Effects of macro-financial shocks on bank liquidity creation: evidence from Mongolia

Abstract

This paper examines the effects of macro-financial shocks on bank liquidity creation in Mongolia, a developing and commodity-exporting economy, using a structural Bayesian vector autoregression (SBVAR). Ten structural shocks (three external and seven domestic shocks) are identified using a triangular factorization. The main results are (i) the measured bank liquidity creation (as a share of total assets) is procyclical and negatively correlated with the liquidity coverage ratio in the banking sector; (ii) external shocks (the US monetary policy, Chinese economic activity, and changes in the global commodity market) have statistically significant and economically major effects on the liquidity creation. Lending rate, NPL ratio, foreign exchange reserves, and competition in the banking sector are key domestic determinants of liquidity creation; (iii) liquidity creation’s main components respond differently to the US federal funds rate and lending rate shocks. External and financial shocks have contributed to the liquidity creation passing through movements in (illiquid assets + liquid liabilities) component; (iv) monetary policy can be an effective counter-cyclical policy instrument in stabilizing the economic and financial cycles.

Keywords:

• Bank liquidity creation
• external shocks
• business and financial cycles
• monetary policy
• bank competition
• open economy macroeconomics
• Bayesian analysis

JEL CLASSIFICATION:

• C51
• E44
• E47
• E58
• F40
• G21
• G28

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Disclosure statement

All authors declare that we have no conflicts of interest, and no funding was received for this research. The opinions expressed herein are those of the author and do not necessarily reflect the official views of Bank of Mongolia.

Notes

1 They claim that during uncertain times, deposits flow into banks, and banks may lower their lending standards and lend more. The banks’ behavior increases on-balance sheet liquidity creation and may generate asset price bubbles that raises the fragility of the sector.

2 In the case of Choleski factorization (i.e., $\mathbf{D}=\mathit{I}$), the impact period impulse response is given by ${\mathbf{\psi }}_0\equiv {\mathbf{A}}^{-1}.$

3 The Normal-Wishart prior is given by $\left.\varphi \right|\mathbf{\Omega }\sim \mathcal{N}\left(\tilde{\varphi },\mathit{\Omega }\mathit{ }⨂ \tilde{\Psi }\right),$ $\mathbf{\Omega }\sim iW\left(\tilde{\mathit{\Omega }}, \alpha \right)$ with prior mean and variance $E\left(\varphi \right)=\tilde{\varphi },$ $\alpha >n,$ and $\mathit{Var}\left(\varphi \right)={\left(\alpha -n-1\right)}^{-1}\tilde{\mathbf{\Omega }}\mathrm{ }⨂\mathrm{ }\tilde{\mathrm{\Psi }},$ $\alpha >n+1,$ where $\alpha$ is the prior degrees of freedom.

4 China’s real GDP is calculated as a ratio of seasonally adjusted current price GDP in China (CHNGDPNQDSMEI) to CPI, all items for China, index 2015 = 100 (CHNCPIALLQINMEI), both data are collected from FRED economic data of Federal Reserve Bank of St. Louis.

5 All commodity price index, 2016 = 100, includes both fuel and non-fuel price indices.

6 Our choice of λ₁ = 0.1 is in line with the value suggested by Dieppe, Legrand, and Roye (2018).

7 BVAR(3) model is also estimated, and results have been robust, as shown in Section 4.4.

8 In the Mongolian economy, household loans account for almost half of total bank loans, and the average maturity for the salary collateral loan is above 1 year.

9 DIC test result of the $\left(\mathit{illiquid} \mathit{assets}+\mathit{liquid} \mathit{liabilities}\right)$ component for ${\mathcal{M}}_1:$BVAR(1), ${\mathcal{M}}_2:$BVAR(2), and ${\mathcal{M}}_3:$BVAR(3) are estimated as −1474.06, −496.87, and 4565.39, respectively. DIC test result of the $\left(\mathit{liquid} \mathit{assets}+\mathit{illiquid} \mathit{liabilities}+\mathit{equity}\right)$ component for ${\mathcal{M}}_1:$BVAR(1), ${\mathcal{M}}_2:$BVAR(2), and ${\mathcal{M}}_3:$BVAR(3) are estimated as −1427.65, −382.78, and 4034.57, respectively.

10 Lenza and Primiceri (2020) raise an issue that a sequence of extreme observations, such as recorded during the COVID-19 pandemic is capable of severely distorting parameter estimates. Therefore, we compare impulse responses for the full sample including the pandemic period and pre-pandemic sample excluding the latest data.

Additional information

Notes on contributors

Gan-Ochir Doojav

Gan-Ochir Doojav is Chief Economist of the Bank of Mongolia. Doojav received his B.Sc. from the National University of Mongolia in 2005 and completed his PhD in Economics at Australian National University in 2016. He accomplished Doctor of Science (D.Sc.) in Economist at National Academy of Sciences of Mongolia in 2021. He has worked for the Bank of Mongolia for 17 years holding various positions such as Director-General of Research and Statistics Department and Advisor to the Governor. Doojav’s research interests include macroeconomic modelling including DSGE and VARs; central banking and monetary policy; international and development economics; and time series econometric methods.

Munkhbayar Purevdorj

Munkhbayar Purevdorj is a Director of Market Division, Reserve Management and Market Department of the Bank of Mongolia. Purevdorj completed his master’s degree in public policy at Hitotsubashi University. His research interests include monetary economics, central banking and monetary policy, applied econometrics, and international economics.