Purpose: This paper aims to investigate the effect of monetary policy indicators on Latin America’s renewable energy development. The authors conduct several regressions as well as a Principal Component Analysis (PCA) to unveil relationships among possible driving factors among others the current account balance, interest rates, money flow and energy trade balance for Latin America’s energy mix. Design/methodology/approach: The analysis was a two-part process. First, the authors used multiple regression to identify if monetary policy affects the development of renewable energy usage at all. To investigate the singular effects of each of the nine macro-economic variables and four energy indicators, collected from the World Bank (2017) database, several regressions were run where the authors regressed each economic indicator on each energy variable. Then, the authors conducted a principal component analysis with all 13 variables. Findings: The authors found a significant relationship between the clean energy share and governmental spending boosting GDP as well as a significant relationship between governmental spending and the amount of foreign exchange reserves. Declining net energy imports indicate that countries in Latin America are getting more and more energy autonomous for the price of building up huge amounts of foreign exchange reserves. Research limitations/implications: Renewable energy indicators are not always available for all Latin American countries. Data tend to be scattered. However, sources such as the International Renewable Energy Agency and the World Bank database can be complementary. Practical implications: The understanding of the effects and impacts of some of the monetary policy related indicators can provide insights for improving renewable energy financing policies. In turn, such policies can have increased influence on renewable energy sustainability and potentially contribute to improving environmental policies. Originality/value: The specific impact of the selected variables on renewable energy has not been studied. This study attempts to discern the impact of such variables to understand how they influence the renewable energy mix. The insights can in turn inform and modify existing policies and guidelines as well as advise new policy.

The quantity equation is a well-established, theoretic, long-run concept that has been criticized for a variety of reasons, i.e., that no precise statements about causality or dynamics between money growth and inflation can be inferred from its components. These shortcomings can be tackled by estimating inflation based upon a holistic approach and the performance of a ceteris paribus analysis for various levels of quantity and velocity of money, as well as GDP. By testing the validity of the quantity equation, it is possible to evaluate possible effects of elevated budget deficits, unprecedented expansions of the monetary base caused by global lockdowns, and a crash in global productivity, on inflation. The main findings of this paper suggest that the level of productivity is the main driver of inflation. The quantity and velocity of money only play a subordinate role in the determination of the inflation level. If inflation is holistically seen as a function of the quantity and velocity of money, as well as general economic productivity, the level of inflation can be very well explained by comparing the supply side with general economic productivity.

This article analyzes the implicit hedging and liquidity costs of structured equity products offered by various financial institutions. We replicate several payoffs of structured products, compare the calculated fair values based on the Heston model as well as geometric Brownian motion, using various optimization techniques, and compare their fair values with the historic prices traded in the market. We find that implicit hedging costs range between 0.9% and 2.9% markup on the fair value, where we find the underlying market volatility to be the relevant driver of this range for complex structures, while market liquidity can be extracted as the only driver of markups for simple structures with no hedging requirements.

In this paper, we take a city’s budget, which represents the resources that need to be allocated, and test how many blockchain users need to join a voting process of how the city’s resources should be allocated in order to best represent their preferences. This voting process can be tracked very well through the utilization of IoT and smart technology in a smart city. Therefore, we showed that the budget resource allocation of a smart city can be significantly optimized through the utilization of blockchain technology. We found that just a tiny fraction of 0.12% of the population of blockchain participants is needed to significantly represent the spending behavior of the total population. This has significant implications as it shows the strength and importance of a required blockchain in a smart city and its minimal energy consumption requirements.

This article deals with the identification of a superior forecasting method for market liquidity using a calibrated Heston model for the bid/ask price path simulation instead of a standard Brownian motion, as well as a compound Poisson process and inverse transform sampling for the generation of the bid/ask volume distribution. We show that the simulated trading volumes converge to one single value, which can be used as a liquidity estimator, and find that the calibrated Heston model as well as the inverse transform sampling are superior to both the use of standard Brownian motion and compound Poisson process.

This article analyzes the tracking error minimization performances of different optimization techniques applied to equity and fixed-income ETFs by the novel inclusion of the correlation between the index and the ETF in the target function. We compare the index tracking error generated by Genetic algorithm, Particle Swarm Optimization, Levenberg-Marquardt and Nelder-Mead Simplex algorithm and find a superior tracking performance of the Levenberg-Marquardt method in case of the fixed income benchmark replication, and a superior tracking performance of the Nelder-Mead Simplex algorithm for the equity benchmark.