Regulation and Policy Coordination in Normal and Crisis regimes

Joe PearlmanCity University

WP9

Participants and Deliverables

• City (Pearlman, Melina) - D9.1, 9.3, 9.7• UvA (Hommes) - D9.4• CERGE (Slobodyan) – D9.2• CEP (Ragot, Iliopoulos) – D9.5, 9.6

• D9.1, D9.4 and D9.2 are being summarized at this meeting

Background to Macroprudential Regulation

• Ayah El-Said has written a paper “On The Impact of MacroPrudential Policy: Lessons From Emerging Markets”.

• Emerging markets employed macroprudential tools for at least two decades to pursue financial stability and reduce systemic risk, with monetary policy pursuing price stability.

• She has examined this in the context of a structural VAR.

Countries of Interest

• Brazil• Turkey– Argentina– Colombia– Mexico– Peru– Czech Republic– South Korea– Russia– South Africa

– Malaysia– China– India– Indonesia– Saudi Arabia– UAE– Egypt

Monthly Data1990 Onwards

Methodology• Uses the SVAR specification •

• Y : a vector of 5 endogenous variables with monthly logarithms of indicators of economic activity, prices, credit, monetary policy, and macroprudential policy.

• Economic activity: unemployment/industrial production• Prices: CPI/core CPI/Housing Specific CPI• Monetary Policy: Policy Rate (ordered last, as it is fast-moving)• Macroprudential Tools

– loan to value ratios, – minimum and total regulatory capital, – Required reserve ratios, effective required reserves – provisioning values (buffer of banks’ own funds from retained profits)

Main Results• LTV ratios are useful for managing price of housing –

significant mainly in Latin America• Increase in provisioning curbs credit growth• Regulatory capital does not have much effect• Required or effective reserves show some effects on

credit• Macroprudential tightening tends to be associated with

currency depreciation which is in line with previous findings.

• Macroprudential policies not always applied countercyclically. In the UAE, the measures were used to lower credit in bad times.

9.1 A stylized model of European monetary union for analysing coordination games for monetary and macro-prudential policy (Cantore, Levine, Melina, Pearlman)

• Initially use a model that includes two non-traded sectors, one traded sector, then estimate using German and peripheral EU data.

• Then investigate a Nash game in simple rules for monetary and macroprudential policymakers

• Currently this work is following Quint and Rabanal (2014), with one or two modifications.

Description of Model• Two-country, two-sector, two agent general equilibrium model of a single

currency area. • Two types of goods, durables and non-durables, produced under monopolistic

competition and nominal rigidities (Calvo or Rotemberg)• Non-durables are traded, durable goods are non-tradable. • In each country, there are two types of agents, savers and borrowers, with

different discount factor and habit formation parameters. Both agents consume non-durable goods and purchase durable goods to increase their housing stock.

• Borrowers are more impatient than savers, which motivates credit• To introduce credit frictions borrowers are hit by idiosyncratic quality shocks to

their housing stock, which affects the value of collateral that they can use to borrow against.

• BGG then applies to residential investment: shocks to the valuation of housing affect the balance sheets of borrowers, which affect the default rate on mortgages and the lending-deposit spread.

Description of Model (cont)

• Domestic financial intermediaries take deposits from savers, grant loans to borrowers, and issue bonds.

• International financial intermediaries trade these bonds across countries to channel funds from one country to the other.

• Thus excess credit demand in one region can be met by funding coming from elsewhere.

• International financial intermediaries charge a risk premium dependent on the net foreign asset position of the country.

Modifications to Quint and Rabanal

• We firstly use a non-separable utility function. This is consistent with – balanced growth– the observed relationship for interest rate in the

Euler equation (Collard and Dellas, 2012)– the increase in consumption in response to an

increase in government spending (Bilbiie, 2009)

(cont)• Secondly we correct what appears to be an error in Quint and

Rabanal.• In their paper their macroprudential instrument is used to regulate

the ratio of borrowings/savings for impatient and patient agents. The instrument reacts to either credit growth or credit/GDP relative to steady state.

• This implies that borrowing could be > saving half the time!• The ratio of mortgage lending to deposits for all banks in the

Bankscope database for 1990-2012 is about 0.5; this will represent our base value therefore for the instrument.

• In addition we allow for the instrument to depend on GDP growth as in Lambertini et al (2013), as recommended by Goodhart, and following new BoE practice.

(cont)

• The welfare in Quint and Rabanal uses a 2nd order approximation, but we use the actual nonlinear form (unlikely though to make much difference).

• We follow Quint and Rabanal and evaluate the effects of non-coordination of monetary and macroprudential policies for a two bloc model of the Euro-area.

• No results as yet.

WP11.2/3: Robust Policy Rules (Amisano, Levine, McAdam, Pearlman)

• We evaluate competing models by Predictive Density Forecasts

• Then use a sample of draws generated by MCMC techniques to design robust simple rules whose average welfare is maximized across the sample.

• The latter technique is very similar to that of Batini et al (2006) and Levine et al (2012)

Novelty of the Work

• The basic idea is that of Geweke and Amisano (2012):• Models M1,…,MM: generate a series of 1-step ahead predictive

densities for y, and define the weighted sum of predictive densities

• A linear pool of the predictive densities is then created and the optimal weights are derived by maximizing that pool of (log) predictive densities:

• Maximizing over the weights usually does not lead to just one model being vastly preferred over others

Modification to Geweke and Amisano

• Since our focus is robust rules, in order to correctly compare models, we need to have the same rule (represented by coefficients r), so we need to solve

• So the algorithm is as follows:• Fix the rule, and optimize for r and wi using Bayesian

maximum likelihood (less time consuming than MCMC)• Estimate each model Mj with optimal rule r using MCMC.• Sample and design the optimal robust rule.

• All steps are in place, apart from the optimal search for .r

Which models will be used?

• Clearly there is a vast range of possibilities• We will restrict ourselves to a small number of

different types of financial frictions, and also address internal and external, and deep habit.

• Then evaluate the robust simple rules.