Financial risk network architecture of energy firms

Restrepo, Natalia; Uribe, Jorge M.; Manotas-Duque, Diego Fernando

doi:10.1016/j.apenergy.2018.02.060

Cited by 64 publications

(40 citation statements)

References 71 publications

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“…This period coincided with a drastic reduction in oil prices. During this period, oil prices decreased due to a significant rise in oil production in the United States and a drop in demand in developing countries [37,39]. It is interesting to note that after the drop in oil, volatility spillover increases.…”

Section: Sgre Vws Xgst Chspte Nrdx Cnementioning

confidence: 97%

“…Following [37][38][39], we compute the volatility connectedness among the clean firms and WTI oil using a 200-day rolling window and 10-day-ahead forecast horizon (as a robustness check, we estimate the model based on alternative rolling windows (150, 250, 500 days), and forecast horizons (20, 30, and 60 days). The results are quite similar and are available upon request).…”

Section: Volatility Connectedness Analysismentioning

confidence: 99%

“…"NET" shows the net spillovers from firm i to all other firms j. TCI is the total spillover index. The diagonal elements correspond to the "own variance share" ( [39]) and are the highest values in the table. The sum of the rows of the pair connection gives an estimate of the total directional spillover "from others".…”

Section: Volatility Connectedness Analysismentioning

confidence: 99%

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Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Foglia

Angelini

2020

Sustainability

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The work investigates the volatility connectedness between oil price and clean energy firms over the period 2011–2020 (including the COVID-19 outbreak). Using the volatility spillover models, and dynamic conditional correlation, we are able to identify the volatility spillover effect between these financial markets and its implications for portfolio diversification. The results indicate a significant change in both static and dynamic volatility connectedness around the COVID-19 outbreak. For instance, total connectedness index changes from 21.36% (pre-COVID-19) to 61.23% (COVID-19). This finding shows the strong effect of the COVID-19 pandemic on these financial markets. Furthermore, we show how the WTI oil from the volatility transmitter (before the outbreak of the pandemic) becomes a risk receiver after the start of the global pandemic COVID-19. Our findings indicate that recent pandemic intensified volatility spillovers, supporting the financial contagion effects. Finally, we determine the optimal hedge ratios and portfolio weights. The estimates provided suggest the need for active portfolio management, taking into account the distinct characteristics of each sector and thus, the firm. For example, the optimal weight analysis shows how the clean sector has become important in optimal diversification strategies. Our results can be used for portfolio decisions and regulatory policymaking, particularly in the current context of high uncertainty.

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Section: Sgre Vws Xgst Chspte Nrdx Cnementioning

confidence: 97%

Section: Volatility Connectedness Analysismentioning

confidence: 99%

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Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Foglia

Angelini

2020

Sustainability

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“…Demirer et al (2018) used the logarithm of daily high, low, opening, and closing prices to estimate return volatility. Restrepoa et al (2018) used the squared returns of each series as a volatility proxy. These two measures tend to agree with each other, so for simplicity but without loss of generality, we omit the previous one and choose to use squared returns of each series as in Restrepoa et al (2018) and apply our proposed VAR model as shown by Equation (1) to this volatility proxy to estimate volatility connectedness.…”

Section: Results and Analysismentioning

confidence: 99%

“…Diebold and Yilmaz (2014) applied the variance decomposition approach to construct directed and weighted networks of financial firms that can characterize the connectedness between firms perfectly. Restrepoa et al (2018) first studied the risk network structure of the largest oil firms on the New York Stock Exchange using these volatility spillover indices. They found that the links between the biggest banks or between the biggest oil firms are tight, so there is no need to consider sparsely connected situations.…”

Section: Introductionmentioning

confidence: 99%

Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network

Chen

Zhang

2020

China and World Economy

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This paper focuses on volatility spillover effects and considers the issue of how to measure the connectedness of networks among financial firms. To assess the network connectedness of firms from different industries, we proposed a novel procedure and applied it to 20 leading financial institutions from four industries in China's stock markets. The results show that the total connectedness of the Chinese financial system was much higher during the stock market crisis between June 2015 and February 2016 than during stable periods of economic development. This analysis can be used to determine which firms play a dominant role in risk transmission throughout the entire system. It is suggested that the government should provide targeted regulatory policies to particular types of firms.

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Dynamic return and volatility spillovers among S&P 500, crude oil, and gold

Balcılar

Özdemir

2019

Int. J Fin Econ

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This paper examines the return and volatility spillover effects among S&P 500, crude oil and gold by employing the spillover index of Diebold and Yilmaz (2012). Monthly realized volatility and return series covering the period from January 1986 to August 2018 are used to examine the return and volatility spillovers. Our findings indicate a bi-directional return and volatility spillover among these assets. The full sample empirical evidence is consistent with the structure in which oil plays a central role in the information transmission mechanism. The role of oil and gold as a safe haven has changed over time in financial and non-financial economic turbulence time-span. Commodity market financialization has decreased the effectiveness of adding commodities to portfolios after 2002.

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Financial risk network architecture of energy firms

Cited by 64 publications

References 71 publications

Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era

Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network

Dynamic return and volatility spillovers among S&P 500, crude oil, and gold

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