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Studies and researches
Vol. 14 Issue 1 - 6/2022
The Use of GARCH Autoregressive Models in Estimating and Forecasting the Crude Oil Volatility
Today, oil is one of the most popular commodities traded globally, due to its indispensable character and multiple properties offered to mankind. Increased attention is paid to the analysis of volatile and fluctuating trends in the overall price of this valuable energy source. Using the autoregressive conditional heteroskedasticity models such as GARCH(1,1), GARCH-M(1,1) and EGARCH(1,1), the present study has as a priority objective in estimating and predicting the volatility of the oil returns series (Brent Crude Oil return series) in the 1987-2022. The main results highlighted the preference in using the asymmetric model EGARCH (1,1) on the measurement of conditional variance, showing that Brent Crude Oil reacts over 90% to any existing market’s shock (i.e.: information, events, facts, news, etc.) in a negative manner/way. At the same time, various tests and evaluation conditions were used (ARCH-LM Test, Durbin-Waston Test, High Log likelihood, Lowest Schwarz Information Criteria) in investigating the level of performance in estimation the conditional crude oil volatility. Each GARCH (1,1) model is meeting brilliantly these conditions and acquiring the character of stability and validity in use. At the same time, performing forecast analysis on crude oil volatility in two different time periods: 1987-2022, respectively 2020-2022, it was shown that existence of the phenomenon of clustering-volatility over the time, with strong implications for the functioning mechanism of international financial markets. Fulfilling those restrictive conditions, the symmetric and parametric model GARCH-M (1,1) becomes, in our case, the most efficient model in forecasting the volatility of Brent Crude Oil return series in the analysed period. Read more
conditional variance, GARCH models, crude oil returns, clustering-volatility, COVID-19 Pandemic

C10, C50, C53, C59, Q43
Studies and researches
Vol. 15 Issue 1 - 6/2023
Financial Data as a Mirror of an Economic Story. An Empirical Approach to the Impact of the COVID19 Pandemic on the Financial Equilibrium
The present article aims to analyze how the imbalances generated by the economic and medical crisis caused by the COVID 19 pandemic in 2020 were reflected in the balance and economic performance at the microeconomic level. Two companies operating in two of the most affected economic areas in 2020, Tourism and Pharmaceutical Industry, represented the foundation of our analysis. Accounting data from the Balance Sheet and Profit and Loss Account, available on the website of the Bucharest Stock Exchange, were the main tools for analyzing the impact that the economic and medical crisis had on indicators such as Working Capital, Self-financing Capacity, Current Ratio, Debt / Equity Ratio, Return on Assets, Return on Equity, etc. The period under consideration covers a period of 4 years, i.e., 2018-2021.  The two areas of activity represent, in our opinion, two major poles of the effects of the COVID 19 pandemic, being the loser and the winner (in financial terms) of the economic and medical developments generated by these. The questions we propose to answer based on the financial analysis are: could these two companies also be considered the winner and the loser of 2020 crisis? And what is the story told by the financial data about a company's vulnerability during the crisis? Read more
Financial data, COVID-19 pandemic, financial analysis, balance sheet, profit and loss account

M21, G31
EJIS is published under the research grant no. 91-058/2007 The Development of Interdisciplinary Academic Research Aimed at Enhancing the Romanian Universities International Competitiveness, coordinated by The Bucharest University of Economic Studies and financed by CNMP Romania.
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