Simulation and hedging oil price with geometric Brownian Motion and single-step binomial price model

This paper[1] uses the Geometric Brownian Motion (GBM) to model the behaviour of crude oil price in a Monte Carlo simulation framework. The performance of the GBM method is compared with the naïve strategy using different forecast evaluation techniques. The results from the forecasting accuracy statistics suggest that the GBM outperforms the naïve model and can act as a proxy for modelling movement of oil prices. We also test the empirical viability of using a call option contract to hedge oil price declines. The results from the simulations reveal that the single-step binomial price model can be effective in hedging oil price volatility. The findings from this paper will be of interest to the government of Nigeria that views the price of oil as one of the key variables in the national budget. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil price volatility; Geometric Brownian Motion; Monte Carlo Simulation; Single-Step Binomial Price Model [1] Acknowledgement: We wish to thank the two anonymous reviewers for their insightful comments and kind considerations. Memos to: Azeez Abiola Oyedele, School of Business and Enterprise, University of the West of Scotland, Paisley Campus, Paisley PA1 2BE, Scotland, Email: [email protected]

Forecasting OPEC oil price: a comparison of parametric stochastic models

Most academic papers on oil price forecasting have frequently focused on the use of WTI and European Brent oil price series with little focus on other equally important international oil price benchmarks such as the OPEC Reference Basket (ORB). The ORB is a weighted average of 11-member countries crude streams weighted according to production and exports to the main markets. This paper compares the forecasting accuracy of four stochastic processes and four univariate random walk models using daily data of OPEC Reference Basket series. The study finds that the random walk univariate model outperforms the other stochastic processes. An element of uncertainty was introduced into the point estimates by deriving probability distribution that describes the possible price paths on a given day and their likelihood of occurrence. This will help decision makers, traders and analysts to have a better understanding of the possible daily prices that could occur. JEL Classification Numbers: E64; C22; Q30 Keywords: Oil Price Forecasting, Probability Distributions, and Forecast Evaluation Statistics, Brownian Motion with Mean Reversion process, GARCH Model

Critical factors for insolvency prediction: Towards a theoretical model for the construction industry

© 2016 Informa UK Limited, trading as Taylor & Francis Group. Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM

Big Data innovation and implementation in projects teams: towards a SEM approach to conflict prevention

Purpose: Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams. Design/methodology/ Approach: This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship, and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology. Findings: Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by Power-Conflict prevention, prevention of relationship disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflict interact on the one hand, while Task and relationship conflict prevention on the other hand, suggesting the prevention of one of the conflicts could minimise the outbreak of the other. Research Limitations: The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations. Practical Implications: The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. We urge organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team. Social Implications: The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams. Originality/Value: The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power, process, relationship & task conflicts; to encourage Big Data implementation