Conditional correlations in the returns on oil companies stock prices and their determinants

Giovannini, Massimo; Grasso, Margherita; Lanza, Alessandro; Manera, Matteo

doi:10.1007/s10663-006-9001-4

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…Empirical findings of their multivariate cointegration and vector error correction models (VECM) confirmed the statistical significance of the major financial variables in explaining the long-run dynamics of oil company stock values. Giovannini et al (2006) studied financial risk factors and several integrated oil company stock price returns based on VECM and DCC-GARCH methods. The financial risk factors include stock market indices, foreign exchange rates, and the difference between the 12-month futures price and spot price on the Brent oil spread.…”

Section: Determinants Of Stock Returns Of Oil and Gas Companiesmentioning

confidence: 99%

Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

Liu

Kemp

2019

Energy Economics

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In this study we propose a method of selecting the macroeconomic variables for forecasting the excess return signs of the U.S. oil and gas industry stock index by combining the Forward Sequential Variable Selection Algorithm and information criteria. We select predictors from a large monthly macroeconomic variable dataset designed by McCracken and Ng (2015). The method can adapt to the updated macroeconomic information and the possible time-varying relationship between the macroeconomic variables and the stock return signs. We also propose a method which can change the threshold value of the probit model automatically for considering the potential time-varying risk aversion level of the market participants. Further, we investigate the investment performance of an active trading strategy based on our forecasting model and compare it with a passive buy-and-hold trading strategy for different time periods. Our study is important for both oil and gas industry investors and U.S. energy policy makers. The method that we used in this study offers a solution to the issue of selecting useful information from large datasets and absorbing updated market information.

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Section: Determinants Of Stock Returns Of Oil and Gas Companiesmentioning

confidence: 99%

Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

Liu

Kemp

2019

Energy Economics

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“…Additionally, Osmundsen et al (2006) found that the major determinants of firm valuation are oil price, oil and gas production, and to a certain extent, reserve replacement. To examine the relationship between stock prices returns and financial risk factors of oil companies, Giovannini et al (2006) used multivariate cointegration techniques and multivariate GARCH Model. In an earlier study, Quirin et al (2000) identified reserve replacement, reserves growth, production growth, and finding costs as the major factors of equity valuation.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Value drivers in Oil Companies: An Application of Variance Based Structure Equation Model

Kumar

Sukumaran

2017

CMR

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This study aims to provide new insights into how the financial and operational information relate to the market valuation of both independent and integrated oil companies. Hence, the study examines the value drivers for value creation in oil firms. Specifically, it explores the effect of investment, financing, and dividend decisions on value creation in oil-based energy firms. It is important to identify factors for value creation in stock market to design effective policies for wealth creation. The study was based on 82 oil and energy firms selected based on average asset sizes and revenues during the

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“…Gogineni (2010) determined a significant influence of the price of crude oil on markets which are not immediately related to the oil industry. Giovannini et al (2004) found a number of important dependencies between companies in the oil industry. Bhardwaj and Dunsby (2012) concluded that the relationships between commodity markets and the stock market vary according to the phase of the business cycle.…”

Section: Literature Reviewmentioning

confidence: 99%

Portfolio diversification during monetary loosening policy

Makiel

2015

The Journal of Risk Finance

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of the paper is to analyze the impact of quantitative easing (QE) performed in the USA on relationship between assets mainly from mining and oil industries. Based on the empirical results, the method of diversified portfolio creation has been proposed. Design/methodology/approach -Nine DCC-GARCH-type models have been estimated for each group centered around a main asset: a company from the oil or mining industry, the appropriate currency pair for its market of origin, commodities which could be used for the diversification of risk involved in investing in a portfolio containing the company, and the largest company from the same industry listed on the US market. Each series of conditional correlations was analyzed with regard to the changes that occurred during the various stages of QE. Findings -The correlations are shown to be stabilizing in the successive stages of QE. The most significant changes in the distribution of correlations can be observed after the first stage of QE. The effects of QE are evident not only in the USA but also in other countries; however, the level of its influence varies between different markets and assets. It is possible to diversify the inflation, currency and market portfolio risk by appropriately chosen asset decomposition. Research limitations/implications -The DCC model is limited, so to provide more precise results, more sophisticated models can be estimated and compared. Practical implications -The paper investigate the fact of stabilization in financial markets relations. The findings may prove the validity of continuation of QE. A portfolio creation method has been proposed -it has been stated that including commodity in portfolio is more appropriate then only-bond-equity mix. Originality/value -The new approach of analyzing financial stability has been proposed -the control for stability of conditional correlation.

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Conditional correlations in the returns on oil companies stock prices and their determinants

Cited by 19 publications

References 16 publications

Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

Value drivers in Oil Companies: An Application of Variance Based Structure Equation Model

Portfolio diversification during monetary loosening policy

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