Modeling and Forecasting USD/UGX Volatility through GARCH Family Models: Evidence from Gaussian, T and GED Distributions

Symmetric and asymmetric GARCH models-GARCH (1,1); PARCH(1;1); EGARCH(1,1,); TARCH(1,1) and IGARCH(1,1)- were used to examine stylized facts of daily USD/UGX return series from September 1st, 2005 to August 30th, 2018. Modeling and forecasting were performed based on Gaussian, Student's t and GED distribution densities with a view to identifying the best distribution for examining stylized facts about the volatility of returns. Initial tests of heteroscedasticity (ARCH-LM), autocorrelation and stationarity were carried out to establish specific data requirements before modeling. Results for conditional variance indicated the presence of significant asymmetries, volatility clustering, leptokurtic distribution, and leverage effects. Effectively, PARCH (1,1) under GED distribution provided highly significant results free from serial correlation and ARCH effects, thus revealing the asymmetric responsiveness and persistence to shocks. Forecasting was performed across distributions & assessed based on symmetric lost functions (RMSE, MAE, MAPE & Thiel's U) and information criteria (AIC, SBC & Loglikelihood). The information criteria offered a preference for EGARCH (1,1) under GED distribution while symmetric lost functions provided very competitive choices with very slight precedence for GARCH (1,1) and EGARCH (1,1) under GED distribution. Following these results, it's recommended that PARCH (1,1) and EGARCH (1,1) be respectively preferred for modeling and forecasting volatility with GED as the choice distribution. Given the asymmetric responsiveness and persistence of conditional variance, macroeconomic & fiscal adjustments in addition to stabilization of the internal political environment are advised for Uganda.  Keywords: Forecasting volatility, GARCH family Models, Probability Distribution Density, Forecast accuracy. JEL Classifications: C58, C53, G17, F31 DOI: https://doi.org/10.32479/ijefi.901

Are government energy technology research, development, and demonstration budgets converging or diverging? Insights from OECD countries

Accelerating the pace of energy technological innovation is essential tocurb global climate change and build a clean energy system. Based onthis idea, from the perspective of renewable energy technology gapsacross countries, this study presents a thorough empirical analysis ofthe convergence of energy technology research, development, anddemonstration (RD&amp;D) budgets across OECD countries over 1985–2017.In doing so, we apply two-regimes threshold autoregressive approachto account for heterogeneity, nonlinearity, and transition path in energytechnology RD&amp;D convergence. One observation is that only a smallnumber of advanced countries namely Canada, Japan, and the U.S.follow a nonlinear process and exhibit partial convergence in energytechnology RD&amp;D budgets with Japan as a transition country.Interestingly, our results also provide clear support for the globalconvergence of energy technology RD&amp;D budgets once accounting forthe two regimes jointly. These trends in national energy innovationpolicies could arguably help Canada, Japan, and the U.S. to drive uptheir energy RD&amp;D trajectory into the future, suggesting that thesecountries would be able to achieve a more sustainable energyinnovation supply system.</p

The dynamic nexus of oil price fluctuations and banking sector in China: A continuous wavelet analysis

The fluctuations in World and national energy prices and energy policies might affect the financial and real sectors. This paper focuses on the nexus between oil prices and bank credits in China through continuous wavelet analyses in the following distinct ways. First, we observe the potential effects of oil prices on Chinese claims on the private sector by following the whole sample data, and, sub-samples of the data. Secondly, we account for higher and lower frequencies for sub-samples and the whole sample in the dynamic interaction between oil prices and Chinese claims on the private sector. Thirdly, this study provides more insight into understanding the influence of oil prices on the supply of credits through time-varying estimations at different scales (frequencies). The continuous wavelet analyses demonstrate a positive impact of Chinese oil prices on the Chinese claims on the private sector from 1999:06 to 2019:02 at a 3-8-year cycle which corresponds to the full liberalization of the Chinese domestic oil market. The positive effect of the world oil price on claims on the private sector is, however, restricted only for the sub-period 2005–2012 due to a relatively lower degree of regulation of the Chinese domestic market during this sub-period

Strategies in Sustainable Tourism, Economic Growth and Clean Energy

The effect of tourism development on GHG has been a controversial research topic, and the existing literature fails to provide satisfactory evidence about the impact of tourism on climate change. To the best of our knowledge, this work is the first to study the dynamics of tourism development with several climate-changing substances through time- and regime (state)-varying analysis. Therefore, this article aims at contributing towards a novel analysis of the behaviour of carbon emissions and tourism development in the US following Markov regime-switching VAR (MS-VAR) models. This book chapter will observe the estimates to understand the effect of tourism on air pollution (CO2&nbsp;emissions) at different regimes/states. The stochastic process generating the unobservable regimes is an ergodic Markov chain with a finite number of states (st = 1……N) which is defined by the transition probabilities. Most of the current studies provide mixed evidence on the relationship between tourism and climate change through time- and regime-invariant parameter estimations. In contrast, MS-VAR model predictions reveal the constant term and other parameter coefficients, which are also subject to change from one regime to another regime, to explore the effects of explanatory variables on CO2&nbsp;in the US. The explanatory variables of this work are the Number of Tourist Visiting the US, Energy Consumption of Transportation Sector, and Industrial Production. MS-VAR models also monitored seasonality effects. In the estimations, we aim at observing accurately the impact of tourism on CO2&nbsp;emissions, as well as the effects of industrial production and transportation sector's energy usage on emissions, in the US.</p

The co-movements between geothermal energy usage and CO2 emissions through high and low frequency- cycles

Geothermal energy is considered environmentally friendly than fossil fuel sources, and geothermal power plants are expected to have a low carbon footprint. It is renewable that can last million years. There exist, however, several gases stored under the earth’s surface which are released into the atmosphere during digging (TWI&nbsp;2020). This research paper aims at monitoring the potential positive and negative effects of geothermal on environmental quality (CO2&nbsp;emissions) in the USA for the period January 1980 to August 2019. The paper employed wavelet and partial wavelet coherence computations to explore the impacts of geothermal energy usage on the environment. The concluding remarks from the estimations can be classified into short-term (1–3-year cycle) results and long-term (3–8-year cycle) results. It is found in the short term that (i) geothermal usage increased CO2&nbsp;emissions during 1980–1983 and 1993–1997 and (ii) CO2&nbsp;emissions caused geothermal usage to increase for the period 2000–2009 and after 2015. The paper reveals also that in the long term, (a) geothermal energy consumption increased CO2&nbsp;emissions during the periods 1985–1990, 1993–1996, and 2013–2016 and (b) geothermal energy consumption decreased CO2 emissions for the period 1996–2008 in the USA. This research work eventually yields some relevant geothermal energy policy suggestions for US policymakers to make geothermal more environmentally friendly.</p

Does convergence contribute to reshaping sustainable development policies? Insights from Sub-Saharan Africa

This study examines environmental convergence in ecological footprint and its sub-components in a region subjected to rapid degradation of environmental conditions and, where environmental conservation significantly remains unpopular in both government policy priorities and academic literature. This is expected to contribute to policy-shaping in the region in terms of sustainable development goals and global climate protocols. To this end, the study employs a sophisticated methodological approach (log t regression) that accounts for slope heterogeneity using a pool of inclusive environmental parameters to test convergence among different sub-components. Results show that ecological footprint and its sub-components do not converge as a whole and several clubs are determined for each sub-component except forest-land and built-up-land footprints. Given the importance of achieving sustainable development goals and struggling with environmental threats collectively, this study highlights the importance of differentiated liabilities for countries