Modelling economic policy issuesImpact of reserve requirement and Liquidity Coverage Ratio: A DSGE model for Indonesia
Introduction
The global financial crisis highlighted the importance of liquidity regulation in the banking sector (Bouwman, 2014, Allen and Gale, 2017). The Basel III regulation specifically required a bank to hold sufficient liquidity which is measured as Liquidity Coverage Ratio (LCR). The implementation of LCR which forces banks to hold a higher fraction of their assets as high quality liquid securities, may reduce the quantity of bank loans and increase the interest rate on bank loans (Covas and Driscoll, 2014).1 This regulation may also interact with previously existing liquidity regulations such as reserves requirements (RR), which also has a similar impact on lending volume and rates. This paper aims to investigate the impact of a change of the LCR and RR on bank balance sheets and macroeconomic variables, and examine the welfare implication of introducing countercyclical liquidity regulations. Liquidity regulations have been important instruments used by central banks for microprudential, macroprudential, and also monetary policy purposes. Therefore, understanding about the effectiveness and interaction among liquidity regulations is crucial for the central bank policy formulation. However, since the LCR regulations are newly implemented, it is too early for empirical analysis of the effects of their imposition (Bech and Keister, 2017).2
This paper develops a medium scale Dynamic Stochastic General Equilibrium (DSGE) model which takes into account both Reserve Requirement and LCR regulation. The model extends and modifies the framework of Gerali et al. (2010) that includes financial frictions in terms of borrowing constraints, price and wage frictions. In the model presented in this paper, the bank has to comply not only with capital regulations but also with two liquidity regulations: reserve requirement and liquidity coverage ratio. The bank chooses endogenously the optimal level of risk-free assets and reserves taking into consideration the expected liquidity risk and the cost of borrowing from the central bank in the case of liquidity shortage.
Studies that examine the interaction of both LCR and reserves requirement in a general equilibrium framework are somewhat limited. Existing literature that discusses both LCR and reserve requirements is Bech and Keister (2017). They study how the introduction of an LCR requirement affects interbank interest rates, and how it alters the effects of central bank monetary policy operations. However, they use a partial static equilibrium model and focus more on the impact of LCR on the central bank open market operation. Another strand of literature discusses only one type of liquidity regulations, either reserve requirements, LCR, or a general form of liquidity regulations (for example: (Roger and Vlcek, 2011, Covas and Driscoll, 2014, Corrado and Schuler, 2015)). Roger and Vlcek (2011) developed a model to assess the costs of increasing reserve requirements. The disadvantage of their model is that they assume an always binding reserve requirement constraint so that the bank will maintain reserves equal to the required reserve. However, as stressed by Chadha and Corrado (2012), it is essential to allow banks to choose excess reserve holding endogenously. Chadha and Corrado (2012) find that the reserves holding over the business cycle can reduce the volatility of interest spreads to shocks and can act as a stabiliser in the economy. Therefore, Chadha and Corrado (2012) supports the countercyclical policy in liquidity that encourages banks to increase reserve holdings in a boom to limit the expansion of loans and then to release the liquidity in recession preventing a too rapid reduction in loans. De Bandt and Chahad (2016) studies the impact of liquidity regulations using a large scale DSGE model. However, they use an ad-hoc approach to model the bank’s liquidity holding by imposing quadratic adjustment costs when a bank is deviating from the regulations. Moreover, Primus (2017) developed a model with endogenous excess reserves as banks voluntarily demand these assets, and there are convex costs associated with holding reserves. However, he assumes a perfectly elastic supply of liquidity, so that the bank is not subject to stochastic withdrawal risk which has been an essential aspect in reserve management models.
This paper contributes to filling the gap in the literature in the following three aspects. First, the model has an explicit features of both LCR and Reserve Requirement. Second, in contrast with some literature that use an ad-hoc approach to model the bank’s liquidity holding or assume an always binding reserve requirement constraint, this article provides a micro-founded bank liquidity management model in which the bank endogenously determines the optimal level of their reserves and high-quality liquid asset. Third, the model is calibrated for the Indonesia economy, one of the big emerging market economies where banks play a dominant role in the economy, thus this paper can be a benchmark for other emerging countries who adopted both liquidity regulations.
The results of this study show that the impact of changing the two types of liquidity requirements on lending and output are relatively similar. However, lowering the LCR regulation will have consequences on the decline of demand for government bonds, so that it has a different impact on taxes, household deposits and bank profit. In the robustness analysis, I found that some parameters such as cost for adjusting rate on loans to entrepreneur, penalty rate on the liquidity shortages, and volatility of liquidity shock affects the magnitude of the response of bank’s portfolio adjustment and macroeconomic variables on a change in liquidity regulations; however, the direction and conclusion is still inline with the simulation using baseline parameters.3 In the final part, this paper also finds that countercyclical liquidity regulations can improve welfare and reduce the volatility of bank loan.
Section snippets
Motivation for investigating the impact of liquidity regulations in Indonesia
Liquidity regulation has been an important instrument used for monetary, macroprudential and microprudential policy in Indonesia. Reserve requirement has been used as part of monetary policy instruments to control the money multiplier in the economy and to strengthen the transmission of policy rate on the interbank market rate. Moreover, Bank Indonesia, recently issued a new liquidity-based macroprudential policy regulation called Macroprudential Liquidity Buffer (MPLB). MPLB is a refinement of
The model
This paper employs a simplified medium scale DSGE model with banking sector developed by Gerali et al. (2010) and Angelini et al. (2014).5
Calibration
I calibrate the model to match the first moments of some Indonesian data throughout 2005Q3–2017Q4.15 One period is a quarter. The targets of the calibration process are the model’s steady-state values, which are computed using various macroeconomic and aggregate banking data that have been filtered using
Simulation results
This section presents the numerical simulation to explore the responses of the bank and macroeconomic variables to the liquidity regulations. In this section I also study the impact of imposing countercyclical liquidity regulations on welfare and volatilities of various variables under two types of shock: liquidity risk shock and technology shock.
Conclusion
The paper has presented a model in which the bank endogenously determines the optimal level of reserves and high-quality liquid asset under Reserve Requirement (RR) and Liquidity Coverage Ratio (LCR) regulation. The model has been calibrated to match data for Indonesia over the period 2005Q3–2017Q4, to study the transmission of liquidity regulations to the real economy.
This paper found that the effect of changes in RR or LCR on the real sector in term of direction are similar. Lower liquidity
Acknowledgements
This paper builds on a chapter in my thesis at the Durham University Business School: Essays on Macroprudential Policies, Non-bank Financing, and Welfare. The author wishes to thank Professor Tatiana Damjanovic, Professor Gulcin Ozkan, Dr Anamaria Nicolae, and two anonymous reviewers for their valuable comments on earlier drafts. The opinions and conclusions written in this paper are of the author and do not reflect the stance of Bank Indonesia. All errors belong to the author.
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