

\(\bar{r}_{j,t}\) is the average interest rate that bank \(j\) obtains in the credit market.

\(i\in \varOmega _{t}\) and \(k\in \varXi _{t}\) are the subset of firms and banks unable to pay their debts back because they go bankrupt.

In Erdös–Renyi random graphs, the average number of links is given by \(x(n-1)\), where \(x\) is the probability of attachment and \(n\) the number of node. With, 50 banks, each firm has an average number of out-going links equals to 2.45.

Researches show a general tendency for new companies to finance themselves with equity rather than loan. The initial bank funding to new firms is around 16 % (Berger and Udell 1998).

The granted-asked loan ratio is uncorrelated with the interbank connectivity.

Empirically it is observed that absolute growth rates are autocorrelated over lags of several years and decay slowly to zero. Several authors (see, for instance, Ding et al. 1983; Ding and Granger 1996; Cont et al. 1997), have shown that the autocorrelation functions decrease hyperbolically with the time lag.

The number of recessions is calculated as the number of local minima of the filtered series. The duration consists in calculating the base of the triangle which includes the points from peak-to-trough of the output filtered series (see Harding and Pagan 2002).

The average number of busts (busts duration) decreases from 8.21, SD 2.09 (20, SD 4.57) to 6.5, SD 2.80 (16,48, SD 3.02), when the inter-bank connectivity is changed from 0 to 0.2. A two-sided Welch t test supports the difference in the means \(\mathrm{t} = 13.8462\,(\mathrm{t}=20.1167)\).

Expansions follow a similar pattern to recessions. Results are omitted.

In particular, we investigate the distribution of the cumulative de-trended growth rate (potential output), where cumulative refers to the sum of consecutive raw observations sharing the same sign (see Burns and Mitchell 1946; Di Guilmi et al. 2004). We then plot the rank-ordering transformations of recessions in a log–log space (DDF plot).

Weibull distribution is \(F(x)=1-exp(-ax^b)\).

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Titel: Markets connectivity and financial contagion
verfasst von: Ruggero Grilli
Gabriele Tedeschi
Mauro Gallegati
Publikationsdatum: 01.10.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Economic Interaction and Coordination / Ausgabe 2/2015
Print ISSN: 1860-711X
Elektronische ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-014-0129-1

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