In this thesis, we study brand choice problem via the following three perspectives: a company's market share management, introduction of customers with different perspectives, and an analysis of an application domain which is illustrative of these issues. Our contributions following these perspectives include: (1) development of a stochastic differential-jump game (SDJG) model for brand competition in a specific situation wherein market share is modeled by a jump-diffusion process, (2) a robust hierarchical logit/probit model for market heterogeneity, and (3) applications of logit/probit model to the dynamic pricing problem occurring in production-inventory systems with jump events. Our research explores the use of quantitative method of operations research to control the dynamics of market share and provides a precise estimation method to integrate more detail information in discrete brand choice models.

This master’s thesis project consists of two parts. Part I of the project compares modeling of historical resource production and forecasting of future production trends using the logit/probit transform advocated by Rutledge (2011) with conventional Hubbert curve fitting, using global coal production as a case study. The conventional Hubbert/Gaussian method fits a curve to historical production data whereas a logit/probit transform uses a linear fit to a subset of transformed production data. Within the errors and limitations inherent in this type of statistical modeling, these methods provide comparable results. That is, despite that apparent goodness-of-fit achievable using the Logit/Probit methodology, neither approach provides a significant advantage over the other in either explaining the observed data or in making future projections. For mature production regions, those that have already substantially passed peak production, results obtained by either method are closely comparable and reasonable, and estimates of ultimately recoverable resources obtained by either method are consistent with geologically estimated reserves. In contrast, for immature regions, estimates of ultimately recoverable resources generated by either of these alternative methods are unstable and thus, need to be used with caution. Although the logit/probit transform generates high quality-of-fit correspondence with historical production data, this approach provides no new information compared to conventional Gaussian or Hubbert-type models and may have the effect of masking the noise and/or instability in the data and the derived fits. In particular, production forecasts for immature or marginally mature production systems based on either method need to be regarded with considerable caution. Part II of the project investigates the utility of a novel alternative method for multicyclic Hubbert modeling tentatively termed “cycle-jumping” wherein overlap of multiple cycles is limited. The model is designed in a way that each cycle is described by the same three parameters as conventional multicyclic Hubbert model and every two cycles are connected with a transition width. Transition width indicates the shift from one cycle to the next and is described as weighted coaddition of neighboring two cycles. It is determined by three parameters: transition year, transition width, and γ parameter for weighting. The cycle-jumping method provides superior model compared to the conventional multicyclic Hubbert model and reflects historical production behavior more reasonably and practically, by better modeling of the effects of technological transitions and socioeconomic factors that affect historical resource production behavior by explicitly considering the form of the transitions between production cycles.

Credit Rating Agencies (CRAs) play a key role in financial markets by helping to reduce informative asymmetry between lenders and investors, on one side, and issuers on the other side, with regard to the creditworthiness of banks or countries. This crucial role has expanded alongside financial globalisation and received an additional boost from Basel II which integrates the ratings of CRAs into the rules for setting weights for credit risk. Ratings adjustment tends to be sticky, lagging behind markets, and often overreact when they do change. This overreaction may have aggravated the recent financial crises, contributing to financial instability and cross-country contagion. Criticism has been especially directed towards the high degree of concentration of the ratings industry. Promotion of competition may require policy action at the international level to encourage the establishment of new agencies and to discover alternative rules or regulatory requirements in order to achieve promising results. The recent growth of Middle Eastern and North African countries (MENA) and their commercial banking system has increased the need of paying widespread attention to this region of the world. This thesis crucially identifies, and estimates, the robust determinants of credit ratings for MENA countries and their commercial banks, incorporating a set of bank level accounting and financial risk factors, as well as country-specific characteristics, including indicators for regulatory, supervision, legal and economic environments. The research contributes, firstly, to the theoretical literature on credit ratings industry by reviewing extant methodologies specifically as they apply to banks and sovereign countries. Secondly, it conducts a systematic, cross-country empirical investigation using panel data econometric methodology for the purpose of estimating MENA countries sovereign and bank credit rating models. Thirdly, it provides tangible and statistically significant evidence on the different factors that determines the estimation of credit ratings and influencing bank's risk. The extant literature reviewed serves as a basis to achieve and develop the research aim, objectives and hypotheses of the thesis. The research then constructs an appropriate panel dataset from different sources, containing bank-level and country-level information for a sample of 108 commercial banks covering 13 MENA countries over the period 2000 - 2012. The methodological framework for estimating credit rating models (linear regression, logit and probit) is also reviewed and the procedures for panel data estimation are implemented using the econometric package STATA (version 13). All relevant data are drawn from public sources including Reuters, Bankscope, IMF and the World Bank. Using the random effects ordered probit and logit methodologies to estimate both sovereign (country) and bank level credit ratings models for the MENA countries, the evidence shows that real GDP growth, capital requirements, restrictions on banking activities and control of corruption all contribute negatively to the sovereign ratings. Furthermore, internal management and organisational requirements is considered as an additional regulatory factor not studied in previous research. The statistically significant and inverse relationship of the latter is considered an important and interesting outcome of MENA countries’ sovereign ratings. On the other hand, GDP per capita, investment (as a percentage of GDP), political stability, government effectiveness and the rule of law all reveal significant and positive impact on the sovereign credit ratings. In general, this research finds that improved macroeconomic conditions are correlated with higher ratings, while greater reserve regulations are correlated with lower ratings. The study also does find the significance of governance and regulatory variables plays a key role into the final credit rating. With regard to the impact on banks’ ratings, the results show that higher return on average assets and equity, larger bank size, more restrictions on bank activities, as well as higher official disciplinary power and higher standards of internal management, will yield higher credit ratings. Apart from having direct and positive impact on banks credit ratings, these variables are important for examining the risk-sharing incentives in MENA countries’ banks. In contrast, the estimation results indicate that net interest margin, net loans to deposits, liquid assets to deposits, capital requirements, deposit insurance scheme, liquidity requirements, unemployment rate and government effectiveness have an inverse and negative impact on banks ratings. In general, this study also finds various financial, macroeconomic, and regulatory effects on banks’ credit ratings. To a much lesser extent than government ratings, various macroeconomic variables also helped predict banks’ ratings, including real GDP growth and the unemployment rate. The thesis concludes by arguing that the combined use of financial and non-financial factors for estimating credit ratings models supports the relevant hypotheses examined and adds value to all stakeholders in improving and obtaining a better quality of credit ratings. This study also demonstrates that a diversity of bank-level and country-level factors influence the MENA sovereign and bank ratings differently, implying that policy makers, regulators alongside rating agencies should distinguish the different environmental factors between nations before any judgment and issuance can be model of the ratings. To conclude, there is no study which exclusively investigates credit rating models for the MENA region exploiting the richness of the data and methodology employed, and the current research aims to fill this gap.