Modelling the Impact of Macroprudential Bank Regulations for South Africa

Konstantin Makrelov, Rob Davies, Laurence Harris*

Centre for Global FinanceSOAS School of Finance and Management

CGF Seminar1st March 2018

This presentation is an output of the Macroeconomic Modelling for Policy Analysis workstream of the SA National Treasury/UNU-WIDER research programme, South Africa-Towards Inclusive Economic Development (SA-TIED).

Our research question

What is the medium term impact on the South African economy of stricter bank regulations?

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Why ask that question?

• South Africa has a highly developed financial sector, with a strong regime of bank regulation and supervision

• Within it, measures of macroprudential regulation have evolved in line with the principles of Basel III

• The wide-ranging principles of bank regulation under Basel III centre on adequate shareholders’ capital (measured as required minimum capital ratios)

• Their principal objective is to maintain an orderly financial system• But voters could ask ‘what is their impact on the economy as a whole’?

• Long term impact of financial system stability is one possible question • Medium term impact on the economy of banks’ adjustment to meet higher required ratios is our

question

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How do we attempt to answer the question?

We construct a general equilibrium model of the SA economy and simulate the effects across the economy of one type of regulatory change: an increase in banks’ required leverage ratio.Our model builds upon the two main types of applied general equilibrium models: • CGE (Computable General Equilibrium) models

• DSGE (Dynamic Stochastic General Equilibrium) models

But ours incorporates four special features:1. A financial sector that interacts with real sectors2. A theory of how banks’ behaviour adjusts to changed risk and costs 3. Constraints ensuring consistency between stocks and flows4. Expectations determining households’ saving are ‘bounded rationality’ not ’rational

expectations’.

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An overview of the paper comes next

I will give it in five parts:

1. South African context2. Simulation set up3. Results4. Key elements of our model5. Implications

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Context: South Africa’s financial sector

• SA’s financial sector is not typical of middle income developing countries. It is characterised by highly developed banks, investment funds, markets, regulatory and legal institutions, and financial ecosystem.

• Some indicators : South Africa has the world’s 12th most highly developed financial sector (WEF Global Competitiveness

Report 2016)

The Johannesburg Stock Exchange is ranked 18th globally in terms of its market capitalisation.

The SA rand is the 20th most traded currency globally (BIS Triennial Survey 2016).

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Context: South Africa’s financial sectorSouth Africa committed to implement Basel III by 2019. It has implemented Basel III minimum capital requirement ratios and banks’ actual ratios have risen :

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Basel III(Regulatory Minimum)

South Africa (2008) Actual

South Africa (2016) Actual

Capital/Riskweighted Assets

8.0 13.0 15.2

Capital/Total unweighted assets (Leverage Ratio)

3.0 5.7 7.8

Simulation set-up

We want to find the economy wide effects of an increase in the banks’ required leverage ratio.

The required ratio (RR) is defined by:

RR = Bank’s equity capital/Bank’s assetsBank’s equity capital = Paid up shares at book value + retained earningsBank’s assets = Loans + other assets

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Simulation set-up

Calibrate our model to SA economy and solve for equilibrium values.

Starting from initial equilibrium we introduce this shock: Regulators raise the minimum required leverage ratio by x basis points

To meet the new requirements banks adjust their balance sheets either by:1. Reducing bank assets by reducing loans, (Simulation 1) or 2. Reducing loans (assets) and increasing bank equity capital by increasing retained

earnings (cutting dividend payouts by 10 per cent)(Simulation 2)

Each response has financial effects on firms and households (and other sectors) which alter their investment, saving, and production decisions.

The simulations trace the evolution of those changes over the following periods until the new general equilibrium is reached.

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Some results: interest rate adjustments

Evolution of banks’ lending rate:

Why the initial rise?Because of the theoryof bank behaviourbuilt into our model. We’ll get to that soon …

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Some results: The real stuff

Real economy equilibrium values after ten period adjustment

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Summary of complete results

• The impact depend on how the banks choose to achieve the higher leverage ratio requirements.

• If banks achieving a higher leverage ratio entirely by reducing their asset holdings there is a large negative effect on the real economy. But if they simultaneously increase the level of retained earnings the negative effect is lower.

• If, instead, banks can raise equity capital (share issues) at zero or low cost, the negative impacts on economic activity of increased required bank leverage ratios are small, but the impact increases if banks widen lending spreads and their profit rate in order to attract capital.

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Key elements of our model

A Stock-Flow Consistent (SFC) model orConsistent Financial-Real (CFR) model

Micro foundations generate real and financial behaviour functions for:Firms(non –financial)HouseholdsFinancial firms Foreign sector

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Households Non-financial Sector

Foreign sector Government

Financial Market

Product Market

Factor Market

Rates of return

Prices

Factor payments

Production using K and L

Diagrammatic representation of the

model

In each period every institution makes the following decisions

1. How much to save and invest2. What assets and liabilities to

accumulate and how to fund the investment-savings shortfall

There are decisions made only by some institutions:1. Household make decisions

about consumption2. The Reserve Bank makes

decisions about monetary policy and money supply

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Model constraints and dynamics

• Real economy flow consistency based on the model developed by Devarajan and Go (1998) i.e current account balance constraint, savings–investment constraint

• Financial flow consistency through a set of identities which ensure that changes in the asset side are matched by changes on the liability side.

• Financial stock consistency by updating the stock levels in each period.

• Key dynamic equations• Labour force• Capital stock• Tobin asset demand function• Price and price expectations• Financial wealth

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Key elements of our model:SA Data

…

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Flow relationships in the model reflect the data in our financial Social Accounting matrix

Source: Hubic (2012)

• Investment plus the change in assets must equal savings plus the change in liabilities

OR

• Uses of funds must equal the sources of funds

Table 1: Simple representation of a financial Social Accounting matrix

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To generate the change in assets and liabilities, we use the flow of funds data

Cash and Deposits Equities LoansCash and demand monetary deposits; Short/medium-term monetary deposits; Long-term monetary deposits; Deposits with other financial institutions ; Deposits with other institutions;

Securities of public enterprises; Ordinary shares; Interest in retirement and life funds; Other loan stock and preference shares;

Bank loans and advances; Trade credit and short-term loans; Long-term loans; Mortgage loans;

BondsTreasury bills; Short-term government bonds; Long-term government bonds; Non-marketable government bonds; Securities of local governments;

OtherGold and other foreign reserves; Other bills; Foreign branch/head office balances; Amounts receivable/payable Other assets/liabilities; Balancing item;

Table 2: Flow of Funds data from the SA Reserve Bank Quarterly Bulletin Table 3: Aggregation of financial instruments

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But we are also interested in stock consistency

• Our institutional balance sheets deal only with financial instruments.

• The general approach followed is to add up the data from the flow of funds over the period 1970 to 2001.

• The approach is similar to the perpetual inventory method and resembles the method employed by Aron and Muellbauer (2006) and Aron, Muellbauer, and Prinsloo (2006).

• Our second step is to compare the values of stocks generated in the first step against the balance sheet information presented in the Quarterly Bulletin for some of the institutions.

• A key challenge with the flow of funds data is that it does not distinguish between changes due to changes in the holding of units, changes due to changes in the price of the financial instrument and other changes.

• The data is updated with the balance sheet information from the Quarterly Bulletin where significant differences exist and it is reasonable to assume that the differences are primarily because of uncaptured capital gain effects.

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Key element of our model: Banks’ behaviourfunction

Adapting Woodford (2010) we model banks’ raising their interest rate spread as higher regulatory requirements increase intermediation costs (We also build on the Borio and Zhu 2012 theory of bank behaviour based on banks’ response to risk).

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Other elements of our model?

Please come back another day for the real fun with the heavy stuff …

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Some implications for monetary policy

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• Change to required leverage ratios can affect the transmission of monetary policy decisions. This can cause lending rates to deviate from what a central bank may perceive as optimal levels, and have unintended consequences for the economy because of changes in bank interest rate spreads.

• Policy makers need to understand the impact of macroprudentialinterventions on bank spreads, lending rates and loan supply and the feedback effects through the real and financial behaviour of other institutions.

• The Committee on the Global Financial System (CGFS 2016) notes that it is uncertain how the introduction of Basel III will impact the monetary policy transmission mechanism. Our model demonstrates some plausible effects.