Clustering in crude oil prices and the target pricing zone hypothesis

Bharati, Rakesh; Crain, Susan J.; Kaminski, Vincent

doi:10.1016/j.eneco.2011.09.009

Cited by 29 publications

(8 citation statements)

References 50 publications

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“…Different studies evidenced the existence of price clustering in other asset classes such as exchange rates (Curcio and Goodhart, 1991;Sopranzetti and Datar, 2002), initial public offerings and seasoned equity offerings (Kandel et al, 2001;Mola and Loughran, 2004;Hu et al, 2019), commodities (Bharati et al, 2012;Palao and Pardo, 2012), derivatives (ap Gwilym et al, 1998;Narayan et al, 2011), betting markets (A. Brown and Yang, 2016), and cryptocurrencies (Urquhart, 2017;Baig et al, 2019).…”

Section: Previous Empirical Evidencementioning

confidence: 99%

Price Clustering in Bank Stocks During the Global Financial Crisis

Lobão

Pacheco

Alves

2019

SAEB

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Market anomalies are one of the most intriguing and fascinating phenomena observed in financial markets. This paper examines the incidence of price clustering in US and European bank stocks during the Global Financial Crisis. The results reveal a significant level of price clustering in European and US banks’ samples, which is difficult to reconcile with the Efficient Market hypothesis. The Attraction hypothesis and the Price Resolution/Negotiation hypothesis seem to be the best explanations for the clustering effect. However, the results also suggest that the uncertainty associated with the crisis did not have a significant impact in the clustering levels, which is at odds with the recently proposed Panic Trading hypothesis. Surprisingly, we observe a tendency to have less price clustering during the period of crisis and banks located in countries mostly affected by the European sovereign debt crisis exhibit lower levels of price clustering. These results are consistent with the idea that investors tend to be more analytical in their appraisals in periods of negative sentiment.

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Section: Previous Empirical Evidencementioning

confidence: 99%

Price Clustering in Bank Stocks During the Global Financial Crisis

Lobão

Pacheco

Alves

2019

SAEB

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“…Here, $10 can be viewed as the sticky point of the diffusion process which models the stock price. Also, mounting empirical evidence (see, e.g., [3], [5], and [31]) has discovered the existence of clustering phenomena in the prices of stocks or commodities like gold and oil, meaning that the price appears at certain levels much more often than other possible levels. Thus, diffusion with sticky points can nicely capture the price behavior in such situations.…”

Section: )mentioning

confidence: 99%

Some explicit results on one kind of sticky diffusion

2019

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In this paper we derive several explicit results on one special sticky diffusion process which is constructed as a time-changed version of a diffusion with no sticky points. A theorem concerning the process-related Green operators defined on some nonnegative piecewise continuous functions is provided. Then, based on this theorem, we explore the distributional properties of the sticky diffusion. A financial application is presented where we compute the value of the European vanilla call option written on the underlying with sticky price dynamics.

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“…The optimality of this policy can explain the otherwise ad hoc assumption that OPEC pursues a price target. See, for example, Tang and Hammoudeh [2002] in analogy to currency bands and more recently, Bharati et al [2012]. The derived radical OPEC profit maximizing strategy may encourage the build up and depletion of inventories although the price jumps occur at uncertain dates.…”

Section: Summary and Final Remarksmentioning

confidence: 99%

A Rationalization of Ups and Downs of Oil Prices by Sluggish Demand, Uncertainty, and Nonconcavity

Wirl

Caban

2013

Natural Resource Modeling

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Abstract. Motivated by the recent switching between "low" oil prices and "high" oil prices, this paper provides an economic explanation for oil price volatility. Given the characteristics of the oil market-sluggish, concave, and uncertain demand, as well as noncompetitive players-the corresponding profit maximizing strategy is to switch between a low price and a high price depending on whether the current demand is below or above a certain threshold. This provides an economic rationalization of oil price volatility (including the low prices) as alternative, or at least as complement, to the typically offered political explanations.

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Clustering in crude oil prices and the target pricing zone hypothesis

Cited by 29 publications

References 50 publications

Price Clustering in Bank Stocks During the Global Financial Crisis

Price Clustering in Bank Stocks During the Global Financial Crisis

Some explicit results on one kind of sticky diffusion

A Rationalization of Ups and Downs of Oil Prices by Sluggish Demand, Uncertainty, and Nonconcavity

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