153 research outputs found
ESTABLISHING A NEW LEGAL MODEL FOR THE GOVERNANCE OF CONTRACTUAL JOINT VENTURES THROUGH THE APPLICATION OF RATIONAL CHOICE THEORY
The purpose of this thesis is to rationalise the law on contractual joint ventures, in the sense of rendering it consistent with its own fundamental tenets and declared objectives. The declared objective of contract law is to give effect to the intentions of reasonable persons, whom the law presumes to be self-interested by default. To this end, this thesis argues for a new legal model to govern the contractual (project-specific) joint venture, which centres on the joint venture contract but is fundamentally augmented through the application of default, mutually binding, fiduciary duties. By applying David Gauthierâs take on rational choice theory in the context of cooperation, the thesis demonstrates that submitting to default duties of this type is the long-term utility maximising strategy for self-interested commercial parties who have chosen to cooperate. For this reason, it argues that English law should imply fiduciary duties into the joint venture contract by default on the basis that this is what the co-venturers would have intended had they properly reflected on what their long-term self-interest requires
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Applications of robust optimal control to decision making in the presence of uncertainty
This thesis is concerned with robustness of decision making in financial economics. Feedback control models developed in engineering are applied to three separate though linked problems in order to examine the role and impact of robustness in the creation and application of decision rules. Three problems are examined using robust optimal control techniques to evaluate the impact of robustness and stability in financial economic models. The first problem examines the use of linear models of robust optimal control in the pricing of castastrophe based derivatives and finds its relative performance to be superior to the popular jump diffusion and stochastic volatility models in the pricing of these emerging instruments. The novelty of the approach arises from the examination of the impact of robustness and stability of the pricing solution. The second problem involves robustness and stability of hedging. An alternative method of creating hedging rules is developed. The method is based on robust control Lyapunov functions that are simple, robust and stable in operation, yet in practice are not so conservative that they eliminate all trading gains. The third problem involves the development of robust control policies for managing risk, using non-linear robust optimal control techniques to provide clear evidence of superior performance of robust models when compared with existing VAR and EVT approaches to risk management. The novelty in the approach arises from the development of a simple and powerful risk management metric
Central banks and financial crises
The paper draws lessons from the experience of the past year for the conduct of central banks in the pursuit of macroeconomic and financial stability. Macroeconomic stability is defined as either price stability or as price stability and sustainable output or employment growth. Financial stability refers to (1) the absence of asset price bubbles, (2) the prevention or mitigation of systemically significant funding illiquidity and market illiquidity and (3) the prevention of insolvency of systemically important financial institutions. The performance of the Fed, the ECB and the Bank of England is evaluated in terms of these criteria. The Fed is judged to have done worst both as regards macroeconomic stability and as regards one of the two time dimensions of financial stability: minimizing the likelihood and severity of future financial crises. As regards âputting out firesâ (dealing with the immediate crisis), the Bank of England gets the wooden spoon for its early failure to perform the lender of last resort and market maker of last resort roles
Spatial competition of learning agents in agricultural procurement markets
Spatially dispersed farmers supply raw milk as the primary input to a small number of large dairy-processing firms. The spatial competition of processing firms has short- to long-term repercussions on farm and processor structure, as it determines the regional demand for raw milk and the resulting raw milk price. A number of recent analytical and empirical contributions in the literature analyse the spatial price competition of processing firms in milk markets. Agent-based models (ABMs) serve by now as computational laboratories in many social science and interdisciplinary fields and are recently also introduced as bottom-up approaches to help understand market outcomes emerging from autonomously deciding and interacting agents. Despite ABMs' strengths, the inclusion of interactive learning by intelligent agents is not sufficiently matured. Although the literature of multi-agent systems (MASs) and multi-agent economic simulation are related fields of research they have progressed along separate paths. This thesis takes us through some basic steps involved in developing a theoretical basis for designing multi-agent learning in spatial economic ABMs. Each of the three main chapters of the thesis investigates a core issue for designing interactive learning systems with the overarching aim of better understanding the emergence of pricing behaviour in real, spatial agricultural markets. An important problem in the competitive spatial economics literature is the lack of a rigorous theoretical explanation for observed collusive behavior in oligopsonistic markets. The first main chapter theoretically derives how the incorporation of foresight in agents' pricing policy in spatial markets might move the system towards cooperative Nash equilibria. It is shown that a basic level of foresight invites competing firms to cease limitless price wars. Introducing the concept of an outside option into the agents' decisions within a dynamic pricing game reveals viihow decreasing returns for increasing strategic thinking correlates with the relevance of transportation costs. In the second main chapter, we introduce a new learning algorithm for rational agents using H-PHC (hierarchical policy hill climbing) in spatial markets. While MASs algorithms are typically just applicable to small problems, we show experimentally how a community of multiple rational agents is able to overcome the coordination problem in a variety of spatial (and non-spatial) market games of rich decision spaces with modest computational effort. The theoretical explanation of emerging price equilibria in spatial markets is much disputed in the literature. The majority of papers attribute the pricing behavior of processing firms (mill price and freight absorption) merely to the spatial structure of markets. Based on a computational approach with interactive learning agents in two-dimensional space, the third main chapter suggests that associating the extent of freight absorption just with the factor space can be ambiguous. In addition, the pricing behavior of agricultural processors â namely the ability to coordinate and achieve mutually beneficial outcomes - also depends on their ability to learn from each other
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Approximate dynamic programming for large scale systems
Sequential decision making under uncertainty is at the heart of a wide variety of practical problems. These problems can be cast as dynamic programs and the optimal value function can be computed by solving Bellman's equation. However, this approach is limited in its applicability. As the number of state variables increases, the state space size grows exponentially, a phenomenon known as the curse of dimensionality, rendering the standard dynamic programming approach impractical. An effective way of addressing curse of dimensionality is through parameterized value function approximation. Such an approximation is determined by relatively small number of parameters and serves as an estimate of the optimal value function. But in order for this approach to be effective, we need Approximate Dynamic Programming (ADP) algorithms that can deliver `good' approximation to the optimal value function and such an approximation can then be used to derive policies for effective decision-making. From a practical standpoint, in order to assess the effectiveness of such an approximation, there is also a need for methods that give a sense for the suboptimality of a policy. This thesis is an attempt to address both these issues. First, we introduce a new ADP algorithm based on linear programming, to compute value function approximations. LP approaches to approximate DP have typically relied on a natural `projection' of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program -- the `smoothed approximate linear program' -- is distinct from such approaches and relaxes the restriction to lower bounding approximations in an appropriate fashion while remaining computationally tractable. The resulting program enjoys strong approximation guarantees and is shown to perform well in numerical experiments with the game of Tetris and queueing network control problem. Next, we consider optimal stopping problems with applications to pricing of high-dimensional American options. We introduce the pathwise optimization (PO) method: a new convex optimization procedure to produce upper and lower bounds on the optimal value (the `price') of high-dimensional optimal stopping problems. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds and lower bounds (via suboptimal exercise policies) of a quality comparable with state-of-the-art approaches. Further, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Finally, we consider a broad class of MDPs and introduce a new tractable method for computing bounds by consider information relaxation and introducing penalty. The method delivers tight bounds by identifying the best penalty function among a parameterized class of penalty functions. We implement our method on a high-dimensional financial application, namely, optimal execution and demonstrate the practical value of the method vis-a-vis competing methods available in the literature. In addition, we provide theory to show that bounds generated by our method are provably tighter than some of the other available approaches
Mathematical control theory and Finance
Control theory provides a large set of theoretical and computational tools with applications in a wide range of fields, running from âpureâ branches of mathematics, like geometry, to more applied areas where the objective is to find solutions to âreal lifeâ problems, as is the case in robotics, control of industrial processes or finance. The âhigh techâ character of modern business has increased the need for advanced methods. These rely heavily on mathematical techniques and seem indispensable for competitiveness of modern enterprises. It became essential for the financial analyst to possess a high level of mathematical skills. Conversely, the complex challenges posed by the problems and models relevant to finance have, for a long time, been an important source of new research topics for mathematicians. The use of techniques from stochastic optimal control constitutes a well established and important branch of mathematical finance. Up to now, other branches of control theory have found comparatively less application in financial problems. To some extent, deterministic and stochastic control theories developed as different branches of mathematics. However, there are many points of contact between them and in recent years the exchange of ideas between these fields has intensified. Some concepts from stochastic calculus (e.g., rough paths) have drawn the attention of the deterministic control theory community. Also, some ideas and tools usual in deterministic control (e.g., geometric, algebraic or functional-analytic methods) can be successfully applied to stochastic control. We strongly believe in the possibility of a fruitful collaboration between specialists of deterministic and stochastic control theory and specialists in finance, both from academic and business backgrounds. It is this kind of collaboration that the organizers of the Workshop on Mathematical Control Theory and Finance wished to foster. This volume collects a set of original papers based on plenary lectures and selected contributed talks presented at the Workshop. They cover a wide range of current research topics on the mathematics of control systems and applications to finance. They should appeal to all those who are interested in research at the junction of these three important fields as well as those who seek special topics within this scope.info:eu-repo/semantics/publishedVersio
From metaheuristics to learnheuristics: Applications to logistics, finance, and computing
Un gran nombre de processos de presa de decisions en sectors estratĂšgics com el transport i la producciĂł representen problemes NP-difĂcils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurĂstiques sĂłn mĂštodes populars per a resoldre problemes d'optimitzaciĂł difĂcils en temps de cĂ lcul raonables. No obstant aixĂČ, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions sĂłn deterministes i conegudes. Aquests constitueixen supĂČsits forts que obliguen a treballar amb problemes simplificats. Com a conseqĂŒĂšncia, les solucions poden conduir a resultats pobres. Les simheurĂstiques integren la simulaciĂł a les metaheurĂstiques per resoldre problemes estocĂ stics d'una manera natural. AnĂ logament, les learnheurĂstiques combinen l'estadĂstica amb les metaheurĂstiques per fer front a problemes en entorns dinĂ mics, en quĂš els inputs poden dependre de l'estructura de la soluciĂł. En aquest context, les principals contribucions d'aquesta tesi sĂłn: el disseny de les learnheurĂstiques, una classificaciĂł dels treballs que combinen l'estadĂstica / l'aprenentatge automĂ tic i les metaheurĂstiques, i diverses aplicacions en transport, producciĂł, finances i computaciĂł.Un gran nĂșmero de procesos de toma de decisiones en sectores estratĂ©gicos como el transporte y la producciĂłn representan problemas NP-difĂciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurĂsticas son mĂ©todos populares para resolver problemas difĂciles de optimizaciĂłn de manera rĂĄpida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurĂsticas integran simulaciĂłn en metaheurĂsticas para resolver problemas estocĂĄsticos de una manera natural. De manera similar, las learnheurĂsticas combinan aprendizaje estadĂstico y metaheurĂsticas para abordar problemas en entornos dinĂĄmicos, donde los inputs pueden depender de la estructura de la soluciĂłn. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurĂsticas, una clasificaciĂłn de trabajos que combinan estadĂstica / aprendizaje automĂĄtico y metaheurĂsticas, y varias aplicaciones en transporte, producciĂłn, finanzas y computaciĂłn.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing
Antecipação na tomada de decisĂŁo com mĂșltiplos critĂ©rios sob incerteza
Orientador: Fernando JosĂ© Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia ElĂ©trica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisĂ”es em processos de escolha, especialmente ao elicitar as importĂąncias relativas de mĂșltiplos critĂ©rios de decisĂŁo e de desempenhos de curto vs. longo prazo. Algumas decisĂ”es, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em açÔes precipitadas com consequĂȘncias imprevisĂveis. Quando uma solução deve ser selecionada sob vĂĄrios pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisĂłrias flexĂveis pode ser fundamental para contornar a falta de informação completa, mantendo opçÔes futuras em aberto. A engenharia antecipatĂłria pode entĂŁo ser considerada como a estratĂ©gia de conceber soluçÔes flexĂveis as quais permitem aos tomadores de decisĂŁo responder de forma robusta a cenĂĄrios imprevisĂveis. Essa estratĂ©gia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade Ă s mudanças futuras. Nesta tese, os papĂ©is da antecipação e da flexibilidade na automação de processos de tomada de decisĂŁo sequencial com mĂșltiplos critĂ©rios sob incerteza Ă© investigado. O dilema de atribuir importĂąncias relativas aos critĂ©rios de decisĂŁo e a recompensas imediatas sob informação incompleta Ă© entĂŁo tratado pela antecipação autĂŽnoma de decisĂ”es flexĂveis capazes de preservar ao mĂĄximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatĂłria on-line Ă© entĂŁo proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluçÔes de trade-off. Esse objetivo Ă© alcançado por meio da previsĂŁo de conjuntos de mĂĄximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurĂsticas multi-objetivo sĂŁo incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisĂ”es de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de açÔes fora da amostra de treino, quando comparada a uma estratĂ©gia mĂope. AlĂ©m disso, a tomada de decisĂ”es flexĂveis para o rebalanceamento de carteiras foi confirmada como uma estratĂ©gia significativamente melhor do que a de escolher aleatoriamente uma decisĂŁo de investimento a partir da fronteira estocĂĄstica eficiente evoluĂda, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opçÔes flexĂveis levou a composiçÔes de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElĂ©tric
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