Impact of US dollar exchange rate and inflation on profitability

This study aims to examine the significance of the effect of the dollar exchange rate and inflation on the profitability of PT. Pupuk Sriwidjaja Palembang. Profitability in this study is measured by return on assets (ROA) with the research period from 2014-2020. The data analysis technique for testing the hypothesis in this study used multiple linear regression. The study results concluded that the dollar exchange rate and inflation had a significant positive effect on the profitability (ROA) of PT. Pupuk Sriwidjaja Palembang during 2014-2020.

The Long-Predicted Failure of the Post-Bretton Woods “Non-System”

The monetary system implemented at Bretton Woods in 1944 made the US dollar the centre of the world economic system, with 43 other countries' currencies linked to it via fixed exchange rates. However, once the US government broke its promise to redeem dollars in gold at $35 per ounce on August 15, 1971, expansion of the supply of dollars was no longer constrained, and like many currencies before it, the lack of monetary discipline led to inflation through which the value of the dollar has fallen by about 98%. The “oil shock” of the 1970s led to the introduction of the “petro-dollar” system whereby Saudi Arabia, then the largest oil producer, agreed to accept only US dollars in payment for its oil in exchange for the US government's pledge to defend it. This shored up demand for the fiat US dollar, enabling it to survive until its now approaching endgame.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

THE IMPACT OF COVID-19 OUTBREAK ON CHINA SECTORAL INDICES IN SHORT AND MEDIUM RUN

The novel coronavirus COVID-19 has severe consequences for countries around the globe. The purpose of this study to find out the impact of COVID-19 on China’s sectoral indices in the short and medium run. This research investigates the impact of COVID-19 on stock exchange index, exchange rates, money lending, and oil prices in the Chinese economy in the short and medium run by using secondary data of different sectoral sectors. The outcomes show that COVID-19 has strongly influenced these sectors. The Shanghai stock exchange is showing a decline in transactions due to this pandemic in the short run. But in the medium run, the outcome shown that Shanghai stock exchange has upward trend. The pandemic has reduced exchange rate of Chinese RMB to US dollar in short-run, but in medium run, Chinese RMB has gained appreciation against US dollar. The money lending for medium enterprises is showing downward curve. Result shows that money lending rate has reduced from 3.25% to 3.10% in the short-run and 2.95% in medium run. Overall, pandemic has had significant influence on the Chinese economy and development.

Is Bitcoin Price Driven by Macro-financial Factors and Liquidity? A Global Consumer Survey Empirical Study

This research aims to utilize macro-financial and liquidity elements as the factors that may affect the price of Bitcoin as the largest cryptocurrency in terms of market capitalization. The macro-financial factors analyzed in this study were foreign exchange, stock market index, interest rates, and gold, while liquidity ratio is the internal factor. This study applied a fixed-effect model (FEM) and Generalized Method of Moments (GMM) on gathered weekly data from 1 January 2017 to 29 December 2019 from 18 countries with the total of 2,826 observations. The analysis revealed that US Dollar amplifies Bitcoin trading; an increase in interest rate will decrease investors’ intention to invest in Bitcoin as a speculative asset, and gold could replace Bitcoin as a substitute asset. Moreover, Bitcoin was found to be highly liquid, which attracts many investors, while the stock market index proved to be insignificant.

Modelling the Botswana Pula/Us Dollar exchange rate using the Skewed generalized t (SGT) distributions

The economy of Botswana heavily relies on mineral exports (mainly diamond exports), which are largely dependent on the exchange rate. And, the US Dollar is one of the most important currencies in the basket of currencies to which the Botswana Pula is pegged. Therefore, this paper seeks to empirically establish the baseline characteristics of the Botswana Pula (BWP) and the US Dollar (USD) exchange rate and to identify the most plausible probability distribution from the skewed generalized t (SGT) family that can be used to model the log-returns of the daily BWP/USD exchange rates for the period January 2001 to December 2020. The SGT family is a highly versatile class of models that can capture the skewness and kleptokurticity that are inherent in financial time series. Four probability distributions are considered in this study: skewed t, skewed generalized error, generalized t and skewed generalized t. The maximum likelihood approach is used to estimate the parameters of each model. Model comparison and selection are based on the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The results of the study show that the daily BWP/USD exchange rate series is nonnormal, negatively skewed heavy-tailed. It is also found that, based on the values of both the AIC and BIC, the model that gives the best fit to the data is the skewed t, which is closely followed by the skewed generalized error distribution, while the generalized t gives the worst fit. Keywords: Pula/US Dollar exchange rate, log returns, Generalized t distribution, Skewed generalized error distribution, Skewed generalized t distribution, Skewed t distribution, skewness, kurtosis, maximum likelihood

Analysis of the development of the foreign exchange market in the Russian Federation

The article focuses on the financial foreign exchange market, the development and condition of which determines the financial well-being of most commercial enterprises of the Russian Federation. The purpose of the research is to give review of the Russian foreign exchange market’ development and situation. The main factors influencing the level of the exchange rate of foreign currencies expressed in national currency are considered. The domestic and international foreign exchange market of Russia for the period 2016-2020 is analyzed. The dynamics of conversion operations, the structure of participants in the domestic foreign exchange market by type of currency. The results of trading on the foreign exchange market, futures and options as a currency instrument, the share of options and futures on the futures market of the Russian Federation, as well as the dynamics of the US dollar against the ruble and exchange trading indicators for the period from 2016 to 2020. The conditions, results and prospects of the development of the financial foreign exchange market of the Russian Federation are discussed in this article