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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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High order compact scheme and its applications in computational financeLee, Tsz Ho January 2010 (has links)
University of Macau / Faculty of Science and Technology / Department of Mathematics
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Analysing tacit collusion in oligopolistic electricity markets using a co-evolutionary approachThai, Doan Hoang Cau, Australian Graduate School of Management, Australian School of Business, UNSW January 2005 (has links)
Wholesale electricity markets now operate in many countries around the world. These markets determine a spot price for electricity as the clearing price when generators bid in energy at various prices. As the trading in a wholesale electricity market can be seen as a dynamic repeated game, it would be expected that profit maximising generators learn to engage in tacit collusion to profitably increase spot market prices. This thesis investigates this tacit collusion of generators in oligopolistic electricity markets. We do not follow the approach of previous work in game theory that presupposes firms' collusive strategies to enforce collusion in an oligopoly. Instead, we develop a co-evolutionary approach (extending previous work in this area) using a genetic algorithm (GA) to co-evolve strategies for all generators in some stylised models of an electricity market. The bidding strategy of each generator is modelled as a set of bidding actions, one for each possible discrete state of the state space observed by the generator. The market trading interactions are simulated to determine the fitness of a particular strategy. The tacitly collusive outcomes and strategies emerging from computational experiments are thus obtained from the learning or evolutionary process instead of from any pre-specification. Analysing many of those emergent collusive outcomes and strategies. we are able to specify the mechanism of tacit collusion and investigate how the market environment can affect it. We find that the learned collusive strategies are similar to the forgiving trigger strategies of classical supergame theory (Green and Porter, 1984). Also using computational experiments, we can determine which characteristics of the market environment encourage or hinder tacit collusion. The findings from this thesis provide insights on tacit collusion in an oligopoly and policy implications from a learning perspective. With modelling flexibility, our co-evolutionary approach can be extended to study strategic behaviour in an oligopoly considering many other market characteristics.
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Union monétaire européenne et théorie des zones monétaires optimalesBeine, Michel January 1996 (has links)
Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished
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Metaheuristic approaches to realistic portfolio optimisationBusetti, Franco Raoul 06 1900 (has links)
In this thesis we investigate the application of two heuristic methods, genetic
algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The
model is based on the classical mean-variance approach, but enhanced with floor and
ceiling constraints, cardinality constraints and nonlinear transaction costs which
include a substantial illiquidity premium, and is then applied to a large I 00-stock
portfolio.
It is shown that genetic algorithms can optimise such portfolios effectively and within
reasonable times, without extensive tailoring or fine-tuning of the algorithm. This
approach is also flexible in not relying on any assumed or restrictive properties of the
model and can easily cope with extensive modifications such as the addition of
complex new constraints, discontinuous variables and changes in the objective
function.
The results indicate that that both floor and ceiling constraints have a substantial
negative impact on portfolio performance and their necessity should be examined
critically relative to their associated administration and monitoring costs.
Another insight is that nonlinear transaction costs which are comparable in magnitude
to forecast returns will tend to diversify portfolios; the effect of these costs on
portfolio risk is, however, ambiguous, depending on the degree of diversification
required for cost reduction. Generally, the number of assets in a portfolio invariably
increases as a result of constraints, costs and their combination.
The implementation of cardinality constraints is essential for finding the bestperforming
portfolio. The ability of the heuristic method to deal with cardinality
constraints is one of its most powerful features. / Decision Sciences / M. Sc. (Operations Research)
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