0000000000175262
AUTHOR
Georgiana Vrinceanu
showing 3 related works from this author
The Influence of Oil Price on Renewable Energy Stock Prices: An Analysis for Entrepreneurs
2020
Abstract This study investigates the relationship between oil price fluctuations and renewable energy stock returns using daily data on Brent crude oil prices and global renewable energy stock market indices between 29 November 2010 and 18 February 2020. The investigation is based on the existing evidence on positive correlations between stock prices and oil prices, but it also considers the shift from non-renewable to renewable sources of energy. A two-stage GARCH(1,1) model and a Granger causality test were applied. Our results show that volatility clustering is present in the renewable energy companies‘ stock prices, but, oil price volatility does not seem to induce any significant effec…
Nonrenewable Energy Prices and Stock Prices of EU Financial Companies: A Short Versus Long-Term Analysis
2021
This paper investigates the relationship between financial companies’ stock prices and nonrenewable energy sources prices (crude oil and coal price) using a sample of major financial companies headquartered in the EU. The link between stock prices and nonrenewable energy sources prices risk is modeled using a set of macroeconomic variables, such as Brent crude oil price, coal price, local stock market indices, the EUR/USD exchange rate, long-term interest rates and a global volatility measure (VIX). We apply panel data as the base econometric model and an ARDL extension that sheds light on the long versus short-run exposure of EU financial companies to nonrenewable energy prices volatility.…
Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity
2021
Our paper addresses the relevance of a set of continuous and categorical variables that describe industry characteristics to differences in performance between foreign versus locally owned companies in industries with dissimilar levels of technological intensity. Including data on manufacturing sector performance from 20 European Union member countries and covering the 2009–2016 period, we used the random forests methodology to identify the best predictors of EU manufacturing industries’ a priori classification based on two main attributes: ownership (foreign versus local) and technological intensity. We found that EU foreign-owned businesses dominate locally owned ones in terms of size, wh…