Prudential regulation in an artificial banking system

This study constitutes an exploratory analysis of the economic role of banks under different prudential frameworks. It considers an agent-based computational model populated by consumers, firms, banks and a central bank whose out-of-equilibrium interactions replicate the conjunct dynamics of a banking system, a financial market and the real economy. A calibrated version of the model is shown to provide an intelligible account of several recurrent economic phenomena and it can be a privileged ground for policy analysis. The authors’ investigation provides a relevant methodological contribution to the field of banking research and sheds new light into the role of banks and their prudential regulation. Specifically, the results suggest that banks are key economic agents. Through their financial intermediation activity, credit institutions facilitate investment and promote growth. JEL C63 G28


Introduction
Are banks key drivers of economic performance? Which are the impacts of different macro and micro-prudential regulations in economic growth? These seminal questions have resurfaced, in the aftermath of the 2008's financial debacle, as a central topic in banking research and as an important source of concern for policy makers. In this study, we develop an agent-based computational model through which an exploratory analysis of the economic role of banks under different prudential frameworks is conducted.
The years that preceded the 2008 crisis were characterized by a blatant phenomenon of reckless mortgage lending. Loan-underwriting criteria were so lenient that "subprime" borrowers with no capacity to redeem their debt had easy access to credit. These mortgages were pooled together and used to back collateralized debt obligations (CDOs). As soon as the housing markets collapsed, "domino effects" spread throughout the extremely interconnected financial system, quickly exposing severe fragilities. The price of mortgage-backed securities dropped astoundingly and allegedly safe CDOs were, in the end, valueless. The sale of such securities or its use as collateral became increasingly difficult. In an effort to rally liquidity, banks started to sell assets at fire-sale prices, giving rise to "price spiral" phenomena that, in turn, reduced banks' capital thanks to "mark-to-market" accounting rules. When banks started doubting the solvency of their counterparties, wholesale agents stopped rolling over short-term debt, causing the failure of banks that had vigorously relied on non-stable sources of funding (Economist, 2013).
The financial system proved to be incredibly fragile. Banks had expanded more than ever before, leveraging beyond reasonable levels and holding insufficient capital to absorb losses.
It is therefore no surprise to note that the ex-post analysis of the failed mechanisms that led to the crisis has focused on banks' raison d'être and on the regulatory instruments that can increase and promote financial stability.
From a theoretical standpoint, banks have long been viewed as fundamental economic agents. Diamond and Dybvig (1983) suggest that banks are useful as liquidity providers since they provide depositors with liquidity insurance against idiosyncratic shocks that affect their consumption needs. In turn, Diamond (1996) suggests that banks provide monitoring services and help decrease asymmetric information between investors and firms. Banks also provide maturity transformation, a process whereby short-term liabilities are converted into long-term assets (Freixas and Rochet, 2008). The existing literature postulates that agency problems (Dewatripont and Tirole, 1994) and the risk of a systemic crisis (Santos, 2000) are the main reasons why banks should be subject to regulatory requirements.
Across time, several were the instruments (one of the most prominent ones being the deposit insurance scheme proposed by Diamond and Dybvig (1986)) suggested by academia to mitigate or prevent the negative effects of bank bankruptcies. In spite of this, and due to its function in reducing risk-taking and providing a buffer to absorb losses, the regulatory limelight always fell more heavily in bank capital (Allen and Gale, 2007). In this scope, Basel III/CRD IV rules, which are at present mandatory for Euro area banks, represent the latest regulatory effort to reinforce the robustness of the banking system. Besides the enhancement of minimum capital requirements to at least 10.5% of risk-weighted assets, it is worth highlighting Basel III's introduction of a counter-cyclical capital buffer. However, the latest Basel Accord, is not completely consensual. Carmassi and Micossi (2012), for instance, argue that Basel III could follow a much simpler path in route to a more stable financial system simply by abandoning the risk weighting approach.
The strong debate surrounding prudential policies, coupled with the blatant failure of macroeconomic models in the 2008 financial crisis, suggests that the study of regulatory policies is not within the grasp of the existing nucleus of mainstream economic knowledge.
The notions of interdependence, networks, trust and expectations, which are key to understanding financial crisis, appear not to be "features of modern macroeconomic models" (Kirman, 2010: 501). To address these shortcomings, it is crucial that economists start considering the economy as a complex evolving system (Taylor, 2007). This can be done through agent-based modelling, a technique that allows researchers to simulate and comprehend out-of-equilibrium economic dynamics (Arthur, 2006). Agent-based computational economics encompasses, as illustrated by Tesfatsion (2006), a group of technologies that are best used in the analysis of complex, evolving systems composed by a multiplicity of agents who interact on the basis of behavioral rules that are exogenously defined. The incentive for this approach is rooted in the fact that complex systems, of which economies are flawless examples, can display emergent phenomena that cannot be foreseen or understood by any agent on an individual level. Instead of modeling economies under the omniscience assumption, according to which the decisions of agents are pre-coordinated, agents are programmed to follow simple behavioral rules that may or may not result in equilibrium. Through computer simulations, the behavioral patterns of the system can be scrutinized and understood. Once a computer program able to replicate the phenomena of interest is developed, researchers can use it as a laboratory in which they can conduct policy experiments.
On this vein, it is the purpose of this paper to assess, through the use of a rich, yet tractable agent-based model, the effectiveness and efficiency of prudential regulatory measures in their effort to maintain a stable and sound banking system. We add to the existing body of literature by creating a stylized agent-based computation model 1 that simultaneously replicates the conjunct dynamics of a financial market, a banking system and the real economy, mimicking several stylized facts of the latter and grasping, to a certain extent, the implications of banking regulation in economic performance.
The conceptual framework employed draws heavily from Takahashi and Okada (2003) and Tedeschi et al. (2012). For tractability, we adopt a parsimonious approach that drifts away from fully specified models such as the Eurace project (Raberto et al., 2011;Cincotti, 2012). Specifically, our model attempts to depict, in an admittedly stylized form, the inner workings of a small economy composed by consumers, firms, commercial banks and a central bank.
Consumers are responsible for providing the work force needed for production and for driving internal demand through consumption. Firms are profit-maximizing entities that produce the consumption goods and are thus responsible for driving supply. The primordial function of banks is to collect deposits from their clients and to give out loans to consumers and companies.
Banks are, in addition, subject to prudential regulations established by the Central Bank (i.e., minimum capital ratios and minimum loan underwriting standards).
Following Ashraf et al. (2011), the model is calibrated to U.S. data. Simulations are performed repeatedly, for several time periods and under diverse parameter settings in order to assess how credit institutions impact macroeconomic performance and how performance, in turn, is impacted by diverse regulatory frameworks.
The model we present aims to be a valuable methodological contribution to the field of banking research, shedding new light on the role of banks and bank regulations by leveraging on the 1 The computer program under the agent-based computation model has been developed in NetLogo, an agentbased programming language and integrated modeling environment. advantages of agent-based computational modelling. In spite of being too simple to befit the policy-making endeavor, it does generate five results of broad-spectrum qualitative interest.
The first result corroborates the view that credit institutions are crucial economic agents in so far as their existence significantly improves the performance of the economy. Banks foster growth by providing credit and expanding firms' investment opportunities, which are usually limited to each company's ability to endogenously generate cash flows. The capacity of the economy supply-side to meet aggregate demand is thus significantly improved when a banking system is in place. The role of credit institutions is also relevant in mitigating the negative externalities of firm failures. By providing financing to new entrants and sustaining incumbent firms by preventing capital erosion, banks are often able to alleviate the effects of shocks.
Our second result reveals that the macroeconomic impact of credit institutions and bank regulation frequently contradicts the traditional dogmas behind micro-prudential policies. In particular, the experiments conducted show that aggregate macroeconomic performance deteriorates as banks become safer (i.e., as stricter capital requirements are imposed). This is because decreased credit availability hampers the creation of value by reducing the speed at which supply is able to meet surges in demand. Furthermore, increased leverage does not always entail augmented firm bankruptcies. If credit levels are not excessive, bank funding is able to prevent the depletion of firms' production capacity in periods of low demand, thus contributing to a reduction of bankruptcies in the long run. Finally, increased capital requirements do not always mean that banks are less likely to fail. Indeed, micro-prudential policy is shown to entail calibration risk: while increasing capital requirements increases the immediate loss-absorbency capacity of banks, it also decreases their ability to endogenously generate capital through profits.
The third result shows that the role of banks (in particular if risky) is more relevant when the economy is performing significantly below optimal levels (i.e., when the economy displays large output gaps). According to our policy experiments, the output gap is closed faster if banks are allowed to require lower financial autonomy ratios from customers and are subject to lower capital adequacy ratios. In other words, credit is proved to be more important when the output gap is larger.
A corollary of this result is that using the credit-to-GDP ratio as a trigger to the implementation of the counter-cyclical capital buffer might be, if taken single-handedly, an insufficient approach. Since the same level of leverage may be too high or too low depending on the current state of the economy (i.e., the magnitude of the output gap), the establishment of a countercyclical capital buffer must also consider the impact of credit-constraining measures in GDP growth and an assessment of the level of debt that can be sustained by the economy without causing the build-up of system-wide risk.
More importantly, our fourth result argues, under the premises of our model, that the countercyclical capital buffer should never actually be implemented. Indeed, our experiments fail to find evidence in support of the rationale behind macro-prudential tools. This conclusion is not surprising if considered conjunctly with previous insights, which showed that riskier banks promote growth in fundamentally all scenarios.
Finally, our fifth result suggests that effective resolution is crucial. Specifically, resolution of credit institutions acts to the benefit of the economy by ensuring depositors are never bailed-in to a great extent and by getting rid of "zombie-banks" that are not able to support investment.
The paper is organized as follows. Next section discusses the conceptual framework of the model and describes the protocol and behavioral rules imposed on its interacting agents. The calibration procedure is also clarified in this chapter. Section 3 describes the core results of our policy experiments, providing insights into the role of banks and the impact of micro and macro prudential policies in economic performance. It also addresses the robustness of the results by examining a scenario where the negative consequences of bank failures are magnified. Section 5 summarizes our concluding remarks

Conceptual Framework
The conceptual framework employed draws heavily from Takahashi and Okada (2003) and Tedeschi et al. (2012). The model depicts, in an admittedly stylized form, the inner workings of a small economy composed by I consumers, E companies (also referred to as firms), B commercial banks and a central bank. Consumers are indexed by = 1, … , , companies are indexed by = 1, … , and banks are indexed by = 1, … , . Since there is no government, there are also no taxes nor public expenditure.
In this economy, there is only one kind of product valued by the population -the consumption good. Consumer goods are perishable and have their priced fixed at unity.
Two types of production factors exist: labor and capital. Consumers provide labor and receive compensation from companies in return. Based on their disposable income, net worth and bank deposits, consumers determine how much to spend on the consumer good. Companies decide how much to produce based on their own production capacity and expectations of demand. In this process, firms can invest in infrastructures and technology, increasing their productivity.
In general, supply and demand (both internal and external) for the consumer good do not balance, giving rise to demand rationing (excess demand) or production waste.
Economic agents can also invest in financial assets (i.e., bonds), with each debt security generating interest income at each time step. This asset is finite and exogenous to the economy, and can therefore be interpreted as a debt security issued by a sovereign country. Each consumer and company can buy and sell the financial asset in the market.
All transactions must be mediated by bank deposits, and proprietors are not able to lend and borrow among themselves. Each bank grants credit and accepts deposits from their customers, with depositors being allocated randomly across banks so that each credit institution starts off with the same number of clients. Transaction settlements are executed by changing the holder of the bank deposit.
For a more comprehensive view of the economy's agents, a cursory description of their core behavioral features and characteristics is provided below.

Consumers
Consumers are responsible for providing the work force needed for production (for which they receive their wage ( )) and for driving internal demand through consumption. They are members of the co-operative firms in which they are employed and are thus entitled to a share of their profits. In addition, consumers are also a big part of the financial asset market since they are able to buy and sell debt securities.
At each time step , the patrimony of consumer is composed by units of the financial asset and a bank deposit ( , ). Since companies may not always be able to fulfill their commitments to their employees, circumstances might happen when the consumer is also owed a part of its salary ( ). To finance the acquisition of the asset, consumers can make use of bank loans ( ).
The value of the financial asset is marked to market at each time step: Hence, the net worth (i.e., the net equity) of each consumer equals: The financial autonomy ratio of consumer can thus be represented as: Finally, each consumer displays, at each time step , a balance-sheet similar to the one depicted in Figure 1.

Companies (also referred to as Firms)
Companies are profit-maximizing entities of a co-operative nature (i.e., owned by their employees) that produce the consumption good and are thus responsible for driving supply.
The production activity entails the usage of two production factors: labor and capital. While the amount of labor is fixed (i.e., each firm has employees), the amount of capital can vary based on investments made by the firm in order to increase its production capacity. Companies are also assumed to be able to invest in the financial asset.
At each time step , company owns units of the financial asset, a bank deposit ( , ) and of productive capital. In order to finance the acquisition of the asset or increase the production capacity through investment, companies can make use of bank loans ( ). Finally, and since companies may not always be able to fully remunerate employees for their work, circumstances might happen when delayed wages are accumulated (∑ , , where , represents the amount of delayed wages between company and consumer ).
Taking into account that the value of the financial asset is marked to market at each time step, the net equity of each firm equals, at time step : The financial autonomy ratio of company can be represented as: Finally, each company displays, at each time step , a balance-sheet similar to the one depicted in Figure 2.

Banks
The primordial function of banks is to collect deposits from their clients and give out loans to consumers and companies. This financial intermediation activity is undertaken by charging interest on loans and remunerating deposits at a lower rate. Loans are made with full recourse and are collateralized by all the assets of the borrower.
While conducting its operations, the bank is subject to mandatory capital requirements and, just like an individual consumer, settles all its transactions through the exchange of deposits. Taking into account that the value of the financial asset is marked to market at each time step, the net equity of each bank equals: The capital adequacy ratio of bank can be represented as: where the risk-weighted assets ( ) represent a risk-based measured of the bank's exposures obtained by multiplying the value of the credit institution's assets by a risk-weight ( ) that varies according to the level of each investment's perceived risk.
The balance-sheet of a bank is depicted in Figure 3. 2 The cash holdings of the bank include the amount of deposits not currently invested in loans plus the bank's own liquidity surplus. The initial liquidity endowment of banks is denoted by . Cash holdings are assumed to be applied in foreign (i.e., model exogenous) credit institutions and are thus considered interest earning assets.

Central Bank
In the model's economy, the central bank is primarily responsible for banking regulation and supervision. The central bank thus regulates banks and is responsible for ensuring their compliance with prudential requirements.
Specifically, the central bank requires each bank to: (i) implement specific loan underwriting policies (as defined by the minimum financial autonomy ratio demanded from customers) and (ii) maintain capital at least equal to a percentage of the value of its risk weighted assets (bank loans and financial assets). The minimum capital adequacy ratio is represented by . Credit institutions that do not comply with this prudential requirement are declared to be in financial distress and are prohibited from granting any new loans. In addition, banks with negative equity are forced into failure by the supervisor, giving rise to potential losses for depositors and to the entrance of a new player.

Protocol and behavioral rules
As usual in the ABM literature, and for the purpose of reducing its computational burden, the model follows a choreographed protocol that restricts the decisions taken by agents and forces them to initiate actions sequentially through 11 stages: Each one of these steps is described in detail below.

Determination of internal demand for the consumption good
The level of consumption is assumed to be determined as a function of the consumer's disposable income and net worth at the beginning of the period: where ∝ represents the marginal propensity to consume from income, represents the marginal propensity to consume out of net wealth, and is a normally distributed random variable that accounts for all other motives that may increase or decrease consumption. It should be noted that consumption is always limited by the amount of cash (i.e., bank deposits) the consumer owns at the beginning of the period.

Determination of external demand for the consumption good
Since the model focuses on an open economy, demand for the consumer good is not only internal. External demand (e.g., exports), which is firm specific, is defined exogenously and assumed to grow at a rate of : where θ is a normally distributed random variable that accounts for the myriad of factors that may affect exports at each moment in time.

Determination of supply for the consumption good
Companies plan their production activity based on the observed last period's demand. Demand is assumed by firms to increase at a constant rate, thus mimicking long term expectations of economic growth. Since producers are undifferentiated, expected internal demand is equally distributed across firms: To produce the consumer good, companies use capital as the only input. Each firm is able to produce according to the following production function: For simplicity purposes, capital productivity is assumed to be constant and uniform across companies. In addition, capital is assumed to depreciate at a constant rate ( ) at each time step.
Companies will initially try to produce as much as their expected demand. Since the only external source of finance that firms have are bank loans, firms will apply for credit whenever their capital is not enough to fulfill expected demand.
However, since borrowing entails the risk of default, each company takes into account its probability of failure when submitting a loan application. Credit demanded by company to bank is thus established according to: where is the firm's risk aversion coefficient, [ ] represents the firm's expected profits and represents the firm's expected debt servicing costs.

Loan underwriting
Credit granting is determined as a function of the financial situation of the borrower and the capital position of the bank. In an effort to replicate the "restricted lending" problems typical of the recent financial crisis, credit institutions with adequate capital are assumed to be more willing to take risks, whereas banks with inadequate capital become reluctant to lend funds.
The level of loan affordability depends on the capital ratio of the bank and takes on the value zero if its capital ratio ( ) stays below or the capital ratio of the borrower ( ) is below . Naturally, the bank must also have liquidity surpluses (i.e., cash holdings) to underwrite the loan. In particular, the bank defines the amount of credit to be granted as follows: Interest rates are assumed to be determined exogenously as result of competition, changing as a result of the excess demand or supply of funds in the financial market. In particular, the interest rate on deposits negatively depends on the amount of deposits held by banks: where ̅ represents the average interest rate on deposits and ̅ the average level of consumption.
Since loans to domestic investors involve the risk of bankruptcy, banks charge higher interest rates on loans. The rate is bank-idiosyncratic and inversely correlated to the financial robustness of the institution: where is a representative parameter of the bank's risk aversion.
All loans are based on variable interest rates, which means that the interest rate charged on the outstanding balance varies as market interest rates change. In addition, all loans are initially given out with a standard maturity . When a consumer or a company increases the amount of borrowings received from the bank, the total amount of debt commitments is, in a process akin to debt restructurings, merged into a single loan that will mature within timesteps.
Loans are considered to be in distress when at least one payment has been missed. Each bank determines a liquidation rule such that each of their customers goes bankrupt whenever they miss consecutive payments.

Assessment of companies' financial position
After trying to sell their production in the market, companies assess their cash in-flows and start making their payments. At this point, each firm's revenues 3 are given by the remuneration generated by each unit of bonds owned and the minimum between real demand and the company's production: where is the coupon rate of the debt security and represents its nominal value.
On the other hand, cash outflows 4 are given by: where × represents personnel costs, × represents funding costs and represents the loan's principal repayment.
Whenever cash outflows are greater than cash inflows plus the company's deposits, the firm gives priority to the payment of wages. Bank loans are assumed to be paid last. Amounts not paid are registered as liabilities and need to be paid in the following time step. In an effort to re-equilibrate its financial position, the company will also try to sell any bond it owns.
Finally, the company net worth is updated as follows:

Companies' bankruptcy and entrance
As already stated, banks will liquidate debtors as soon as they miss payments. In such situations, the remaining company's assets are ceased by the bank and the player leaves the market.
The model assumes a simple entry mechanism based on one-to-one replacement. As such, firms that go bankrupt are automatically replaced by new players. In accordance with the empirical literature (Bartelsman and Scarpetta, 2005), new entrants are usually smaller than existing firms. Specifically, the stock of capital of new firms is drawn from a uniform distribution with = min( , , … , ) and = median ( , , … , ).

Assessment of consumers' financial position
At this stage, the income of consumer in period ( ) 5 , is a function of his wage and the amount of bonds it owns. This can be represented as: Note that the consumer might not always receive the totality of his wage, as the company for which it works for might be in financial distress.
On the other hand, the cash outflow 6 of each consumer at time step is given by: where represents consumption and × , represents funding costs.
Whenever cash outflows are greater than income plus the consumer's deposits, the consumer gives priority to the liquidation of consumption expenses. Bank loans are assumed to be paid last. Amounts not paid are registered as liabilities and need to be paid in the following time 5 Consumer's income also includes interest received on deposits and potential cash-inflows stemming from the sale of debt securities and from firm dividends. At this point, however, it is not possible to know whether the revenue generating capacity of the consumer's bank is enough to fulfill the totality of its commitments. Interest revenue is thus added to the consumer's net worth after the computation of the bank's financial position (please refer to stage 8). In addition, proceeds from the sale of debt securities are added to the consumer's bank deposits whenever a transaction is settled (please refer to stage 10). Cash inflows associated with firm dividends are added to the consumer's net worth at the end of each time step (please refer to stage 11). 6 Consumer's cash-outflows also include potential payments stemming from the purchase of debt securities. Such disbursements are deducted from the consumer's bank deposits whenever a transaction is settled (please refer to stage 10).
step. In an effort to re-equilibrate its financial position, the consumer will also try to sell any lot of land it owns.
Finally, the consumer's net worth is updated as follows:

Assessment of banks' financial position
At each time step, banks will assess their cash-flows in order to determine the robustness of their financial position. Cash inflows 7 consist fundamentally of interest income and are thus a function of the debtors' ability to service their debt and the rate of remuneration of the bank's claims on other (foreign) credit institutions: In accordance with commercial banks' business models, cash outflows 8 consist of interest expenses related with the remuneration of deposits and the payment of interest on interbank loans: Liquidating distressed borrowers is needed for banks to raise liquidity, thus playing a crucial role for banks' financial stability. As already stated, banks will liquidate debtors (consumers or companies) as soon as they miss payments. In this process, credit institutions take over any bond still owned by the debtor 9 and immediately try to sell it on the market. The transfer of the collateral's ownership implies a direct write off of the bad loan.
Whenever cash inflows are greater than cash outflows plus the bank's cash holdings, it is clear that the credit institution is able to fulfill the totality of its commitments. The net worth of both companies and consumers is thus increased by their respective remuneration on deposits: Finally, the bank net worth is updated as follows: where stands for the nonperforming loans that were written off by bank in time step and represents the number of debt securities foreclosed during the same time step.

Banks' bankruptcy and entrance
Bankruptcy is assumed to happen to banks whenever credit institutions are not able to fulfill the totality of their commitments (i.e., whenever cash outflows are greater than cash inflows plus the bank's cash holdings) or whenever their capital ratio is below zero. The model thus captures the possibility for both liquidity and solvency related default events.
In the event of bankruptcy, the credit institution enters into resolution. As part of this process, non-performing loans (i.e., loans with > 0) are foreclosed. Debtors that were not able to fully pay their obligations to the bank see their holdings confiscated. Since the collateral's value may not be enough to fully compensate non-performing loans outstanding, the bank's capital might be further reduced during this process.
After all the non-performing loans are foreclosed, the capital position of the bank is assessed.
Losses are first and foremost absorbed by equity capital. Depositors are bailed-in whenever equity capital is negative. Since all depositors have the same level of seniority, losses are distributed among all clients proportionally to the amount of deposits they own.
Similarly to the process described in point 6, a recapitalization of the bank using foreign capital is assumed. Since only healthy assets are now left in the bank, this does not constitute a farfetched scenario. The amount of the recapitalization is equal to the maximum between the endowment initially given to all banks and the amount of capital needed for the bank to display a capital adequacy ratio 12% in excess of minimum requirements. When computing the latter, a haircut ( ) is applied to the value of the financial assets.

Purchase and sale of the financial asset
Supply and demand for the financial asset are generally determined by how attractive the bond is relative to other financial assets (loans). For each agent, the attractiveness of the debt security in period , denoted by , is given by: and ~ 0 1 The first term on the right hand side of equation (28) represents the expected income accrued from owning one unit of the financial asset. The second term captures the expected capital gain perceived by the trend chasers augmented by , the strength of the trend chasing attribute.
Expected capital gains in equation (28) reflect the change in the asset price over the last periods. Even though all traders are trend chasers, the capital gain term is weighted by a random variable ( ) to capture divergent expectations.
The gains from holding debt securities (first term of equation (27)), minus loan interest (denoted by the third term), measure the net marginal gain from owning an additional bond. In reality, other factors affect purchase and sale decisions: the second term of equation (27) represents such factors. This second term further helps traders forming different expectations.
The owner attempts to sell one lot of land when it judges land to be less attractive (i.e., less than ̅ ), or when it is pressed to sell land due to a financial distress situation. For ≥ 1, supply of land ( ) is such that: Economic agents, unless they are debtors of nonperforming loans, will try to make a purchase of one unit of the financial asset if having the additional unit is attractive enough: After defining its interest in buying the financial asset, every agent needs to determine if it needs bank financing. Buy-side players' intention to acquire debt securities is limited to the purchase of one unit.
After each agent determines its market positioning, supply and demand meet randomly, resulting in transactions. The short side of supply or demand determines the actual quantity of units traded. Agents that manage to settle their transactions see their financial position updated accordingly: where represents the amount of debt securities purchased and represents the amount of debt securities sold.
In general, prices are assumed to be sticky. The price therefore responds to the difference between supply and demand ( ), with representing the speed of the price adjustment:

Distribution of profits
At the end of each time step, firms, in accordance with their cooperative nature, distribute dividends. The payout ratio is a function of both the firm's current cash holdings and the outflows that the company expects to face in the next period. Specifically, expected disbursements reflect each firm's forecasted personnel and investment costs. Mathematically: where [ ] represents next period's expected demand, represents next period's production capacity and designates each firm's propensity to fund new investments with equity (i.e., the percentage of cash withheld to fund new investment opportunities or, in other words, the target capital structure of greenfield projects).
Naturally, and in case the distribution of profits is consentaneous with the liquidity position of each company, the net worth of each of its associates must be updated with their respective share of the dividend: Finally, each company's net worth is updated as follows:

Model calibration
As demonstrated above, the model has multiple individual agents. In order to restrain the number of parameters and to facilitate calibration, ex ante symmetry between agents was imposed. In spite of the homogenous initial setting, the economy develops heterogeneous behaviors and results due to the impact of feedback effects and noise.
The calibration procedure follows the methodology described in Ashraf et al. (2011) and assumes each time step represents one month. The 39 parameters of the model were classified as consumer parameters, firm parameters, bank parameters, financial market parameters and general parameters. These are listed in Table 3

First stage of the calibration
Consumer parameters. Following Takahashi and Okada (2003):  The consumer's propensity to consume out of disposable income ( ) was set to 0.95;  The consumer's propensity to consume out of net wealth ( ) was set to 0.01;  The initial wage of each worker ( ) was set to 500.
Analogously to the volatility of the shock to aggregate demand defined by Tedeschi et al. (2012), the volatility of the shock to each agent's consumption ( ) was set to 0.1.

Firm parameters.
In accordance with Tedeschi et al. (2012):  Capital productivity ( ) was set to 0.1;  Each firm's risk aversion coefficient when submitting loan applications ( ) was set to 1.
Bank parameters. As defined by Tedeschi et al. (2012):  Each bank's risk aversion coefficient used for the purposes of defining the spread on granted loans ( ) was set to 1;  The standard maturity of every loan ( ) was set to 12 (i.e., 1 year).
As foreseen in the Basel III capital accord, the minimum capital adequacy ratio ( ) was set at 8% (capital buffers not included). The risk-weight ( ) on non-risk-free assets is approximated by the average Basel II RWA density 10 reported by Le Leslé and Avramova (2012). The risk-weight attributable to claims on other credit institutions ( ) is set at 20%, which is the weight foreseen in CRD IV for claims on banks belonging to the best credit quality step.
Finally, and based on Banco de Portugal's definition of credit-at-risk 11 , the number of missedpayments necessary for the bank to trigger the liquidation of a debtor was set at 3 (i.e., 90 days).

Financial market parameters. FollowingTakahashi and Okada (2003):
 Each agents' propensity to form divergent expectations regarding the financial asset price (as measured by and ) were set to 0.5 and 0.2, respectively;  The strength of the trend chasing ( ) was set to 1;  The attractiveness threshold for the financial asset ( ̅ ) was set to 0.01;  The price adjustment speed ( ) was set to 0.1.
General parameters. The long-term growth expectation of internal and external demand ( ) was set at 0.26% based on the monthly growth rate of the US economy between 1947 and 2014 according to the data series available at the Federal Reserve Bank of Saint Louis. To estimate 10 Percentage of RWAs over total assets. 11 The concept of credit at risk was initially defined by Banco de Portugal in Instruction No 22/2011. Credit at risk corresponds to the following elements as a whole: (a) Total amount of outstanding loans with principal installments or interest overdue for a period of 90 days and over. Non-contracted current account claims should be considered as credit at risk 90 days after an overdraft is recorded; (b) Total amount of outstanding restructured loans not covered by the preceding sub-paragraph, whose installment or interest payments, overdue for a period of 90 days and over, have been capitalized, refinanced or their payment date delayed, without an adequate reinforcement of collateral (this should be sufficient to cover the total amount of outstanding principal and interest) or the interest and other overdue expenses that have been fully paid by the debtor; (c) Total amount of credit with principal installments or interest overdue for at least 90 days, but on which there is evidence to warrant classification as credit at risk, notably a debtor's bankruptcy or winding-up. the growth rate, and taking into account that our model does not consider inflation (i.e., the price of the consumer good is fixed at unity), the data series for the real gross domestic product 12 was used.

Second stage of the calibration
During the calibration phase of the model, the price of the debt security ( ) was found to show mean reverting behavior. As such, its initial value was set to 2055 (i.e., the median value of the cross-run averages of the security's price). With regards to the real values of U.S. data used, it is important to note that:

Third stage of the calibration
 GDP's volatility is the standard deviation of the detrended HP-filtered log GDP series for the period 1947-2014. As suggested by Ravn and Uhlig (2002) for quarterly data, the value of the multiplier parameter was set at 1600. The autocorrelation of this variable was computed by estimating an AR(1) process over the same time period.
 The default rate is the value-weighted average of rated corporate bond issuers that entered into financial distress each year between 1920 and 2010, which Moody's (2011) reports to be 1.15 percent. At this juncture, it is important to acknowledge that circumscribing the data to the exit rate on rated corporate bond issuers is most likely severely underestimating the aggregate default rate of regular companies in the U.S.
 Finally, and according to (Ashraf et al., 2011), the average yearly commercial bank bankruptcy rate was about 0.51 percent over the period from 1984 to 2006.
The real U.S. variable values are enumerated in Table 1   In addition, the model is also successful in replicating several of the stylized facts thoroughly surveyed by Rebelo and King (1994) with respect to the U.S. real aggregate activity. To study these properties and assess its emergence in the model, we focus on a time series composed by the cross-run average of each variable at each time step. Persistence. All detrended business cycle variables (please refer to Table 2) display substantial persistence (average first order autocorrelation of 0.68), which means that shocks have a real effect on the evolution of each economic cycle.
Volatility. The detrended macroeconomic aggregates produced by the model also replicate real phenomena concerning levels of dispersion:  Consumption is less volatile than output (relative standard deviation of 0.65);  Investment is much more volatile than output (relative standard deviation of 4.35); Contrary to the stylized fact postulated by Rebelo and King (1994), however, the stock of capital appears to be slightly more volatile than output in our model (relative standard deviation of 1.03). 13 Based on the ADF test it is also possible to conclude that the series follows a deterministic trend (i.e., it is trend stationary). This conclusion is based on the fact that the null hypothesis is rejected (p-value of 0%) when conducting the ADF test with trend and intercept (please refer to Appendix II). However, this specific characteristic is a byproduct of the cross-run averaging process in which the impact of the stochastic shocks is smoothed. This is corroborated by the analysis of an exemplificative test run (seed -397426811), which follows a stochastic trend according to the ADF test (p-value of 82.4%; please refer to Appendix III).
Comovement. Most of the economic variables are pro-cyclical, thus exhibiting a positive contemporaneous correlation with output (average correlation coefficient with output of 0.74). All variables are in logarithms and have been detrended using the HP filter. As suggested by Ravn and Uhlig (2002) for monthly data, the value for the multiplier parameter was set at 129 600.

Source: Authors
Finally, it is also worth highlighting that the model is able mimic relevant relations among macroeconomic variables that hold over long horizons. In analyzing the model's capacity to replicate stylized facts of economic growth and development, we focus on the macroeconomic variables produced endogenously by the model to conclude that, as postulated by Kaldor (1957):  Capital per worker shows steady growth (please refer to Figure 5);  The average growth rate of output per worker is positive and relatively constant over time (please refer to Figure 6).
Nevertheless, and contrary to Kaldor's facts of growth:  The shares of income devoted to capital and labor show trends, thus not fluctuating around constant means (please refer to Figure 7);  The real rate of return to capital shows an upward trend (please refer to Figure 8).
These results are, however, far from unexpected. Indeed, the fact that our model does not encompass wage adjustments or population growth naturally implies that the share of income devoted to labor will remain constant while the share of income attributable to capital will increase.     Number of consumers in the economy 1,000 Propensity to consume out of disposable income 0,95 Propensity to consume out of net wealth 0,01 Volatility of the shock to consumption 0,1 Initial wage 500 Initial liquidity endowment provided to consumers 5,000 Initial amount of debt securities held by each consumer 1

Firm parameters
Number of firms in the economy 50 Initial capital endowment provided to firms 150,000 Capital productivity 0,1 Capital depreciation rate 0,6% Initial liquidity endowment provided to firms 150,000 Firm's risk aversion coefficient when submitting its loan application 1 Initial amount of debt securities held by each firm 1 Firm's propensity to fund new investments with equity 45%

Bank Parameters
Number of banks in the economy 10 Initial liquidity endowment provided to banks 250,000 Bank's risk aversion coefficient when defining the spread on loans 0,1 Initial interest rate on deposits 0,17% Interest rate received due to claims on foreign credit institutions 0.15% Number of missed-payments necessary for liquidation to be triggered 3 Standard maturity of loans granted 12 Minimum bank capital adequacy ratio 8% Minimum financial autonomy required of firms to be eligible for loans 70% Minimum financial autonomy required of consumers to be eligible for loans 70% Risk-weight on loans to foreign credit institutions 20% Risk-weight on risky assets 62.5% Haircut applied to the value of the financial asset when estimating recapitalization needs 50% Initial amount of debt securities held by each bank 0

Financial Market Parameters
Coupon rate of the debt security 0,17% Notional amount of the debt security 300,000 Initial price of the financial asset 2055 Agents' propensity to form divergent expectations regarding the financial asset price 0,5 Agents' propensity to form divergent expectations regarding the financial asset price 0,2 Strength of the trend chasing attribute 1 ̅ Attractiveness threshold for the financial asset 0,01 Price adjustment speed 0,1

General Parameters
Expected growth of internal and external demand 0,26% Initial amount of external demand 56,000 Source: Authors

Simulation and Results
As pointed out in the introduction of the dissertation, this study focuses on developing a model that replicates the conjunct dynamics of a financial market, a banking system and the real economy, mimicking several stylized facts of the latter and grasping, to a certain extent, the implications of banking regulation in economic performance.
Specifically, and based on the model described in section 3, policy experiments are designed to answer the following seminal questions: 1. Are banks key drivers of economic performance? 2. What impacts do different micro-prudential regulations have in economic growth?
3. Are macro-prudential policies effective promoters of financial stability?
In line with the methodology used to calibrate the model, each experiment encompassed 1000 simulations of 40 years (i.e., 480 time steps). For each run, the average of each indicator variable was computed using only the last 35 years to get rid of transients and capture only the system's stochastic steady state. Finally, the median of the simulation averages was computed.
The only exception to this methodology was the calculation of GDP related variables, which were assessed based on a time series composed of the cross-run averages of the economy's domestic product at each time step.

The role of banks
From the theoretical standpoint described in section 2.1.1, and following the seminal paper of Diamond and Dybvig (1983), banks play a crucial role in economic growth by pooling liquidity from depositors (who prefer to have their savings placed in liquid instruments) and channeling the funds to firms (who require long term, large sum investments in order to generate returns in the future).
This view is corroborated by the policy experiments of our model. To assess the importance of banks in economic performance, a comparison was made between the most relevant economic aggregates resulting from simulated economies with and without banks. Specifically, we compare the model results in the baseline calibration with the model results in a scenario where banks are shut down. Operationally, the behavior of banks was adjusted in order to require from any loan applicant a financial autonomy ratio larger than 100% (which is naturally not feasible). Banks were thus turned, in this scenario, in mere operators of the economy's payments systems (which are, nevertheless, a crucial infrastructure of modern economies).
From the results laid down in Table 4 it is clear that the existence of banks improves economic development. Indeed, all measures display a manifest degradation in median performance when banks are suppressed.
The reason for these phenomena is related with the inner workings of the modelled economy.
Since no aggregate equilibrium relationship is forced between the agents' actual and expected demand, out-of-equilibrium dynamics are created at the level of each specific agent. Since there are no market-clearing mechanisms, the economy spontaneously self-organizes towards a state in which demand persistently exceeds supply. Economic growth in our model is thus a function of the firms' capacity to increase production capacity through investment. The inexistence of banks is a clear obstacle to this process, since in this scenario companies' investment capacity is limited to their ability to endogenously generate capital. As such, the capacity of the economy to meet aggregate demand is severely hampered when there is no banking system, as reflected in the difference of the output gap (here defined as the difference between potential GDP if all demand was met and actual GDP) in both scenarios (3.31% in the baseline calibration versus 7.35% in the setting with no banks).
This mechanism is also the reason why GDP volatility is smaller in environments where credit institutions are shut down (0.86% in the baseline calibration against 0.59% in the setting with no banks). The fact that capital investments cannot be leveraged through bank financing means that production responds less quickly to increases in expected demand. Firms adjust more slowly and smoothly to increases in consumption and exports, which in turns reduces GDP's volatility.
In addition, firm failures are, in our baseline calibration, mostly attributable to potential gaps between expected and actual demand. These gaps can create an unexpected shock to profits which, associated to firm's leverage, may render institutions unable to fulfill their commitments. It would thus be expected that an economy without banks (and hence without credit) would result in decreased default rates.
In a very interesting result, however, the default rate of firms is higher under the scenario where credit institutions are shut down (1.49% in the baseline calibration against 5.20% in the setting with no banks). This curious feature of the model is attributable to the fact that firms face a monthly capital depreciation rate. Without banks to finance reinvestment, firms are often unable to secure the amount of cash needed to prevent capital erosion. Ultimately, the depreciation of capital reduces firms' production capacity to the extent of making them unable to achieve break-even, in which case firms default due to their inability to pay wages. Based on this, it is possible to conclude that banks do not only foster growth, they also contribute to the maintenance of the current level of wealth as measured by an economy's production capacity.

The impact of micro-prudential policies
The impact of micro-prudential regulation is gauged by considering alternative scenarios where banks are either riskier or safer than in our baseline calibration. In the risky scenario:  Loan underwriting policies are looser. By imposing a minimum financial autonomy ratio of just 0.3, banks implement lenient credit quality requirements when assessing firms and consumers, thus significantly increasing the universe of loan-eligible agents;  The required capital adequacy ratio is diminished to just 2%, which significantly increases the capacity of credit institutions to provide loans.
In the safe scenario:  Loan underwriting policies are stricter. By imposing a minimum financial autonomy ratio of 0.9, banks implement stricter credit quality requirements when assessing firms and consumers, thus significantly decreasing the universe of loan-eligible agents;  The required capital adequacy ratio is augmented to 15%, a much more creditconstraining scenario than the one imposed in the baseline calibration (8%). As Table 5 clearly suggests, aggregate macroeconomic performance deteriorates has banks become safer. Indeed, the median output gap is higher in scenarios where prudential policies are looser (the output gap stands at 3.26% in the risky scenario, which compares to 3.31% in the baseline calibration and 3.66% in the setting with safe banks). This result corroborates our prior conclusions, according to which increased credit availability improves the creation of value by increasing the speed at which supply is able to meet surges in demand. In the same vein, it is also clear that the mechanisms contributing to GDP volatility are magnified when credit is less constrained.
On the other hand, it is interesting to recognize that the default of firms responds non-linearly to changes in the micro prudential framework. While the scenario with safe banks displays the highest default rate (1.83%), thus confirming that credit is important for firms to stay afloat in periods of low demand, the scenario with risky banks is not the best performer (the default rate is 0.17% higher than in the baseline calibration). This result suggests that there is an inflexion point after which less credit constraints actually contribute to a degradation of firms' financial robustness. Beyond a certain indebtedness level, and in a manifestation of the financial accelerator effect postulated by Bernanke et al. (1996), increased firm leverage (see figure 9) makes credit institutions more likely to face distress (due to increased client bankruptcies and credit losses), which in turn increases companies' likelihood of failure (due to foreclosures by failing banks and credit rationing by low-capitalized institutions).
Also remarkable is the non-linear tradeoff between firm defaults and the output gap. Given that new entrants are smaller than incumbent companies, it would be expected that more firm failures would entail decreased economic performance. This thesis is repudiated by the risky scenario, where the greatest amount of defaults coincides with the lowest median output gap.
Such a result clearly indicates that firm failures are less likely to slow down the economy when firms have easy access to credit, since this source of financing allows entrants to quickly catch up with production requests. Credit-to-GDP -Regular Banks Credit-to-GDP -Risky Banks Credit-to-GDP -Safe Banks

Source: Authors
As expected, the decrease in the capital-adequacy ratios of banks is automatically reflected in increased default rates among credit institutions. On the other hand, increased capital-ratios are not automatically reflected in safer institutions, as seen by the fact that the median bank bankruptcy rate is virtually the same in the scenarios with safe and regular banks. This result indicates that micro-prudential policy entails calibration risk: while increasing capital requirements increases the loss-absorbency capacity of banks, it also decreases their ability to endogenously generate capital through profits.
In addition, and despite the fact that bank and firm failures are much higher in the risky scenario than in the baseline setting, value-creation actually improves. This phenomenon stems from the fact that there is an important circuit-breaker that prevents the modelled economy from suffering terribly with bank-bankruptcies. The effective resolution of credit institutions, which are terminated immediately after entering into technical insolvency (i.e., after displaying capital adequacy ratios below 0), means that depositors are not usually bailed-in to a great extent. As such, bank bankruptcies represent salutary events for the economy, which performs better after getting rid of "zombie-banks" that are not able to support investment due to the fact that they do not comply with micro-prudential requirements.
Relevant insights can also be drawn from analyzing the behavior of the output gap in time.
During the initial stages of the simulations, the gap between production capacity and aggregate demand is large in all scenarios (please refer to Figure 10). In the setting with risky banks, the ability of firms to tap onto external sources of funds is translated into increased leverage and bankruptcies, which in turn contributes to a high output gap when compared to the remaining scenarios. However, as demand grows, firms become able to sustain supplementary levels of debt. The output gap is thus closed much quicker in the scenario where banks are riskier.  In the long run, growth in the economy is ultimately capped by the growth of consumption and exports. Since the economy does not, on average, experience extraordinary spurs of growth, firms are usually able, in all scenarios and towards the end of the simulations, to raise the necessary capital (either endogenously or through bank financing) to meet demand. As such, the output gap in different prudential frameworks tends to converge to the same value as time goes by.  In addition, and since labor costs are fixed in our model, the increase in the size of firms means they are more easily able to sustain additional financing costs, which in turn is reflected in a substantial decrease of the number of firm defaults towards the end of the simulations (please refer to Figure 11). A corollary of these results is that the role of banks (in particular if risky) is more relevant when the economy is performing significantly below optimal levels (i.e., when the economy displays large output gaps).
Finally, and with respect to micro-prudential policy, it is also relevant to disentangle between the effects of changes in the capital framework and modifications to the loan underwriting criteria of banks.
To analyze this, a sensitivity analysis is conducted. Starting from the model's baseline calibration, we vary, ceteris paribus, the minimum capital requirements of banks and the minimum financial autonomy ratios demanded by credit institutions from their customers. In order to guarantee that the stochastic properties of the model do not affect results, the simulation is performed using the same seed value for the random number generator. Figure   12 and Figure 13 display the differences in the output gap between each scenario and the baseline setting (depicted by the red line).
As can be seen, changes to the capital requirements have a severe impact on economic performance. In a clear validation of previous results, it is clear that the output gap is persistently higher (lower) when minimum capital requirements are greater (smaller).
On the other hand, it is also clear that changing the loan underwriting policies has a reduced impact on economic activity. In spite of this, and while there are no relevant differences in median performance between the baseline scenario (median output gap of 2.74%) and the scenario where the minimum financial autonomy ratio is set at 30% (median output gap of 2.91%), it can be concluded that increasing the strictness of loan underwriting criteria tends to degrade macroeconomic performance (median output gap of 4.09%). Source: Authors

An exploratory analysis of macro-prudential policies
As already discussed, the recent financial crisis exposed a vicious circle whereby difficulties in the banking system can prompt a recession in the real economy that then feeds back on to the financial sector. This phenomenon suggests that banks should increase their capital buffers in periods where systemic risk is greater.
The countercyclical buffer was designed to guarantee that the level of capitalization of the banking sector is consistent with the macroeconomic environment in which credit institutions operate. It should be enforced when the Authorsities acknowledge that the levels of credit in the economy are excessive and associated with systemic risk, and is targeted at guaranteeing that banks have sufficient capital to withstand potential future losses in case of a bust.
To test the effectiveness of the counter-cyclical capital buffer, we depart from the baseline scenario by establishing a setting in which the regulator enforces an additional capital surcharge for banks whenever the credit-to-GDP ratio of the economy rises above 35% (which is the average value of the indicator in the baseline cross-run average time series). The buffer is then maintained at least for one year, with the possibility of extension should the economy's leverage remain above the threshold.
The results laid down in Table 6 indicate that imposing a capital buffer of 2.5% (which is the value foreseen in Basel III) or 5% does not have a significant impact on the overall dynamics of the economy. The only visible benefit stemming from this measure is a very mild reduction in the median amount of firm defaults. However, and similarly to the results obtained in the "safe banks" scenario, this is not reflected in persistently lower output gaps (in fact, the opposite occurs).
As already explained, this is underpinned by the fact firm defaults appear not to have a significant impact on economic growth (in spite of new entrants being smaller than incumbent firms). Indeed, increased access to credit allows new players to quickly catch up with production needs, a phenomenon that is able to completely mitigate the slowdown effect of firm bankruptcies. In spite of this, it is possible to conclude that using credit-to-GDP ratios as a trigger to the implementation of the counter-cyclical capital buffer might be, if taken single-handedly, an insufficient approach. The results of our model suggest that credit is most important when the output gap is larger. In practice, this means that high levels of leverage may be beneficial if related with the need to close a large gap between the economy's supply and demand (i.e., when firms have abundant investment opportunities). Since the same level of leverage may be too high or too low depending on the current state of the economy, the establishment of a counter-cyclical capital buffer must also take into account the impact of credit-constraining measures in GDP growth and an assessment of the level of debt that can be sustained by the economy without causing the build-up of system-wide risk.

Endogenizing bank failures
As already described, bank failures are a beneficial phenomenon in our baseline model. A bank that enters into resolution is naturally not complying with minimum capital requirements and is thus not allowed to make new loans. The assumption that this agent can be replaced through foreign investment by a credit institution that can finance economic activity from the onset could be signaled as one of the reasons why our model reveals improved economic performance with risky banks.
To address this shortcoming, we endogenize the cost of bank failures. Like before, depositors of failed banks are still bailed-in, thus absorbing the losses of the institution and raising its capital to zero. Under the new setting, however, depositors further see a part of their deposits being converted into equity so that the new institution complies with minimum capital requirements (specifically, the capital adequacy ratio is set 12% in excess of the minimum in each scenario).
Because bank recapitalization costs are now fully borne out by depositors, the amount of cash available for agents to consume (in the case of consumers) and invest (in the case of firms) is reduced. The consequences of a bank failure for the economy are thus magnified in this setting.
As suggested by Table 7, macroeconomic performance is clearly worst when costly bank failures are built into the model. However, the scenario where banks are risky is still clearly the best performer within the scenarios in analysis. This result thus suggests that the benefits of increased credit availability are able to surpass the costs stemming from a higher probability of bank failures.
When comparing the baseline scenario with the scenario where bank bankruptcy is more costly, it is also possible to conclude that the magnitude of the degradation of macroeconomic performance is inferior in the setting with risky banks. Indeed, the difference in the output gap between the baseline scenario and the current setting is much lower in the setup where the economy is less credit-constrained (change of +0.01% for risky banks, which compares with 0.22% for safe banks). This result once again corroborates the view that increased credit availability is generally beneficial for the economy, even when bank bankruptcies are extremely costly.
Finally, it is also important to note that this exercise demonstrates the importance of effective bank resolution. By proving that costlier bank bankruptcies have a nefarious effect on economic performance, the experiment simultaneously proves that mechanisms that promote the effective resolution of banks by mitigating losses among creditors are of pivotal relevance for economic growth.

Concluding Remarks
In this study, we developed an agent-based computational model through which an exploratory analysis of the economic role of banks under different prudential frameworks was conducted.
In a context where mainstream economic knowledge has been strongly discredited by the subprime mortgage crisis and the ensuing great recession, we provide a relevant methodological contribution to the field of banking research by developing a rich, yet tractable agent-based computational model that is able to replicate the conjunct dynamics of a financial market, a banking system and the real economy.
A calibrated version of the model is shown to provide an intelligible account of some of the most relevant stylized facts of U.S. business cycles and economic growth. In a series of policy experiments, we prove that the model constitutes a powerful laboratory in which to investigate several of the seminal questions that have recently emerged as major concerns for policymakers: 1. Are banks key drivers of economic performance? 2. What impacts do different micro-prudential regulations have in economic growth?
3. Are macro-prudential policies effective promoters of financial stability?
In this respect, our innovative approach gives rise to several results of general qualitative interest, shedding new light into some of the conventional notions underpinning the study of banks and bank prudential policies. Specifically, our analysis suggests that banks are indeed key drivers of economic performance. Through their lending activity, credit institutions are able to facilitate investment, thus promoting growth and alleviating the effect of shocks.
Our investigation also demystifies some of the canons behind micro-prudential regulation, which has recently leaned in favor of ever-increasing capital requirements. In particular, the experiments conducted show that: i. Stricter capital requirements have nefarious effects on aggregate macroeconomic performance by indirectly giving rise to credit rationing phenomena; ii. Credit availability, to the extent that it is not excessive, prevents the depletion of firms' capital and, as such, contributes to a reduction in corporate bankruptcies; iii. Stricter capital requirements are not always conducive to a reduction of bank defaults due to the impact of this measure on the profitability levels of banks.
Our study also fails to find evidence in support of the implementation of counter-cyclical prudential tools. It does demonstrate, nonetheless, that credit availability is more important when the economy is operating far from optimal levels (i.e., when the output gap is larger). By showing that high levels of economic leverage may be beneficial when paired with large output gaps, this result implicitly demonstrates that the credit-to-GDP ratio is not an accurate standalone trigger for the implementation of the counter-cyclical buffer.
Finally, the results of our model also highlight the vital importance of effective resolution frameworks. Such mechanisms significantly contribute to macroeconomic stability by ensuring that "zombie-banks" are shut down in a timely fashion and that depositors are not exposed to extreme losses in case of bail-in.
These insights are of great interest to the regulatory Authorsities, especially in what concerns the negative impact that higher regulatory capital requirements tend to have on macroeconomic growth. Indeed, our conclusions suggest that the introduction of Basel III/CRD IV, where capital requirements are increased and counter-cyclical buffers introduced, may in the end contribute to the worsening of the economic environment. On the other hand, the implementation of the Banking Recovery and Resolution Directive, which aims at overcoming several of the shortcomings in the existing tools available to EU Authorsities for preventing or tackling the failures of banks, is strongly supported.
The pioneering analysis conducted in this study is, however, too stylized to guide policymaking on its own. In many ways, our model constitutes an idealized environment in which (i) firm bankruptcy costs are not fully built into the model and, more importantly, (ii) there is no interbank market and hence no interconnectedness between credit institutions. While not invalidating the fact that our framework provides a fruitful benchmark for analyzing the properties of banking systems, these limitations cannot be neglected.
Going forward, there are thus a number of ways in which this work could be extended. First, the costs associated with firm bankruptcies could be fully endogenized. As it stands, the model builds on the premise that companies can be replaced through foreign investment. Although the economy is still impacted by firm defaults due to the fact that new entrants are assumed to be smaller than incumbents, the current architecture of the model mitigates the negative effects of this phenomenon.
Second, an interbank market could be integrated. As a result, the agent-based framework developed here could be used to study the "domino effect", one of the main drivers behind the recent vicissitudes of financial markets. Specifically, modelling an interbank market would allow the study of its potential to act as contagion channel for liquidity and solvency crises.
Given that our framework is a stylized, yet substantive representation of a real economy, this would complement recent research efforts that have sought to analyze and estimate the effects of credit networks on macroeconomic performance (Delli Gatti et al., 2010;Tedeschi et al., 2012).
As a concluding note, we reiterate the exploratory nature of our work while concomitantly accentuating its methodological contribution to the field of banking research, materialized in the development of a model that provides insights that could otherwise be elusive if scrutinized under the lens of other, more conventional approaches.