eprintid: 247
rev_number: 15
eprint_status: archive
userid: 7
dir: disk0/00/00/02/47
datestamp: 2009-10-20 10:24:05
lastmod: 2015-05-29 19:51:35
status_changed: 2009-10-20 10:24:05
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Jones, Marvin
creators_name: Barton, David
creators_name: Hall, Cameron
creators_name: Coskun, Erhan
creators_name: Lacey, Andrew
creators_name: Lorenz, Maike
creators_name: Maringer, Johannes
creators_name: Please, Colin
creators_name: Richardson, Giles
corp_creators: Wajid Rasheed
title: Oil price cycle and sensitivity model
ispublished: pub
subjects: utilities
subjects: finance
studygroups: esgi68
companyname: SmartReamer Ltd
full_text_status: public
abstract: EPRasheed wishes to be able to model and predict oil prices out to a time-horizon of 2050, taking into account a number of known factors. These include the finite supply of oil, growing and shifting demand, the viability of alternative energy sources (at different pricing levels) and the interactions of oil producers and oil consumers, as they respond to current pricing levels.

The study group concluded that while ‘prediction’ of price in any meaningful sense was not viable, a model for scenario analysis could be realised. The model did not incorporate all of the factors of interest, but did model important time lags in the response of market players’ future behaviour to current oil prices. Consideration of the optimisation of supply through new capacity in the telecoms industry led to a generalisation of the standard Cournot-Nash equilibrium. This indicates how an output-constrained competitive market might operate. It enables identification of different pricing regimes determined by the level of competition and the resource limitations of particular supplier firms. Two models were developed sufficiently to enable simulation of various conditions and events. The first modelled oil price as a mean reverting Brownian motion process. Strategies and scenarios were included in the model and realistic simulations were produced. The second approach used stability analysis of an appropriate time-delayed differential equation. This enabled the identification of unstable conditions and the realisation of price oscillations which depended on the demand scenarios.
date: 2009
related_url_url: http://www.smithinst.ac.uk/Projects/ESGI68/ESGI68-EPRasheed/Report
citation:   Jones, Marvin and Barton, David and Hall, Cameron and Coskun, Erhan and Lacey, Andrew and Lorenz, Maike and Maringer, Johannes and Please, Colin and Richardson, Giles  (2009) Oil price cycle and sensitivity model.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/247/1/oil-prices.pdf