ARIMA Model based Time Series Modelling and Prediction of Foreign Exchange Rate against US Dollar

Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data. © 2022 IEEE.

Insight into the resilience of the Romanian economy

We are contemporary with various financial crises of global magnitude, starting with the Great Depression of 2008-2009, so-called the "subprime crisis", the economic crisis generated by the COVID 19 pandemic, but also the one that is ongoing nowadays, caused by the Russian-Ukrainian conflict. All these extreme situations generate reactions of the most diverse and challenging to delimit and predict so that the economic entities must show permanent resilience to recover quickly and emerge victorious from the fight with the disturbing phenomena. The present study attempted an x-ray of the Romanian economy after the first year of the COVID pandemic, the most difficult year when the restrictions were among the most severe, analyzing at the same time the years before the beginning of the pandemic for an accurate picture. At the same time, we tried to answer the questions of why a company is more resilient than others using a sample of the top 100 companies in Romania, analyzing the impact on revenue growth of 13 indicators grouped in 4 classes, namely Business efficiency, Sustainable Profitability, Financial Stability, Business dynamics & stability. The obtained results may have significan economic policy implications.

Spatial relationship between COVID-19 and previous infectious disease outbreaks: Evidence from South Korea.

As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.

analysis of thE impact of thE coViD-19 panDEmic on stock ExchangE inDicEs in italy1

The present paper investigates the impact of the COVID-19 pandemic on the prices of the Italian stock exchange indices. During the pandemic, the global economy as well as financial markets suffered due to isolation and social distancing. Paired models of the dependence of the key indices of the Italian stock exchange on the number of patients, recovered and died were analysed using the least squares method. Further, various tests were performed to verify the feasibility of the Gauss-Markov conditions by applying Gretl tools: White Test for heteroskedasticity of residues, Durbin-Watson test for autocorrelation of residuals and normality of distribution of residuals. Statistically significant regression models were constructed that characterise the impact of morbidity and mortality in the Italian population during the COVID-19 pandemic on the price of 11 key stock exchange indices. Based on this, the study examined the COVID-19 pandemic period in the spring of 2020 in Italy, the results of which revealed a loss in stock returns and high volatility in stock returns during this period compared to the normal study period. The econometric model shows that COVID-19 had a negative impact on stock returns and a number of other stock market indicators in Italy. It was revealed that the number of deaths from coronavirus is statistically significantly interconnected with all key stock exchange indices. © Akbulaev N. N., Ahmadov F. S., Mammadova M. R. Text. 2022.

A Bayesian approach for the estimation of weight matrices in spatial autoregressive models

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number of time periods relative to spatial units . When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic.

Interconnections between Social and Economic Indicators in the Context of EU Resilience

The COVID 19 pandemic has once again exposed a number of important risks and problems for the world's economies. Although the present analyzes in the literature are more and more often aggregated between fields, emphasizing the capacity of digitalization and international relations to improve the transition to the circular economy, resilience speaks not only of positive aspects but also of vulnerabilities. Thus, the article deals with the link between vulnerabilities and capacities of the socio-economic domain at EU27 level. The study uses Eurostat data for the period 2011-2020, systematized in the panel form. The results once again demonstrate the need to strengthen public support for health and education, for research and development, in order to reduce socio-economic vulnerabilities at EU27 level, demonstrating the need to correlate policy efforts with results.

LSTM Model based Time Series Modelling and Prognosis of Foreign Exchange Rates against US Dollar

In today's global economy, precision in projecting macroeconomic characteristics such as the foreign exchange rate, or at the very least properly gauging the trend, is critical for any prospective investment. In recent time, application of artificial intelligence-based forecasting models for macroeconomic variables has been extremely fruitful. The global currency rate changed dramatically during the Covid-19 incident. This study examines the behaviour of the Australian dollar's (AUD) daily exchange rates against the US dollar's (USD) daily exchange rates from January 2016 to December 2020 and makes LSTM RNN-based predictions for the 2021 exchange rate. There are different sorts of performance metrics used in exchange rate forecasting to compute the accuracy of the projected result. This research investigates six performance metrics and discovers that the accuracy of the anticipated outcomes is satisfactory when compared to the actual data. © 2022 IEEE.

Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China.

Air pollution may change people's gym sports behavior. To test this claim, first, we used big data crawler technology and ordinary least square (OLS) models to investigate the effect of air pollution on people' gym visits in Beijing, China, especially under the COVID-19 pandemic of 2019-2020, and the results showed that a one-standard-deviation increase in PM2.5 concentration (fine particulate matter with diameters equal to or smaller than 2.5 µm) derived from the land use regression model (LUR) was positively associated with a 0.119 and a 0.171 standard-deviation increase in gym visits without or with consideration of the COVID-19 variable, respectively. Second, using spatial autocorrelation analysis and a series of spatial econometric models, we provided consistent evidence that the gym industry of Beijing had a strong spatial dependence, and PM2.5 and its spatial spillover effect had a positive impact on the demand for gym sports. Such a phenomenon offers us a new perspective that gym sports can be developed into an essential activity for the public due to this avoidance behavior regarding COVID-19 virus contact and pollution exposure.

A Bayesian approach for the estimation of weight matrices in spatial autoregressive models

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number of time periods (Formula presented.) relative to spatial units (Formula presented.). When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

MODELING MIGRATION CHANGES ACCORDING TO ALTERNATIVE SCENARIOS IN THE CONTEXT OF THE GLOBAL COVID-19 PANDEMIC: THE EXAMPLE OF UKRAINE

Global processes significantly affect the mobility of the population. In the context of geopolitical transformation, globalization and quarantine restrictions of Covid-19, it is important to predict the development of the migration movement of countries that are developing. Therefore, the article is aimed at modelling migration changes according to alternative scenarios using the example of Ukraine. The theoretical and methodological basis of the research is formed by a number of scientific works of leading scientists from different countries, statistical information on migration processes and socio-economic indicators of Ukraine’s development, economic, mathematical and scenario methods. In the course of the study, the main factors were identified that more affect the migration processes of Ukraine, taking into account the trends in the impact of Covid-19 on them. These include population size, life expectancy, GDP per capita, average monthly wages, and the volume of remittances from individuals to Ukraine. With the help of correlation-regression analysis, a multivariate econometric model of migration growth (reduction) has been built. This made it possible to study the absolute and relative influence of factors on the magnitude of the migration increase (decrease), determine the potential reserves for its increase (decrease), evaluate them using a comparative analysis and carry out predictive calculations of the volume of migration increase (decrease) in Ukraine. © 2022, Bulgarska Akademiya na Naukite. All rights reserved.

The role of data frequency and method selection in electricity price estimation: Comparative evidence from Turkey in pre-pandemic and pandemic periods

The study examines the role of data frequency and estimation methods in electricity price estimation by applying selected machine learning algorithms and time series econometric models. In this context, Turkey is selected as an emerging country example, seven explanatory variables including COVID-19 pandemic is considered, and daily and weekly data between February 20, 2019 and March 26, 2021 that includes pre-pandemic and pandemic periods are used. The empirical results show that (i) machine learning algorithms perform better than time series econometric models for both pre-pandemic and pandemic periods;(ii) high-frequency data increases the performance of estimation models;(iii) machine learning algorithms perform better with high-frequency (daily) data with regard to low-frequency (weekly) data;(iv) the pandemic causes an adverse effect on the performance of estimation models;(v) energy-related variables are more important than other variables although all are significant;(vi) the share of renewable sources in electricity production is the most important variable on the electricity prices in both periods and data types. Hence, the findings highlight the role of data frequency and method selection in electricity prices estimation. Moreover, policy implications are discussed. © 2022 Elsevier Ltd

Global Challenges vs. the Need for Regional Performance Models under the Present Pandemic Crisis.

The present study uses the analysis of the EU's regional performance structure based on clusters to test the versatility of the regional administrative capacity in relation to three disruptive global phenomena: the economic crisis, the coronavirus epidemic and the phenomenon of refugee migration to Europe. We defined a regional performance model based on maintaining sustainability indicators in the 240 EU regions. The objectives of the study are aimed primarily at a structured assessment of regional administrative capacity in the initial version, based on statistical indicators, and in the current version, after the outbreak of the pandemic, based on quantifying the impact of the disturbing factors. Secondly, the objectives of the study are to evaluate the reaction of the administrative units according to their ability to respond to the economic problems in the region, in the sense of improving the performance of the regional economies. The methods used in this paper will be empirical (the study of the specialized literature), analytical and will contain econometric modelling and statistical processing of the data. The results of the study will allow the identification of the necessary traits to train a leader in regional performance, traits that will be useful to European decision makers in adjusting the EU regional policy. Moreover, the need to redefine the EU in terms of performance will be substantiated once again. The study is current and is based on the latest Eurostat information, pertinent tables and diagrams.