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Published in: Financial Markets and Portfolio Management 3/2012

01-09-2012

Financial frictions and real implications of macroprudential policies

Author: Alexis Derviz

Published in: Financial Markets and Portfolio Management | Issue 3/2012

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Abstract

I model a number of imperfections in financial intermediation that have implications for real economic activity in a production economy with technological risk. Partially opaque firms are financed by both debt and insider equity. Banks have market power over borrowers. There can be a prior bias in public beliefs about aggregate productivity (business sentiment). I investigate the dependence of equilibrium on the biased business sentiment and a prudential policy instrument (a convex dependence of bank capital requirements on the quantity of uncollateralized credit). Loss given default can be reduced by both a monetary restriction and a macroprudential restriction. Real implications of both are very similar in the aggregate, but macroprudential policies are more advantageous for bank earnings. On the other hand, the policies considered here are unable to reduce the number of defaulting firms (default frequency). Economic activity is highly sensitive to “leaning against the wind” actions on both fronts, so that using a macroprudential instrument to intervene against an asset price bubble has tangible welfare costs comparable to those of a monetary restriction. The costs can be offset by fine tuning capital charges as a function of corporate governance on the borrower side (specifically, by discouraging limited liability of borrowing firm managers).

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Footnotes
1
One example is the full competitiveness assumption, which imposes the zero-profit constraint on lenders. What may be a gain in analytic convenience (a reduction of the number of free parameters) is also a loss in flexibility, since the lender’s market power is a feature one would really want to model. Moreover, it is often overlooked that zero profit is a two-way “egalitarian” constraint: not only is economic profit prohibited, but so are losses. But to model a bank without downside risk is nearly meaningless, as the recent financial crises painfully illustrated.
 
2
One can imagine a household of two, with one member supplying equity financing and the other member supplying human capital input to the same family industry.
 
3
This seems a plausible reflection of at least some instances of the transition from “purely financial” revision of beliefs, and the corresponding turbulence in asset markets, to the abrupt adverse impact on investment and GDP as observed during the latest crisis.
 
4
One can compare this feature with Bernanke et al. (1999) and successor models: these, too, contain both aggregate and firm-specific uncertainty, but the role of the former is played down, with substantial impact on interpretation of results. Indeed, when systemic uncertainty is present, Bernanke et al. (1999) do not even have a proper debt contract in the model, and the state-contingent hybrid they use instead is difficult to rationalize. On the contrary, my model includes systemic uncertainty as a key fundamental factor and lets it play a role in both equity and debt pricing.
 
5
In model extensions containing the retail investor’s consumption in period 1, \(M\) should be equal to the marginal utility of consumption in that period.
 
6
Note that being an equation that generalizes the conventional asset-pricing formula, Eq. (3) introduces an equity-market-based (co-)determination mechanism for physical capital. Such a mechanism is absent from existing financial accelerator models.
 
7
For simplicity, we consider only the case in which the loan manager knows the type precisely, that is, the loan manager has the same information as does firm management. Generalizations allowing the relationship banker to have “noisy” information, that is, information not quite as good as management’s but better than that possessed by the public, are possible but do not add much to the qualitative insights of the model.
 
8
We thereby avoid the need to account for the consequences of possible firm default on the payment to \(m\) suppliers. In principle, we could have defined a contract with \(m\) suppliers receiving payment in period 2. Then, under default, these claimholders would have been pooled with the lending bank for the purposes of debt resolution. However, this would have meant unnecessary technical complications without much of a benefit to the main goal of the present analysis, which is to explore the real consequences of interactions between firms and banks.
 
9
Note the difference between our \(q\) variable and the net worth variable of Bernanke et al. (1999) and successors: since the latter (financial frictions) models do not have explicit equity markets, their net worth value is monolithic, whereas mine is naturally split into foundation and traded stock.
 
10
Exact \(L\) knowledge by both the firm manager and the delegated loan manager (relationship banker) is a useful technical simplification, but not central to the qualitative results. What is important is that the amount of knowledge held by the firm and the relationship bank, even if different, is higher than that known by the retail investor and the wholesale bank.
 
11
Although this cut-off value is formally analogous to similar parameters used by Bernanke et al. (1999), Christiano et al. (2008), and related models (the usual notation being \(\bar{\omega })\), remember that my critical productivity value refers to systemic uncertainty realizations conditional on the given firm-specific uncertainty, whereas the mentioned papers deal with the firm-specific component.
 
12
This setup endows the bank with market power (cf. Sect. 1.2, note 1). The fact that, generically, a bank–client relationship is not fully competitive on either side is long recognized in the literature (cf. Santomero 1984). Moreover, as described by Saunders and Walter (2012), recent technological advances, geographical expansion of banking business opportunities, and the financial risk and policy response globalization during the latest crisis have allowed many banks with already considerable precrisis market shares to garner even more market power in its wake. Popular examples of imperfect competition modeling use the concept of client “catch-up” in a specific bank (see, e.g., Bonaccorsi di Patti and Dell’Ariccia 2004).
 
13
For the latter inference to work, one would need, quite unrealistically, the agents to have complete knowledge of the model and no interfering noise. More generally, it makes no sense to explore the pooling/separating equilibrium issue here, since implementation of a separating equilibrium under the preferences, technologies, and uncertainty distributions dictated by our macro application would be highly artificial in view of associated stability, sensitivity, and robustness problems. Recall also the corresponding remark in Sect. 2.4.
 
14
The assumption of common prior beliefs is made to simplify the analysis of public sentiment implications. It can be easily relaxed if it is necessary to consider belief differentials across important subcategories of economic agents.
 
15
All calculations were conducted using Mathematica\(^{\textregistered }\).
 
16
Interestingly, the outcome will be reversed under some realizations of prior prejudice. This should act as a warning that in a general equilibrium environment, asset bubbles caused by cognitive aberration have the potential to reverse conventional findings of adverse selection/moral hazard microeconomics.
 
17
Observe that if, in comparison to the model here that encompasses only two representative firms—one of each type—we had a large number of small-size borrowers, perfectly independent firm-level risks would mean no systemic risk whatsoever. In our two-firm setting, the exact size of systemic risk depends, beside correlation, on the relative size of two representative borrowers. This issue is not explored any further in the present paper.
 
18
In our calculations, we use \(a=0.01\). This corresponds to adding 1 % to funding costs of a loan of whose face value 50 % is secured by the borrower’s physical capital.
 
19
As we know, the most notorious macro failure of the Basel rules is their tendency to support the pro-cyclicality of capital requirements instead of suppressing it. Another, less conspicuous, but equally fundamental weakness in the rules from the systemic risk perspective is the lack of separation between the precautionary buffer and the leverage-brake functions of capital requirements.
 
20
Recall that the considered behavior of the borrower firm does not mean that the lender receives full repayment in all states of nature, meaning that in adverse states of nature (output less than debt service, i.e., default), a part of the compensation comes from the borrower’s private wealth. As before, in default the jointly available assets of the firm and its management are insufficient to service the debt. All that is assumed here is that the firm manager compensation is an affine function of firm earnings less debt service. In such a case, the manager would select production inputs as if the firm operated under unlimited liability.
 
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Metadata
Title
Financial frictions and real implications of macroprudential policies
Author
Alexis Derviz
Publication date
01-09-2012
Publisher
Springer US
Published in
Financial Markets and Portfolio Management / Issue 3/2012
Print ISSN: 1934-4554
Electronic ISSN: 2373-8529
DOI
https://doi.org/10.1007/s11408-012-0189-y

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