Regulation and Policy Coordination in Normal and Crisis regimes Joe Pearlman City 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 Data 1990 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 macroprudential policy (Cantore, Levine, Melina, Pearlman) • Initially use a model that includes two nontraded 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.