Volumen 9 (2020): Edición 3 (September 2020)

The interferences among some financial, economic and monetary variables are checked as an indicator of economic performance in the long run and for the monetary policy applied between the Great Moderation (GM) of 1987-2001 and the Global Financial Crisis of 2007-2009. For achieving this target, some Granger causality tests are applied to GDP growth, credit growth, and lending interest of 36 countries of the EU and the OECD for the full sample of 1987-2012 and the sub-sample of 2002-2007. Results corroborate the interferences among these variables for the discretionary monetary policy applied immediately after the GM, within the “Ad Hoc Era” or “lax period”, and independence when monetary policy was correctly applied and rules-based.

Fighting climate change is one of the biggest challenges in the 21^st century. Climate change that leads to global warming has been increasingly visible in our environment. Extreme weather conditions such as hurricanes, floods, and droughts have been escalating and their acceleration can be expected in the future. They cause changes in sea levels, epidemics, large fires, etc. Increasingly, we are witnessing minor or major damage caused by these extreme weather conditions. Numerous studies have proven that climate change has negative impact on economic growth and prosperity. However, this paper starts from the premise that in addition to unequivocally identified threats, climate change also creates opportunities.

The paper reaches a conclusion that climate change can adversely affect balance sheets of financial institutions. Therefore, climate change is a source of financial risk and thus a part of the mandate of central banks and supervisors in preserving financial stability. This type of risk has not been given enough attention by either supervisors or financial institutions over the past period. This paper develops a model for managing financial risks as a result of climate change.

Timely and effective climate action is a precondition for the stability of the global financial system and for long-term, inclusive prosperity. Because the Federal Reserve and other central banks share responsibility with legislative and regulatory authorities and other experts for maintaining financial system stability, the Fed also shares responsibility for effective climate action. For climate action to be effective in reducing greenhouse gas emissions and limiting global warming, it must be widespread, it must be substantive, and it must come sooner rather than later. The new low-interest rate monetary policy environment favors sustainable long-term, but also high-risk, investments. Market participants need timely guidance and support from regulatory and supervisory authorities, including the Federal Reserve, in order to expedite global fund allocations to low-carbon assets.

The main goal of our study is to theoretically and empirically contribute to the analysis of relation between institutional features and R&D in business enterprise sector and consequently on sustainable economic growth. We exploited the available measures of institutional quality from World Government Indicators of World Bank and data on R&D in business enterprise sector. We employed First-Differencing GMM method to estimate the model on the balanced panel data including the following European countries: Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Serbia and the Slovak Republic, for the time span of 2007-2017. The institutional features are statistically significant factor of R&D in business enterprise sector. We found that Government effectiveness and Control of corruption in particular are very important in supporting the R&D. These measures of institutional quality are key institutional determinants of R&D in business enterprise sector. R&D in business enterprise sector is one of the crucial parts of the overall R&D, and therefore policymakers have to develop favourable conditions for those activities in private firms. Institutional features should be an important variable in explanation of R&D intensity. The presented analysis empirically contributes to the body of knowledge on determinants of R&D for European countries that have to develop their institutions further in order to grow sustainably. This study is using contemporary methods for testing the underlying problem.

This study reconsiders the Fisher effect for the UK from a different methodological perspective. To this aim, the nonlinear ARDL model recently developed by Shin et al. (2014), is applied over the periods of 1995M1-2008M9 and 2008M10-2018M1. This model decomposes the changes in original inflation series as two new series: increases and decreases in inflation rates. Hence, it enables us to examine the Fisher effect in terms of increases and decreases in inflation separately. The empirical findings support asymmetrically partial Fisher effects for the UK in the long-run only for the first period. Additionally, this study attempts to describe and introduce a different version of the partial effect concept for the first time for the UK.

This study investigates how twelve cryptocurrencies with large capitalization get influenced by the three cryptocurrencies with the largest market capitalization (Bitcoin, Ethereum, and Ripple). Twenty alternative specifications of ARCH, GARCH as well as DCC-GARCH are employed. Daily data covers the period from 1 January 1 2018 to 16 September 2018, representing the intense bearish cryptocurrency market. Empirical outcomes reveal that volatility among digital currencies is not best described by the same specification but varies according to the currency. It is evident that most cryptocurrencies have a positive relationship with Bitcoin, Ethereum and Ripple, therefore, there is no great possibility of hedging for crypto-currency portfolio managers and investors in distressed times.

Current research, especially after the financial crisis, highlights different key determinants of high risk bank profiles. The main aim of this paper is to test, through an empirical model, the impact of various determinants of bank business models on the bank risk with the purpose of enabling early identification of signals of risk and timely application of prudential measures. There are two basic business models for banks: market-oriented wholesale bank business model and client-oriented bank business model. In the wholesale model, a significant share of the assets is comprised of securities in the trade portfolio, the bank is strongly involved in the international financial markets, while on the income side of the bank profile, a large part is related to non-interest income. In the client related business model, classical banking is dominant, which is visible in the high share of loan-related assets, a larger share of self-financing and a larger share of income from interest-operational income in the total income structure of the bank. In the panel analysis of the empirical data, as an indicator of the bank risk profile, the stock market price to stock market price volatility ratio was used with the presumption that the market price and its volatility, with sufficiently liquid shares listed on public stock exchanges, is representative of bank risk. The analysis is conducted on a homogenous example of 20 European banks in the period 2002-2017. Following the econometric analysis, the conclusion is that banks in which business model wholesale characteristics are dominant are more exposed to business risk in periods of market shocks and, as such, represent a danger for the long-term stability of the financial sector.

This study examines the problem that a central bank may face after exiting a monetary quantitative easing policy. It develops a simple dynamic optimization model of a central bank, which finds that if the bank needs to absorb a substantial amount of excess reserves when exiting, the monetary base may become uncontrollable. In this case, the bank has no option but to increase the monetary base by more than the target amount, which leads to an undesirable money supply expansion and, ultimately, to inflation pressures. The model shows the condition when a central bank faces such a challenging situation.

This paper measures the financial strength of the People’s Bank of China from the perspective of balance sheets, and then examine empirically whether its financial strength influences its policy commitments given its financial conditions. The econometric results suggest that, first, the financial strength of the People’s Bank of China does affect its policy performance, although the effects are weak and overall results lack robustness with respect to the econometric technique and the choice of alternative measures of financial strength. Second, alternative financial strength indicator plays different role in helping the People’s Bank of China achieve its alternative policy objectives. Therefore, maintaining benign financial conditions and a resilient balance sheet are necessary pre-conditions for the People’s Bank of China to achieve desirable policy outcomes. Third, the People’s Bank of China’s current standalone finance is healthy under our stressing tests, despite certain concerns attained.

Employing Factor Augmented Vector Autoregression (FAVAR) model where factors are obtained using the principal component analysis (PCA) and the parameters of the model are estimated using Vector Autoregression framework, we analyse how changes in monetary policy variables impact inflation, output, money supply, and the financial sector in India. Our results for the period 2001:04 to 2016:03 show that the benchmark FAVAR model showed more reliable results than baseline VAR model. Benchmark FAVAR model shows the existence of weak ‘liquidity puzzle’ in India. The impulse responses from the FAVAR approach reveal that monetary policy is more efficient in explaining the variations in inflation rather than stimulating output indicating its effectiveness in attaining the objective of price stability.