Empirical evidence of news about future prospects in the risk-pricing of oil assets

Kakeu, Johnson; Bouaddi, Mohammed

doi:10.1016/j.eneco.2015.10.018

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…The estimation of latent factors is based on the procedure proposed by Bai and Ng (2002) and presented in Appendix A. Other studies that have used the latent factor approach include Bai and Ng (2002); Bernanke and Boivin (2003); Bernanke et al (2005); Favero et al (2005); Boivin and Giannoni (2006); Forni et al (2009); Ludvigson and Ng (2009); Bouaddi and Taamouti (2012); Bouaddi and Taamouti (2013); Kakeu and Bouaddi (2017).…”

Section: Estimation Of the Parametersmentioning

confidence: 99%

“…In a recursive utility framework, the value function depends upon expectations about future consumption growth. Proxying the recursive utility index by using latent factor techniques that capture expectations about the future of the economy is well grounded following tradition in empirical works using recursive utility (Cochrane (2005); Hansen (2010Hansen ( , 2012; Chen et al (2013); Kakeu and Bouaddi (2017)). For instance, Chen et al (2013) use latent factor techniques to explicitly estimate the unobservable continuation value of the future consumption plan in a discrete time Epstein and Zin (1989) recursive utility framework.…”

Section: Estimation Of the Parametersmentioning

confidence: 99%

“…The methodology of latent factor analysis is a technique that allows a large panel of indicators on consumer future expectations to be summarized by a relatively small number of estimated factors. In the past few years, a growing number of applied works in finance and macroeconomics have used latent factor models [see for instance Bai and Ng (2002); Bernanke and Boivin (2003); Bernanke et al (2005); Favero et al (2005); Boivin and Giannoni (2006); Forni et al (2009); Ludvigson and Ng (2009); Taamouti (2012, 2013); Chen et al (2013); Kakeu and Bouaddi (2017); and Thimme (2017)]. To provide endogenous approximation of the value function of the consumer problem, it is replaced with a log linear function of sentiment latent factors and parameters to be estimated through the dynamics of the optimal consumption.…”

Section: Introductionmentioning

confidence: 99%

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Estimating sentiment and risk in a consumption model: a factor analysis approach

Bouaddi

Kakeu²

2023

Macroecon. Dynam.

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This empirical paper deals with the impacts of sentiment about the future, short-run risk, and long-run risk in a dynamic economic model of optimal consumption decisions with Schroder and Skiadas [(1999) Journal of Economic Theory 89, 68–126.] continuous-time stochastic recursive preferences. The empirical strategy combines both a latent factor method and a democratic orthogonalization technique. The latent factor method is applied to a large database of macroeconomic indicators, and a democratic orthogonalization technique is used to separate the relative importance of sentiment about the future and long-run risk channels in shaping optimal consumption decisions. The empirical results suggest that consumers with recursive preferences are not indifferent to long-run uncertainty shocks to future consumption prospects. Endogenous consumption variations are driven by a multicomponent mechanism, where on average, the sentiment component accounts for 15.33%, the short-run risk accounts for 16.89%, and the long-run risk pertains to 34.51%.

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Section: Estimation Of the Parametersmentioning

confidence: 99%

Section: Estimation Of the Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating sentiment and risk in a consumption model: a factor analysis approach

Bouaddi

Kakeu²

2023

Macroecon. Dynam.

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“…Liu et al [2] apply panel models for identification and analysis of influence of oil price volatility on statistical properties of country risk ratings, which stem from uncertainty of macroeconomic fluctuations and find that country risk remains comparatively steady despite of oil price volatility. Kakeu and Bouaddi [3] use an econometric method based on dynamic factor analysis for estimating the pricing equation of oil stocks and find that oil investors care about long-run risks associated with future growth prospects. Lux et al [4] apply the Markov-switching multifractal (MSM) model and a battery of generalized autoregressive conditional heteroscedasticity (GARCH)-type models to forecast oil price volatility.…”

Section: Previous Relevant Studiesmentioning

confidence: 99%

Grey theory based evaluation of importersâ€™ strategies for hedging the price risk in the Tanzanian oil supply chain: a focus on derivative products

Massami

Manyas

Myamba

2018

IJAMR

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The landed cost for oil products in local markets is very often affected by the fluctuation of price related to the international purchasing from oil markets. As a result, oil products are primarily procured via term contracts i.e. derivatives as wholesalers are typically loath to rely heavily on spot supplies as these may be unreliable and exhibit high price volatility. In this study, we apply Grey Theory to evaluate the derivatives based strategies of the Tanzanian oil products imports for hedging the price risk in the local market. After comprehensive evaluation, we find that the applicability of oil derivatives by the Tanzanian importers is high. Thus, the government (i.e. Ministry of Finance and Planning, Ministry of Trade and Industry) and other stakeholders have the obligation to continue bringing awareness on the benefits of the derivative instruments in the purchasing of oil products, which ultimately upon application would bring a relief to all consumers of the oil products in the country. Moreover, as the grey theory can deal with vague and incomplete data, the proposed model can be applied as an evaluation tool for quantifying qualitative data in any industry.

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“…Identifying risk and preventive action are needed for mitigating and minimizing the risks. Some risk analysis researches have been done in some aspect of oil and gas industries such as in financial [8,9] and supply chain modeling [10,11].…”

Section: Introductionmentioning

confidence: 99%

Fuel distribution process risk analysis in East Borneo

Laksmita

Widodo

2018

MATEC Web Conf.

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Fuel distribution is an important aspect of fulfilling the customer’s need. It is risky because it can cause tardiness that can cause fuel scarcity. In the process of distribution, many risks are occurring. House of Risk is a method used for mitigating the risk. It identifies seven risk events and nine risk agents. Matrix occurrence and severity are used for eliminating the minor impact risk. House of Risk 1 is used for determining the Aggregate Risk Potential (ARP). Pareto diagram is applied to prioritize risk that must be mitigated by preventive actions based on ARP. It identifies 4 priority risks, namely A8 (Car trouble), A4 (Human Error), A3 (Error deposit via bank and underpayment), and A6 (traffic accident) which should be mitigated. House of Risk 2 makes for mapping between the preventive action and risk agent. It gets the Effectiveness to Difficulty Ratio (ETD) for mitigating action. Conducting safety talk routine once every three days with ETD 2088 is the primary preventive actions.

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Empirical evidence of news about future prospects in the risk-pricing of oil assets

Cited by 6 publications

References 32 publications

Estimating sentiment and risk in a consumption model: a factor analysis approach

Estimating sentiment and risk in a consumption model: a factor analysis approach

Grey theory based evaluation of importersâ€™ strategies for hedging the price risk in the Tanzanian oil supply chain: a focus on derivative products

Fuel distribution process risk analysis in East Borneo

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