The impact of monetary policy and bank competition on banking industry risk: A default analysis

This paper examines the long-term effect of various regulatory, bank-specific and macroeconomic factors on the determination of liquidity in Indian banks. For this purpose, the study uses a random effect panel data regression model and tests it with data on Indian banks for 21 years, covering the period from 1996 to 2016. The model considers the effect of regulatory factors, cash reserve ratio, and statutory liquidity, and incorporates four different liquidity ratios specific to the Indian banking scenario. The results of the analysis show contrasting relationships between the independent variables and the dependent variables measured by four liquidity ratios.
It is interesting to note that Indian banks rely more on asset-based liquidity and less on liability-based liquidity. More specifically, the most important liquidity ratio of L1 (liquid assets to total assets ratio) showed a significant relationship with macroeconomic variables of discount rates, call rates, foreign exchange reserve, exchange rate with US dollar, consumer price index and gross domestic product. L1 also showed a significant relationship with bank-specific variables of capital to total assets and bank size. However, the regulatory factors of cash reserve ratio and profitability determined by return on equity (ROE) and non-performing assets were not found to have any effect on liquidity of Indian banks.

The article analyzes the influence of inflation on economic growth and substantiates the main directions of increasing the effectiveness of the central bank's anti-inflation policy.
In order to determine the limit of inflation, the excess of which has a negative impact on the economic growth, the relationship is analyzed between the inflation rate and the real GDP growth rate on the basis of IMF statistics using the example of 158 countries. It was determined that in 2010–2017, in the global economy, the 6.0% inflation was the marginal value of the inflation rate, beyond which the economic growth rate declined or slowed down.
Given the inverse relationship between the inflation rate and the real GDP growth rates as well as empirical calculations for the period 1996–2017, the threshold for inflation rate for Ukraine at the level of 4.51% was determined based on empirical calculations for the 1996–2017 period. The results indicate that the National Bank of Ukraine set the inflation target above the level of the calculated threshold inflation.
It has also been established that the link between the rates of nominal GDP growth, as opposed to real GDP, and the inflation rate, is more direct and more tight. It is substantiated that to analyze such dependence it is better to use GDP deflator instead of CPI. The results indicate that deflation constrains economic growth much less than inflation.
In order to increase the effectiveness of the central bank’s pro-cyclical monetary policy aimed at supporting economic growth, the relationship between the rates of real GDP growth and the indicator characterizing the gap between the growth rates of M3 and inflation, which actually reflects the real money supply dynamics, is determined. The results obtained allowed to conclude that in 2009 and 2014-2017, the artificial “squeezing” of the money supply took place in Ukraine, resulting in a decrease in the level of the economy monetization by 22.0% in 2017 compared to 2013.
It has been proved that in order to minimize the negative impact of inflationary processes on economic growth, the policy of the National Bank of Ukraine should be aimed at eliminating the artificial squeezing of the money supply through a reasonable increase in the economy monetization and the implementation of an effective monetary policy.

The government bond (GB) has become the most attractive investment portfolio option, even though many macroeconomic factors affect the bond yield. This paper aims to investigate the determining factor of local currency government bond yield by considering the inflation rate, credit default swap, stock market index, exchange rate, and volatility index. This study used 240 data panel from the Bloomberg stock market in the form of data panel covering Southeast developing countries, namely Indonesia, Thailand, Malaysia, and the Philippines, for five years or sixty months from January 2015 to December 2019. Data analysis used recursive models and multivariate regression techniques using EViews software. The random effect model results revealed that change in the foreign exchange rate and volatility indexes affected, partially and simultaneously, the changes in the stock market index. The result also showed that changes in the stock market index, inflation rate, and credit default swap affected, partially and simultaneously, government bond yield changes. These results suggest that the government bond yield could be managed by controlling volatility index, foreign exchange rate, stock market index, inflation rates, and credit default swaps. This finding could provide an insight into the policymaker and fiscal authority on managing the risk of government bonds under control during high volatility or even making it reasonably lower. This result could contribute to the current research in the field of financial management.

Acknowledgment
It is the author’s pleasure to thank Muhammad Aulia SE MSc CSA® from the Ministry of Finance of Republic Indonesia, for his invaluable contribution to encourage this study and also to share the data required for this paper. He also delivers essential insights into improving the quality of this work. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.