Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Lesław Markowski
 + 1562 word(s) 1562 2022-03-23 04:36:27 |
2 format
Bruce Ren
 -6 word(s) 1556 2022-03-28 03:43:02 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Markowski, L. Market Risk Measures of Energy Companies. Encyclopedia. Available online: https://encyclopedia.pub/entry/21053 (accessed on 17 May 2024).
Markowski L. Market Risk Measures of Energy Companies. Encyclopedia. Available at: https://encyclopedia.pub/entry/21053. Accessed May 17, 2024.
Markowski, Lesław. "Market Risk Measures of Energy Companies" Encyclopedia, https://encyclopedia.pub/entry/21053 (accessed May 17, 2024).
Markowski, L. (2022, March 25). Market Risk Measures of Energy Companies. In Encyclopedia. https://encyclopedia.pub/entry/21053
Markowski, Lesław. "Market Risk Measures of Energy Companies." Encyclopedia. Web. 25 March, 2022.
Market Risk Measures of Energy Companies
Edit
This entry is adapted from the peer-reviewed paper 10.3390/en15062138

Companies in the energy sector, due to their important role in the economy and the specificity of energy sources, are exposed to many types of risk, ranging from the risk associated with the company’s operations and the global economic and political situation in the world. Energy companies are usually large capital companies whose shares are listed on the stock market. The mentioned risk factors may shape the risk level of these companies. 

energy sector ROA downside risk LPM semivariance accounting beta CAPM D-CAPM quantile regression

1. Introduction

Considerable fluctuations in power row material costs in the years 2020–2021 caused by the COVID-19 pandemic and the speculative and political actions of countries on the energy markets show how much this sector of the economy is exposed to this risk [1]. Energy companies around the world are struggling with numerous difficulties, such as: the volatility spillovers and co-movements of commodity prices and stock prices [2]; price shocks, especially in the oil and gas market [3][4]; the need to meet the conditions contained in the European Union energy packages; the impact of new technologies and advances in digital technology; and increasing the scale of international actions to prevent global warming [5]. The Polish energy industry is an important element of the European Union’s energy system, and it is a bridge connecting the countries of northern Europe with the countries of western and southern Europe. Over the last few decades, hard coal and lignite were seen as essential energy raw materials. Currently, this sector is undergoing transformation, where the importance of other energy sources such as natural gas, biogas, photovoltaics, wind energy, and hydrogen is growing [6]. These sources are part of the energy mix which is implemented in the European Union’s energy policy [7]. In addition, technologies are being implemented that make better use of traditional fuels, an example of which is the development of high-efficiency cogeneration fired with natural gas. Thus, under the conditions of potential risks, the production, extraction, and distribution of energy will fluctuate. This will probably have a significant impact on changes in the revenues and profits of energy companies. This translates to a further extension into the level of fundamental and investment risk on the capital market. Companies in the broadly understood energy sector attract the attention of stock exchange investors. The risk of investing in such companies then becomes of particular importance.
Risk and uncertainty are inherent in any business. Thus, quantifying, identifying, and controlling risk is essential for business practitioners and scientists. For listed companies, the risk may be considered from the company’s side but also the side of the stock exchange investor. A natural assumption is that the risk to which a listed company is subject translates into the risk borne by a stock exchange investor. Here considers the investor’s risk in the context of total volatility measured by the standard deviation and semi-deviation of returns on the capital market. In the context of systematic risk, the measure of which may be the beta coefficient from the Sharpe model and its modifications in the context of the downside risk. The volatility of the financial result measures the risk from the side of the enterprise. The return on assets profitability ratio (ROA) is used to measure the enterprise’s relative profitability. The ROA is frequently used as a measure of financial performance [8]. Finally, the accounting beta and downside accounting beta are used to test the sensitivity of the financial result to the general situation in the Polish energy sector.

2. Market Risk Measures of Energy Companies

This risk is most often considered in the context of the modern portfolio theory, and capital asset pricing model (CAPM) proposed independently by Sharpe [9], Lintner [10], and Mossin [11]. The first is the theory of risk-based decision-making based on the diversification technique. The second is a method of determining the equilibrium prices of securities depending on the risk they represent. This model identifies the sources and risk measures that are appropriate from the point of view of the portfolio theory. Empirical confirmation of the relationship between profitability and risk is important for decisions making regarding the selection of portfolios.
The CAPM stems from the equilibrium theory in which investors maximize an expected utility function that is based on the mean and variance of returns of their portfolio, and the distributions of return are normal. Despite this, variance may be considered an inappropriate measure of risk. Most of the distributions of rates of return are neither symmetrical nor normal, which is confirmed by many empirical studies. Moreover, investors, usually unevenly risk-averse, treat deviations above the threshold (e.g., expected value) as a profit and below deviation as a potential loss. Risk aversion creates a new concept called downside risk, the possibility of achieving the returns below the assumed rate of return. The main measure of the total downside risk is the semi-variance or semi-deviation of returns. For symmetric distributions, the semi-variance is at least the same appropriate measure of risk as variance, while asymmetric distributions present a much more useful measure than a variance. Semi-variance belongs to the group of asymmetric measures known as lower partial moments (LPM). Bawa (1975) [12] showed that semi-variance is a second-order LPM, and in the case of investors with decreasing risk aversion, it is an appropriate measure of total risk. In addition, downside risk measures can be viewed in the context of the joint changes of two risky assets or portfolios. These measures are asymmetric covariance. Semi-covariance is an example of such measures and was proposed by Hogan and Warren [13]. Bawa and Lindenberg [14] incorporated semi-covariance in the structure of n-degree LPM measures, often called asymmetric co-LPM. The analysis of the mean-risk relationship in the context of the semi-variance for the assumed rate of return can also be found in Fishburn [15]. The risk measures described in the above studies contributed to the development of the CAPM in downside framework theory. The first study in this area was the research by Hogan and Warren [13], who demonstrated the permanence of the downside CAPM (D-CAPM) with the conventional CAPM. Subsequently, Bawa and Lindenberg [14] showed that the conventional CAPM is a mean-lower partial moment model in which the threshold is equal to the risk-free rate. Another more general approach was taken by Harlow and Rao [16], assuming the threshold rate of return as the average rate of return.
Many empirical studies based on risk measures in conventional and downside approaches compared the results regarding the construction of optimal portfolios and capital assets pricing. The construction of a portfolio based on semi-variance was first developed by Markowitz [17]. According to Foo and Eng [18], asset returns are skewed, and then mean-variance portfolio optimization is ineffective. Moreover, when risk aversion is ignored, the (LPM) framework affords a more efficient risk measure. Further, this research shows how to include investor risk aversion into a downside risk asset optimization model. Salah et al. [19] proved that the downside risk model for portfolio optimization overcomes the weakness of the conventional mean-variance model concerning the skewness of returns and the perception of risk among investors. Research supporting the use of downside risk measures in portfolio theory can be found in other works [20][21][22][23].
Further work shows the importance of the downside approach to risk in the pricing of assets. The studies of Estrada [24][25], conducted on developed and emerging capital markets, also show the superiority of downside risk measures over conventional measures based on CAPM. Ang, Chen, and Xing [26], based on individual companies listed on the NYSE, AMEX, and NASDAQ, prove that investors are rewarded with a market premium for the downside risk, which means that securities with higher downside beta ratios achieve higher rates of return on average. Galagedera [27] reveals that the association between the two approaches to systematic risk measures is to a great extent dependent on the volatility of the market portfolio returns and the deviation of the target rate from the risk-free rate. Moreover, downside models indicate a higher level of explanation volatility of rates of return than the classic CAPM [28][29]. Ajrapetova [30] demonstrated that downside beta outperformed conventional beta and other total or systematic risk measures in the developed markets. However, the results of these studies were not confirmed in emerging markets, where total risk measures performed the best. Its results also imply that in emerging markets, the diversifiable risk should be priced. Extensive research in emerging markets, such as those of Slovenia, Croatia, and Serbia, demonstrated that the downside risk is the variable that statistically and significantly explained mean returns [31].
Another point of view is to consider the risk of the company looking at the variability (semi-variability) of profitability ratios. The whole set of measures based on accounting data can be named accounting risk measures. Here, two kinds of accounting risk measures are used: measures of the total variability (semi-variability) of profitability ratios [32] and accounting betas proposed by Hill and Stone [33], and downside accounting beta proposed by Rutkowska-Ziarko [34].
Many different concepts for measuring a company’s risk from accounting information can be found in the literature. A broad review of the various methods is described in the works of Amorim et al. [35], Latif and Shah [36], or Huang et al. [37]. Here, the term “accounting beta” (“downside accounting beta”) is understood as a measure of systematic risk that expresses a change in the accounting profitability of a given enterprise caused by a change in the accounting profitability of the relevant sector or market.

References

  1. Wielechowski, M.; Czech, K. Companies’ Stock Market Performance in the Time of COVID-19: Alternative Energy vs. Main Stock Market Sectors. Energies 2022, 15, 106.
  2. Antonakakis, N.; Cunado, J.; Filis, G.; Gabauer, D.; Perez de Gracia, F. Oil Volatility, Oil and Gas Firms and Portfolio Diversification. Energy Econ. 2018, 70, 499–515.
  3. Kang, W.; Perez de Gracia, F.; Ratti, R.A. Oil Price Shocks, Policy Uncertainty, and Stock Returns of Oil and Gas Corporations. J. Int. Money Financ. 2017, 70, 344–359.
  4. Diaz, E.M.; de Gracia, F.P. Oil Price Shocks and Stock Returns of Oil and Gas Corporations. Financ. Res. Lett. 2017, 20, 75–80.
  5. Sitek, M.; Tvaronavičienė, M. Innovation Management in Polish Real Estate Developers in the Renewable Energy Sources Context. Energies 2021, 14, 1702.
  6. Wolniak, R.; Skotnicka-Zasadzień, B. Development of Photovoltaic Energy in EU Countries as an Alternative to Fossil Fuels. Energies 2022, 15, 662.
  7. Miciuła, I.; Wojtaszek, H.; Bazan, M.; Janiczek, T.; Włodarczyk, B.; Kabus, J.; Kana, R. Management of the Energy Mix and Emissivity of Individual Economies in the European Union as a Challenge of the Modern World Climate. Energies 2020, 13, 5191.
  8. Kludacz-Alessandri, M.; Cygańska, M. Corporate Social Responsibility, and Financial Performance among Energy Sector Companies. Energies 2021, 14, 6068.
  9. Sharpe, W.F. Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. J. Financ. 1964, 19, 425–442.
  10. Lintner, J. The valuation of risk assets and the selection of risky investments in stock portfolio and capital budgets. Rev. Econ. Stat. 1965, 47, 13–37.
  11. Mossin, J. Equilibrium in a capital asset market. Econometrica 1966, 34, 768–783.
  12. Bawa, V.S. Optimal Rules for Ordering Uncertain Prospects. J. Financ. Econ. 1975, 2, 95–121.
  13. Hogan, W.; Warren, J. Toward the development of an equilibrium capital-market model based on semivariance. J. Financ. Quant. Anal. 1974, 9, 1–11.
  14. Bawa, V.S.; Lindenberg, E.B. Capital Market Equilibrium in a Mean-Lower Partial Moment Framework. J. Financ. Econ. 1977, 5, 189–200.
  15. Fishburn, P.C. Mean-Risk Analysis with Risk Associated with Below-Target Returns. Am. Econ. Rev. 1977, 67, 116–126.
  16. Harlow, W.V.; Rao, R.K.S. Asset pricing in a generalized mean-lower partial moment framework: Theory and evidence. J. Financ. Quant. Anal. 1989, 24, 285–311.
  17. Markowitz, H. Portfolio Selection: Efficient Diversification of Investments; Wiley: New York, NY, USA, 1959; Available online: https://trove.nla.gov.au/version/260173474 (accessed on 2 February 2022).
  18. Foo, T.; Eng, S. Asset allocation in a downside risk framework. J. Real Estate Portf. Manag. 2000, 6, 213–223.
  19. Salah, H.B.; Chaouch, M.; Gannoun, A.; De Peretti, C.; Trabelsi, A. Mean and median-based nonparametric estimation of returns in mean-downside risk portfolio frontier. Ann. Oper. Res. 2018, 262, 653–681.
  20. Boasson, V.; Boasson, E.; Zhou, Z. Portfolio optimization in a mean-semivariance framework. Invest. Manag. Financ. Innov. 2011, 8, 58–68.
  21. Pla-Santamaria, D.; Bravo, M. Portfolio optimization based on downside risk: A mean-semivariance efficient frontier from Dow Jones blue chips. Ann. Oper. Res. 2013, 205, 189–201.
  22. Ling, A.; Sun, J.; Wang, M. Robust multi-period portfolio selection based on downside risk with asymmetrically distributed uncertainty set. Eur. J. Oper. Res. 2020, 285, 81–95.
  23. Markowski, L. Capital asset pricing in the classical and downside approaches to risk. Pol. Stat. Wiadomości Stat. 2019, 64, 58–75.
  24. Estrada, J. Systematic risk in emerging markets: The D-CAPM. Emerg. Mark. Rev. 2002, 3, 365–379.
  25. Estrada, J. Mean-semivariance behaviour: Downside risk and capital asset pricing. Int. Rev. Econ. Financ. 2007, 16, 169–185.
  26. Eng, A.; Chen, J.; Xing, Y. Downside risk. Rev. Financ. Stud. 2006, 19, 1191–1239.
  27. Galagedera, D.U.A. An analytical framework for explaining relative performance of CAPM beta and downside beta. Int. J. Theor. Appl. Financ. 2009, 12, 341–358.
  28. Chen, D.; Chen, C.; Chen, J. Downside risk measures and equity returns in the NYSE. Appl. Econ. 2009, 41, 1055–1070.
  29. Tsai, H.; Chen, M.; Yang, C. A time-varying perspective on the CAPM and downside betas. Int. Rev. Econ. Financ. 2014, 29, 440–454.
  30. Ajrapetova, T. Cross-Section of Asset Returns: Emerging Markets and Market Integration. Eur. Financ. Account. J. 2018, 13, 41–60.
  31. Momcilovic, M.; Zivkov, D.; Vlaovic-Begovic, S. The downside risk approach to cost of equity determination for Slovenian, Croatian and Serbian capital markets. Ekon. Manag. 2017, 3, 147–158.
  32. Rutkowska-Ziarko, A. The Influence of Profitability Ratios and Company Size on Profitability and Investment Risk in the Capital Market. Folia Oecon. Stetin. 2015, 15, 151–161.
  33. Hill, N.C.; Stone, B.K. Accounting Betas, Systematic Operating Risk, and Financial Leverage : A Risk-Composition Approach to the Determinants of Systematic Risk. J. Financ. Quant. Anal. 1980, 15, 595–637.
  34. Rutkowska-Ziarko, A. Profitability Ratios in Risk Analysis. In Contemporary Trends and Challenges in Finance; Jajuga, K., Locarek-Junge, H., Orlowski, L., Staehr, K., Eds.; Springer Proceedings in Business and Economics; Springer: Cham, Switzerland, 2020; pp. 77–88.
  35. Amorim, A.L.; Lima, I.S.; Dal-Ri Murcia, F. Analysis of the Relationship between Accounting Information and Systematic Risk in the Brazilian Market. Rev. Contab. Finanç. 2012, 23, 199–211.
  36. Latif, A.S.; Shah, A. The Impact of Quality of Accounting Information on Cost of Capital: Insight from an Emerging Economy. Asian Econ. Financ. Rev. 2021, 11, 292–307.
  37. Huang, W.; Luo, Y.; Zhang, C. Accounting-Based Downside Risk and Stock Price Crash Risk: Evidence from China. Financ. Res. Lett. 2021, 45, 102152.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Agricultural Economics & Policy
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Lesław Markowski
View Times: 705
Revisions: 2 times (View History)
Update Date: 28 Mar 2022
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback