scholarly journals GLOBAL MARKET OF PETROLEUM PRODUCTS: CURRENT STATE AND PERSPECTIVES OF DEVELOPMENT

2020 ◽  
pp. 27-39
Author(s):  
K. Bezugla ◽  
N. Kostyuchenko
Keyword(s):  
New Technologies ◽  
Time Series Data ◽  
Supply And Demand ◽  
Price Volatility ◽  
Petroleum Products ◽  
Global Market ◽  
Series Data ◽  
Total Output ◽  
Petroleum Production ◽  
The World

The paper is devoted to the peculiarities and perspectives of the global petroleum market development. The peculiarities of supply and demand formation at the global market of petroleum products are investigated in the article. The balance of supply and demand at the petroleum market is determined. The paper outlines the peculiarities of pricing for petroleum products. The dynamics of price changes on the global petroleum market in the period of 2010-2020 is studied. The conclusion was made that there is a price volatility on the global petroleum market. An analysis of the dynamics and structure of the world petroleum production by regions revealed that the total output of oil has increased due to the development of new technologies and due to the increased efficiency of petroleum production. The performed forecasting made it possible to conclude that petroleum price is expected to increase in the coming two periods. That will allow to establish a balance between supply and demand at the petroleum products’ market. Accordingly, the equalization of supply and demand for petroleum products is forecasted (even despite the crisis in the world). The econometric method of economic analysis was applied in the paper. The authors constructed an additive model for time series data to predict the dynamics of prices on the global market of petroleum products. The model was designed based on 16 observations in the period of October 2016 – July 2020.

Factors Influencing International Institutional Investments

2020 ◽  
pp. 195-212
Author(s):  
Akriti Gupta ◽  
Gurpreet Kaur ◽  
Mahesh Sarva
Keyword(s):  
Financial Market ◽  
Time Series Data ◽  
Trade Openness ◽  
Global Market ◽  
Series Data ◽  
Institutional Investment ◽  
Flow Of Funds ◽  
The World ◽  
International Investors ◽  
Financial Market Risk

At the turn of the 21st century, globalization of developed and developing countries in the world witnessed institutional inflows from international investors which became the main characteristic of global capital markets. The current research has assessed time-series data from 2000 to 2017 to understand how the different elements that have influenced the foreign institutional investments and helped India become a global market for such investors. The results revealed that political risk, financial market development, trade openness of the country, size of the economy, and rate of return on investment are the important determinants in attracting foreign institutional investments in India. The chapter also found economic risk and financial market risk played an insignificant role in determining foreign institutional investment in India. The findings of the research help the present government and market regulators to introduce policies aimed at increasing the flow of funds from international institutional investors.

scholarly journals A Comparison of World and Domestic Price Volatilities of Oilseeds: The Ethiopian Case

2015 ◽  
Vol 7 (2(J)) ◽  
pp. 145-161
Author(s):  
Zerihun G. Kelbore
Keyword(s):  
Poverty Reduction ◽  
Time Series Data ◽  
Market Price ◽  
Price Volatility ◽  
World Market ◽  
Domestic Market ◽  
Series Data ◽  
The World ◽  
Price Risks ◽  
The Government

This study investigates and compares oilseeds price volatilities in the world market and the Ethiopian market. It uses a monthly time series data on oilseeds from February 1999 to December 2012; and analyses price volatilities using unconditional method (standard deviation) and conditional method (GARCH). The results indicate that oilseeds prices are more volatile, but not persistent, in the domestic market than the world market. The magnitude of the influence of the news about past volatility (innovations) is higher in the domestic market for Rapeseed and in the World market for Linseed. However, in both markets there is a problem of volatility clustering. The study also identified that due to the financial crisis the world market price volatilities surpassed and/or paralleled the higher domestic oilseeds price volatilities. The higher domestic oilseeds price volatility may imply that the price risks are high in the domestic oilseeds market. As extreme price volatility influences farmers` production decision, they may opt to other less risky, low-value and less profitable crop varieties. The implications of such retreat is that it may keep the farmers in the traditional farming and impede their transformation to the high value crops, and results in lower income hindering the poverty reduction efforts of the government. This is more important to consider today than was before, because measures undertaken to reduce poverty must bring sustainable change in the lives of the rural poor. For this reason, agricultural policies that enable farmers cope with price risks and enhance their productivity are crucial.

scholarly journals MODEL PENAWARAN DAN PERMINTAAN MINYAK KELAPA SAWIT INDONESIA DI PASAR INTERNASIONAL

2020 ◽  
Vol 35 (3) ◽  
pp. 119-128
Author(s):  
Tia Sofiani Napitupulu ◽  
Djaimi B Akce ◽  
Almasdi Syahza ◽  
Brilliant Asmit ◽  
Syaiful Hadi
Keyword(s):  
Palm Oil ◽  
Time Series Data ◽  
Supply And Demand ◽  
Coconut Oil ◽  
Oil Prices ◽  
World Market ◽  
Least Square ◽  
Series Data ◽  
The World

ABSTRACT   Indonesia is the leading producer of palm oil in the world. In 2016 Indonesia and Malaysia produced 81% of the world's palm oil. This study aims to analyze the response of the supply and demand for Indonesian palm oil in the world market. This study used time-series data from 1980-2016. The model built is an econometric model, simultaneous equations. To answer the research objectives, the data were analyzed using the Two Stages Least Square (2SLS) method. The main finding of this study is that in the short term, there are no responsive variables. In the long term, the variable that is responsive to the supply of Indonesian palm oil is the lag area of Indonesian palm oil. In the Malaysian palm oil supply equation, the response variable is the lag area of Malaysian palm oil. In terms of domestic demand for Indonesian palm oil, there are no responsive variables both in the short and long term. In the equation of demand for Malaysian palm oil, the responsive variables are the price of Malaysian palm oil and the price of Malaysian coconut oil. In the international demand for palm oil, the responsive variables are the increase in world palm oil prices, world palm oil prices, 2-year lag in world palm oil prices, and GDP per capita Pakistan. In terms of price, the responsive variable affecting the price of Indonesian palm oil is the world price of palm oil.

Oil and Methanol Price volatility

2013 ◽  
Vol 03 (08) ◽  
pp. 01-10
Author(s):  
Majid Delavari ◽  
Nadiya Gandali Ali khani ◽  
Esmaeil Naderi
Keyword(s):  
Crude Oil ◽  
Time Series Data ◽  
Price Volatility ◽  
High Sensitivity ◽  
Oil Price ◽  
Series Data ◽  
Cultural Issues ◽  
Crude Oil Price ◽  
Oil Export ◽  
The One

Crude oil as one of the main sources of energy is also the main source of income for members of OPEC. So, the volatility of crude oil price is one of the main economic variables in the world and analysis of the effect of its changes on key economic factors has been always considered as significant. The reason might be the high sensitivity of oil price to political, economic and cultural issues worldwide and consequently its volatility on the one hand, and the high influence of the volatile prices on macroeconomic variables. On the other hand, for different reasons such as oil price volatilities and income from oil export, economic planners and policy makers in Iran have been mainly focused on the promotion of non-oil exports especially during the last few decades. Therefore, methanol as one of the most commonly used petrochemical products has a high potential for production and export of non-oil products in Iran. For this reason, in the present study there was an attempt to examine the relationship between the prices of Iran’s crude oil and methanol using FIGARCH model and based on the weekly time series data related to the research variables. The results of the study showed that the long memory parameter is equal to 0.32 which is meaning the shocks caused by volatility of methanol market and crude oil price to the methanol price were lasting and meaningful and were revealed in the long term.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

scholarly journals Analysis of Budget Imbalance Dynamics in Kenya

2018 ◽  
Vol 4 (4) ◽  
pp. 352
Author(s):  
Alex Oguso ◽  
Francis M. Mwega ◽  
Nelson H. Wawire ◽  
Purna Samanta
Keyword(s):  
Public Sector ◽  
Time Series Data ◽  
Supply And Demand ◽  
Series Data ◽  
Fiscal Consolidation ◽  
The Public ◽  
Distributed Lag ◽  
Seeking Behavior ◽  
Development Goals ◽  
The Government

<p><em>Kenya needs substantial and sustained fiscal consolidation to create fiscal space for financing the government’s election pledges, the Vision 2030 development projects, and sustainable development goals. However, the government has found it hard to sustain its fiscal consolidation attempts. This study investigates the fiscal consolidation constraints that act through the budget imbalance dynamics in Kenya using the </em><em>Olivera-Tanzi effect approach.</em><em> The study covers the period 2000-2015</em><em> using time series data and employs three </em><em>Auto-regressive Distributed Lag (ARDL) error correction models</em><em> in the analysis. The study showed that a </em><em>rise in the general price levels in the economy, adjustment of minimum wages, rise</em><em> in perceived levels of corruption in the public sector and the political budget cycles (occurrence of a general election) worsen the budget imbalances (deficits) thus </em><em>constrain fiscal consolidation efforts in Kenya. The study also demonstrated that </em><em>budget imbalance dynamics in Kenya could partly be explained by the Olivera-Tanzi proposition. </em><em>The study rec</em><em>ommends measures to reduce the fiscal imbalance gap in Kenya, which include controlling both supply and demand side inflationary pressure and dealing with rent seeking behavior in the public sector.</em></p>

scholarly journals Smart Retailing in COVID-19 World: Insights from Egypt

2020 ◽  
Vol 3 (2) ◽  
pp. 132
Author(s):  
Ismail Kotb ◽  
Riham Adel
Keyword(s):  
New Technologies ◽  
Supply And Demand ◽  
Response Strategy ◽  
Retail Sector ◽  
Labor Costs ◽  
Structured Interviews ◽  
Medium Term ◽  
Financial Investments ◽  
The World ◽  
Smart Technologies

COVID-19 pandemic has had a significant impact on business in general and the retail sector in particular which was hardly hit as sales fell by 20 percent during this period. The main challenges that face retailers nowadays during COVID-19 outbreak include: balance in supply and demand; safety of employees; trust between retailer and consumer; distribution - transportation capability; shortage of manpower and consumer behavior (Fernandes, 2020; Kumar et al., 2020; Pantano et al., 2020). Yet, retailers around the world started to understand the benefits and new opportunities offered by smart technologies (Inman and Nikolova, 2017; Renko and Druzijanic, 2014). New technologies like internet of things (IoT) can play a significant role in controlling the pandemic situation in the retail sector and are expected to bring substantial benefits such as lower labor costs, increased efficiency, attraction of new shoppers and the generation of new revenue channels (Roy et al., 2017; Vaishya et al., 2020; Wünderlich et al., 2013). However, only a limited number of retailers has adopted IoT due to the huge financial investments required and uncertainty of customers' acceptance (Alkemade and Suurs, 2012; Evans, 2011; Pantano et al., 2013). It is, thus, expected to see smart retailers taking a series of actions over the short to medium term to conserve cash and structure of their company for post-COVID-19 operations. This research aims to explore the retailers' perceptions towards the adoption of smart retail technologies in Egyptian retailing context throughout conducting a series of semi-structured interviews with mid-to-top level management in different retail organisations and discuss the advantages and challenges expected from smart retail technologies adoption. Moreover, the research explains how smart retail can help overcoming uncertainities and is considered a well managed response strategy by retailers to COVID-19.

scholarly journals BLACK SWAN GLOBAL MARKET: ANALYSIS OF THE EFFECT OF THE COVID-19 DEATH RATE ON THE VOLATILITY OF EUROPEAN FOOTBALL CLUB STOCK PRICES (CASE STUDY OF JUVENTUS F.C., MANCHESTER UNITED, AJAX AMSTERDAM AND BORUSSIA DORTMUND)

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Franco Benony Limba ◽  
Jacobus Cliff Diky Rijoly ◽  
Margreath I Tarangi
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Time Series Data ◽  
Global Market ◽  
Bad News ◽  
Series Data ◽  
Black Swan ◽  
Financial Industry ◽  
Market Effect ◽  
European Football

Abstract: The Covid-19 pandemic that hit the world also directly affected financial markets and global stock markets; this condition in economic terminology is known as the Black Swann Global Market Effect. Black Swan Global Market Effect is also experienced by sports industries in the financial industry, the football industry. The purpose of this paper is to see whether there is an influence between the Covid-19 pandemic conditions on the share value of several major European football clubs, namely Ajax Amsterdam, Borussia Dortmund, Juventus F.C., and Manchester United, as a result of the Black Swan Global Market Effect. The data used in this paper is time-series data from March 2020 to August 2020. Meanwhile, to answer the black swan effect phenomenon, the Threshold Generalized Autoregressive Conditional Heteroskedasticity (TGARCH) method is used. The results showed that stocks that were the object of research (Ajax, Borussia Dortmund, Juventus, and Machester United) showed a large response to bad News (an increase in deaths due to covid-19). Abstrak:Pandemic covid-19 yang mengantam dunia juga secara langsung mempengaruhi pasar keuangan serta pasar saham global, kondisi ini dalam terminology ekonomi dikenal sebagai Black Swann Global Markert Effect. Black Swan Global Market Effect hal ini juga dialami industry-industri olahraga yang berada dalam industry keuangan tersebut salah satunya industry sepakbola.Tujuan penulisan ini adalah untuk melihat apakah terdapat pengaruh antara kondisi pandemic covid-19 terhadap nilai saham beberapa klub sepakbola besar eropa yaitu Ajax Amsterdam, Borussia Dortmund, Juventus FC, dan Manchester United sebagai akibat dari Black Swan Global Market Effect.Data yang digunakan dalam penulisan ini adalah data time series dari bulan maret 2020 hingga agustus 2020. Sementara untuk menjawab fenomoena black swan effect ini digunakan metode Threshold Generalized Autoregressive Conditional Heteroskedacity (TGARCH). Hasil Penelitian menunjukkan bahwa, saham-saham yang menjadi objek penelitian (Ajax, Borussia Dortmund, Juventus, dan Machester United) menunjukan respons yang besar terhadap bad news (peningkatan jumlah kematian akibat covid-19). Black Swan Global Market, Pandemi Covid-19, TGARCH Models

A Deep Learning Based Methods for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognising and extracting the features which affect the pandemic is in the highest priorities. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Out-put Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which is outperforming other proposed method in the literature.

Returns and volume

2020 ◽  
Vol 37 (3) ◽  
pp. 457-473
Author(s):  
Panos Fousekis
Keyword(s):  
Trading Volume ◽  
Time Series Data ◽  
Price Volatility ◽  
Statistical Testing ◽  
Model Specification ◽  
Series Data ◽  
Causal Order ◽  
Content Type ◽  
Daily Data ◽  
The Usa

Purpose The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA. Design/methodology/approach Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing. Findings The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works. Originality/value The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

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