Developing real option game models

By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a ‘‘game’’ between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been ‘‘solved’’ by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.

A robustness framework for monitoring real options under uncertainty

This paper presents a problem structuring methodology to assess real option decisions in the face of unpredictability. Based on principles of robustness analysis and scenario planning, we demonstrate how decision-aiding can facilitate participation in projects setting and achieve effective decision making through the use of real options reasoning. We argue that robustness heuristics developed in earlier studies can be practical proxies for real options performance, hence indicators of efficient flexible planning. The developed framework also highlights how to integrate real options solutions in firms’ strategic plans and operating actions. The use of the methodology in a location decision application is provided for illustration.

Exchange rate economic exposure and hedging:the significance of currency options

This thesis focuses on the theoretical examination of the exchange rate economic (operating) exposure within the context of the theory of the firm, and proposes some hedging solutions using currency options. The examination of economic exposure is based on such parameters as firms' objectives, industry structure and production cost efficiency. In particular, it examines an hypothetical exporting firm with costs in domestic currency, which faces competition from foreign firms in overseas markets and has a market share expansion objective. Within this framework, the hypothesis is established that economic exposure, portrayed in a diagram connecting export prices and real exchange rates, is asymmetric (i.e. the negative effects depreciation are higher than the positive effects of a currency depreciation). In this case, export business can be seen as a real option, given by exporting firms to overseas customer. Different scenarios about the asymmetry hypothesis can be derived for different assumptions about the determinants of economic exposure. Having established the asymmetry hypothesis, the hedging against this exposure is analysed. The hypothesis is established, that a currency call option should be used in hedging against asymmetric economic exposure. Further, some advanced currency options stategies are discussed, and their use in hedging several scenarios of exposure is indicated, establishing the hypothesis that, the optimal options strategy is a function of the determinants of exposure. Some extensions on the theoretical analysis are examined. These include the hedging of multicurrency exposure using options, and the exposure of a purely domestic firm facing import competition. The empirical work addresses two issues: the empirical validity of the asymmetry hypothesis and the examination of the hedging effectiveness of currency options.

Real options and multinational investments : an empirical study of risk, performance and organisational awareness

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT This thesis is a cross-disciplinary study of the empirical impact of real options theory in the fields of decision sciences and performance management. Borrowing from the economics, strategy and operations research literature, the research examines the risk and performance implications of real options in firms’ strategic investments and multinational operations. An emphasis is placed on the flexibility potential and competitive advantage of multinational corporations to explore the extent to which real options analysis can be classified as best practice in management research. Using a combination of qualitative and quantitative techniques the evidence suggests that, if real options are explored and exploited appropriately, real options management can result in superior performance for multinational companies. The qualitative findings give an overview of the practical advantages and disadvantages of real options and the statistical results reveal that firms which have developed a high awareness of their real options are, as predicted by the theory, able to reduce their downside risk and increase profits through flexibility, organisational slack and multinationality. Although real options awareness does not systematically guarantee higher returns from operations, supplementary findings indicate that firms with evidence of significant investments in the acquisition of real options knowledge tend to outperform competitors which are unaware of their real options. There are three contributions of this research. First, it extends the real options and capacity planning literature to path-dependent contingent-claims analysis to underline the benefits of average type options in capacity allocation. Second, it is thought to be the first to explicitly examine the performance effects of real options on a sample of firms which have developed partial capabilities in real options analysis suggesting that real options diffusion can be key to value creation. Third, it builds a new decision-aiding framework to facilitate the use of real options in projects appraisal and strategic planning.

Real options in management and organizational strategy:a review of decision-making and performance implications

This paper contributes to the debate on the role of real options theory in business strategy and organizational decision-making. It analyses and critiques the decision-making and performance implications of real options within the management theories of the (multinational) firm, reviews and categorizes the organizational, strategic and operational facets of real options management in large business settings. It also presents the views of scholars and practitioners regarding the incorporation and validity of real options in strategy, international management and business processes. The focus is particularly on the decision-making and performance attributes of the real options logic concerning strategic investments, governance modes and multinational operations management. These attributes are examined from both strategic and operating perspectives of decision-making in organizations, also with an overview of the empirical evidence on real options decision-making and performance.

Real options and demographic decisions:empirical evidence from East and West Germany

Iyer and Velu (2006) have convincingly argued that contemporary analyses of fertility behaviour fail to explain why a woman (or a couple) will choose to postpone childbirth, and in particular to consider the role of uncertainty in this regard. They have addressed this lacuna in the literature by using a real options approach to model fertility decisions by relating uncertainty experienced by individuals to the likelihood of childbirth. However, they did not present empirical evidence. Since the theory implies the existence of two offsetting effects of uncertainty on fertility decisions, a positive insurance effect and a negative option value effect, it is not easy to reject the theory on the basis of empirical analysis, when one of these effects offsets the other. We construct such a test for East (and also West) Germany during that country's reunification, which takes advantage of the fact that because of the country's strong welfare system, the insurance effect should be dominated by the option value effect, thereby suggesting that the net relationship should be negative. The results provide rather strong support for the real options link, especially for Eastern Germany.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Neural Network Modelling with Input Uncertainty: Theory and Application

It is generally assumed when using Bayesian inference methods for neural networks that the input data contains no noise. For real-world (errors in variable) problems this is clearly an unsafe assumption. This paper presents a Bayesian neural network framework which accounts for input noise provided that a model of the noise process exists. In the limit where the noise process is small and symmetric it is shown, using the Laplace approximation, that this method adds an extra term to the usual Bayesian error bar which depends on the variance of the input noise process. Further, by treating the true (noiseless) input as a hidden variable, and sampling this jointly with the network’s weights, using a Markov chain Monte Carlo method, it is demonstrated that it is possible to infer the regression over the noiseless input. This leads to the possibility of training an accurate model of a system using less accurate, or more uncertain, data. This is demonstrated on both the, synthetic, noisy sine wave problem and a real problem of inferring the forward model for a satellite radar backscatter system used to predict sea surface wind vectors.

Employee negative emotions in supervisory relationships : an attachment theory perspective

Relationships with supervisors are a major source of negative emotions at work, but little is known about why this is so. The aim of the research was to use attachment theory (Bowlby, 1969, 1973; 1980) as a framework for investigating the nature and causes of employee negative emotional experiences, in the context of their supervisory relationships. The research was conducted in three stages. In Stage 1 two studies were conducted to develop a measure of employee perceptions of supervisor caregiving (SCS). Results indicated that the 20-item scale had good reliability and validity. Stage 2 required participants (N=183) to complete a questionnaire that was designed to examine the roles of supervisor caregiving and working models (specific and global) in determining cognitive and emotional responses to hypothetical supervisor behaviours. The results provided partial support for an Independent Effects Model. Supervisor caregiving predicted specific anxiety and avoidance. In tum, both dimensions of attachment predicted negative emotions, but this relationship was mediated by event interpretation only in the case of avoidance. Global models made a smaller but significant contribution to negative emotions overall. There was no support for an interaction effect between specific and global models in determining event interpretation. In stage 3 a sub-sample of questionnaire respondents (N=24) were interviewed about 'real-life' caregiving and negative emotional experiences in their supervisory relationships. Secure individuals experienced supervisors as consistently warm, available, and responsive. They reported few negative events or emotions. Individuals with insecure specific working models experienced rejecting or inconsistent supervisor caregiving. They were sensitised to trust and closeness issues in their relationships, and reported negative events and emotions underpinned by these themes. Overall, results broadly supported attachment theory predictions. It is concluded that an attachment theory perspective provides new insight into the nature and causes of employee negative emotions in supervisory relationships.

Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

Pragmatic development of service based real-time change data capture

This thesis makes a contribution to the Change Data Capture (CDC) field by providing an empirical evaluation on the performance of CDC architectures in the context of realtime data warehousing. CDC is a mechanism for providing data warehouse architectures with fresh data from Online Transaction Processing (OLTP) databases. There are two types of CDC architectures, pull architectures and push architectures. There is exiguous data on the performance of CDC architectures in a real-time environment. Performance data is required to determine the real-time viability of the two architectures. We propose that push CDC architectures are optimal for real-time CDC. However, push CDC architectures are seldom implemented because they are highly intrusive towards existing systems and arduous to maintain. As part of our contribution, we pragmatically develop a service based push CDC solution, which addresses the issues of intrusiveness and maintainability. Our solution uses Data Access Services (DAS) to decouple CDC logic from the applications. A requirement for the DAS is to place minimal overhead on a transaction in an OLTP environment. We synthesize DAS literature and pragmatically develop DAS that eciently execute transactions in an OLTP environment. Essentially we develop effeicient RESTful DAS, which expose Transactions As A Resource (TAAR). We evaluate the TAAR solution and three pull CDC mechanisms in a real-time environment, using the industry recognised TPC-C benchmark. The optimal CDC mechanism in a real-time environment, will capture change data with minimal latency and will have a negligible affect on the database's transactional throughput. Capture latency is the time it takes a CDC mechanism to capture a data change that has been applied to an OLTP database. A standard definition for capture latency and how to measure it does not exist in the field. We create this definition and extend the TPC-C benchmark to make the capture latency measurement. The results from our evaluation show that pull CDC is capable of real-time CDC at low levels of user concurrency. However, as the level of user concurrency scales upwards, pull CDC has a significant impact on the database's transaction rate, which affirms the theory that pull CDC architectures are not viable in a real-time architecture. TAAR CDC on the other hand is capable of real-time CDC, and places a minimal overhead on the transaction rate, although this performance is at the expense of CPU resources.

Developing the practice of educational psychologists through theory and research

Recent research in literacy acquisition has generated detailed programs for teaching phonological awareness. The current paper will address three issues that follow from this research. Firstly, much of the past research has been conducted under conditions that are divorced from the classroom. As a result, it is not known whether the suggested teaching strategies will lead to an increase in children’s attainments when integrated into a broad reading curriculum implemented by teachers in mainstream classrooms. Secondly, these phonological interventions have been designed either to prevent the occurrence of reading difficulties or to meet the needs of failing readers. Therefore, it is not known whether the same methods would advantage all children. Thirdly, teaching children to read takes a minimum of two to three academic years. We herefore need to develop a reading curriculum that can provide the progression and differentiation to meet a wide range of needs over several academic years. We report two studies that have addressed these issues through monitoring the impact of a reading curriculum, implemented by teachers, which integrated children’s acquisition of phonological skills with broader aspects of teaching reading over three academic years. The attainments of children at all levels of ability in the experimental group were raised relative to controls, and importantly, these gains were maintained after the intervention was withdrawn. These results demonstrate that phonological awareness training can be successfully integrated into real classroom contexts and that the same methods raised the attainments of normally developing children, as well as those at risk of reading failure.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.