Monetary Regime Choice and Optimal Credit Rationing at the Official Rate: The Case of Russia
Stabilizing monetary policy in a small open economy is constrained by the open economy trilemma. In this paper we investigate whether foreign exchange market interventions and the Central Bank’s credit rationing at the official rate (CROR) may soften this constraint and improve the results of monetary policy for different monetary regimes. We construct a DSGE model appropriate for analysing the forward-looking behaviour of households facing non-zero probabilities of losing access to financial market and CROR.
We have found significant credit rationing in the quarterly Russian data of 2001:Q1–2014:Q2. The probability of losing access to financial market and the probability of CROR are estimated as 22% and 66%, respectively. Using Russian data of 2001:Q1–2014:Q2 we demonstrate that CROR provoked forward-looking activity in financial market, which led to more Ruble devaluation in the crises of 2008-2009. It improved poor countercyclical performance of two Russian monetary policy rules, whereas made small effect on welfare. Welfare maximization exercises reveal a trade off between low-inflation and high-welfare solutions and favour of a floating exchange rate regime. We found the optimal value of the probability of CROR in both exchange rate-based and Taylor rule-based models but resulting improvement in welfare is very small.
This article compares the properties of different non-linear Kalman filters: the well-known Unscented Kalman filter (UKF), the Central Difference Kalman Filter (CDKF) and an unknown Quadratic Kalman filter (QKF). A small financial DSGE model is repeatedly estimated by several quasilikelihood methods with different filters for data generated by the model. Errors in parameters estimation are a measure of the filters’ quality. The result shows that the QKF has a reasonable advantage in terms of quality over the CDKF and the UKF, albeit with some loss in speed.
The paper analyses Russian monetary policy in 2004 – 2012. We present a model that describes short run nonlinear monetary dynamics induced by balance of payments and policy shocks. We consider Central Bank’s international reserves volume as the key factor of monetary and exchange rate stabilization using “ad hoc” monetary rule. Empirical analysis of the model is carried out with Bayesian techniques. Estimation measures the difference in Central Bank’s preferences in crisis and no crisis dynamics.
This paper presents a three-sector DSGE model for a small open economy under the intermediate exchange rate regime. The central bank balance sheet equations are added to allow introducing two different monetary policy rules in the model. The principal question is how many independent monetary policy rules do we need to describe Russian monetary policy in 2001–2012. To get an answer we perform Bayesian estimation of the DSGE model for four different combinations of monetary policy rules. The main finding of the paper is that describing dynamics of 14 observable variables requires using Taylor rule together with the exchange rate adjustment rule. Two rules do not make an overdeterminacy; they have original transmission channels and reflect two different stabilization principles: output and inflation stabilization (Taylor rule) vs. balance of payments stabilization (exchange rate-based rule).
This article suggests a new approach to an approximation of nonlinear DSGE models moments. This approach is fast and accurate enough to use it for an estimation of nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of suggested approach. Approximations of moments are close to the results of large sample Monte Carlo estimation. Quality of parameters estimation with suggested approach is close to the Central Difference Kalman Filter (the CDKF). At the same time suggested approach is much faster.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.