Market economy | EPFL Graph Search

A market economy is an economic system in which the decisions regarding investment, production and distribution to the consumers are guided by the price signals created by the forces of supply and demand. The major characteristic of a market economy is the existence of factor markets that play a dominant role in the allocation of capital and the factors of production. Market economies range from minimally regulated free-market and laissez-faire systems where state activity is restricted to providing public goods and services and safeguarding private ownership, to interventionist forms where the government plays an active role in correcting market failures and promoting social welfare. State intervention can happen at the production, distribution, trade and consumption areas in the economy. The distribution of basic need services and goods like health care may be entirely regulated by an egalitarian public health care policy (while having the production provided by private enterpri

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development in mathematical finance. The market in shipping derivatives (FFA and FOSVA) has only been developed after 2000 and now exhibits impressive growth. Financial actors have entered the field, but it is still largely undiscovered by institutional investors. The first part of the work was to identify the characteristics of the market in shipping, i.e. the segmentation and the volatility. Because the shipping business is old-fashioned, even the leading actors on the world stage (ship owners and banks) are using macro-economic models to forecast the rates. If the macro-economic models are logical and make sense, they fail to predict. For example, the factor port congestion has been much cited during the last few years, but it is clearly very difficult to control and is simply an indicator of traffic. From our own experience it appears that most ship owners are in fact market driven and rather bad at anticipating trends. Due to their ability to capture large moves, we chose to consider Lévy processes for the underlying price process. Compared with the macro-economic approach, the main advantage is the uniform and systematic structure this imposed on the models. We get in each case a favorable result for our technology and a gain in forecasting accuracy of around 10% depending on the maturity. The global distribution is more effectively modelled and the tails of the distribution are particularly well represented. This model can be used to forecast the market but also to evaluate the risk, for example, by computing the VaR. An important limitation is the non-robustness in the estimation of the Lévy processes. The use of robust estimators reinforces the information obtained from the observed data. Because maximum likelihood estimation is not easy to compute with complex processes, we only consider some very general robust score functions to manage the technical problems. Two new class of robust estimators are suggested. These are based on the work of F. Hampel ([29]) and P. Huber ([30]) using influence functions. The main idea is to bound the maximum likelihood score function. By doing this a bias is created in the parameters estimation, which can be corrected by using a modification of the following type and as proposed by F. Hampel. The procedure for finding a robust estimating equation is thus decomposed into two consecutive steps : Subtract the bias correction and then Bound the score function. In the case of complex Lévy processes, the bias correction is difficult to compute and generally unknown. We have developed a pragmatic solution by inverting the Hampel's procedure. Bound the score function and then Correct for the bias. The price is a loss of the theoretical properties of our estimators, besides the procedure converges to maximum likelihood estimate. A second solution to for achieving robust estimation is presented. It considers the limiting case when the upper and lower bounds tend to zero and leads to B-robust estimators. Because of the complexity of the Lévy distributions, this leads to identification problems.

EPFL2008

Decoding urban development dynamics through actor-network methodological approach

Jean-Claude Bolay, Marija Cvetinovic

Due to growing social and physical transformations, contemporary cities reveal the profound necessity of proper scientific approaches that are adjusted to conditions of global complexity and dynamic patterns of development. Predominance of an overall market economy, sporadic deregulations of administrative powers and a lack of local investment or resources, dominate urban reality. Incongruous urban decision-making procedures result in contextually inappropriate and incoherent urban management. We will explore these operational elements in Savamala neighbourhood in Belgrade. The actor-network theory.(ANT) is applied to analyse the hyper dynamic circumstances of transition in Serbia. An unclear regulatory framework, powerful,financial means for investment and limited institutional influence of citizen participation, deploy unstable urban development modalities at the neighbourhood level. ANT offers an insight into how urban norms, projections and structures unfold and how associations and translations of urban elements develop. Plausible yet complex collisions in Savamala constitute a challenge for ANT in mapping urban development processes and visualizing actors and networks through diagrams. Based on the presented results, the illustrative perspective of ANT minimalizes both the importance and the influence of the permanence of urban structures across time and space. Instead, ANT deals with a city as a contingent, fragmentary and heterogeneous, yet persistent product of actors, their roles, associations, agencies and networks. Possible adaptations of ANT should respond to the needs of non-scientific actors and practitioners for an interpretive tool that addresses undercover processes and mechanisms or provides explanations, recommendations or operational diagnoses on how to absorb urban development dynamics.

2017

Multi-agent modelling of electricity markets

Abdelaziz Lahlou

This thesis describes the development of three conceptual models built to serve as decision support tools in liberalised electricity markets. The introduction of competition, higher uncertainty and decentralised planning requires new planning and analysis tools on the medium to long term to support decision making at the level of the industry as well as at the level of the market authorities. We have mainly developed models of market simulation and investment decision by investigating how multi-agent systems can contribute to the understanding of the decentralised nature of competitive decision making in a liberalised electricity market. The thesis focused particularly on bilateral transactions arguing that such transaction processes are specially relevant from the point of view of risk management when considering medium to long term decisions (for both portfolio management and investment decision making). We have used decision analysis under uncertainty and dynamic modeling approaches under a multi-agent framework by considering that such approaches are more adequate in a competitive environment due to the market actors' exposure to risks and need for adaptation in a changing environment. Monthly spot market prices are taken into account through scenarios that reproduce volatility by lognormal processes. Monte Carlo simulation is used to quantify the risks among which the spot prices are taken as the most significant source of uncertainty. The main objective of this thesis is to contribute to the problem of modeling investment decisions in new generation capacities using electricity market simulation from a planning point of view. This global objective is driven according to the: Modeling of the bilateral transaction processes between generation companies and demand companies at the level of wholesale market by taking into account the possible tradeoffs between spot and bilateral markets. The transaction processes models represent the market simulation modules by: Distinguishing the different forms the transaction processes can take (negotiation vs. auctions). Modeling of the market actors' portfolio management from a planning perspective by taking into account the market uncertainties (sales portfolio for generation companies and supply portfolios for demand companies). Modeling of the investment decisions in new electricity generation power plants. "Negotiation based portfolio management in a liberalised electricity market: a multiagent approach" The first model of the thesis formalizes the transactions of demand companies that would have to face a single generation company. The transaction process is here modeled as a bilateral negotiation algorithm between buyers and a seller. The negotiation is driven on two issues: prices and quantities of monthly contracts. It is simulated as a succession of offers and counteroffers during a limited period of time. Each market actor is searching for efficient transactions that satisfy its objectives, preferences and constraints. The reasoning process of the agents is based on the multi-attribute tradeoffs under uncertainty seeking each month the optimal configuration of portfolios composed of bilateral and spot contracts. The simulated output of the negotiation does not necessarily optimise the social welfare as both parties have opposing interests and interact under incomplete information. The efficiency of the algorithm is characterized by a ratio relatively to the Pareto optimal contracts. It was found that a supplier with nuanced risk aversion have high efficiency beyond the Pareto optimal points (between 100 and 120%). It indicates that the single generation company has a higher bargaining power than the individual demand companies independently of the preferences and negotiation behaviour of its counterparts. The negotiation efficiency of the different demand companies ranges from 80 to 100% and is related to their attitude toward risks. Risk averse demand companies accept higher risk premiums in order to avoid spot market risk exposure unlike other agents that may have more nuanced risk preferences. The bilateral contracts concluded covers the electricity demand partially or totally depending on the risk attitude. Demand companies with nuanced risk aversion negotiate contracts in order to manage some flexibility and take benefit from potential spot market opportunities. "Bilateral transactions in a competitive electricity market: multiple auctions using an agent based approach" The second transaction process models bilateral transactions between demand companies and several generation companies through multiple auctions. In this context, the generation companies must compete between each other to satisfy the call-for-bids (CfBs) launched simultaneously by each demand company, i.e. each buyer in the system opens an auction by expressing its needs and preferences by product (base and peak). After receiving the CfBs, the suppliers send offers responding to the multiple auctions opened by the demand companies. The offers timing is sequenced by a priority list that ranks the demand companies according to their value. The construction of the offers is modeled with a function of the evaluated utility of the customer based on its CfB. The demand companies, at the closure of their individual auction, compare and select the offers with maximum value by including the evaluation of the possible tradeoffs between bilateral and spot markets. The dynamic of competition requires the suppliers to adapt their monthly pricing strategies to satisfy their profit and market share objectives. The adaptive behaviour of the generation companies is here modeled using a directed learning approach. Learning is performed on the basis of two variables (risk premiums and market shares) which previous month instances permit causal inferences on eight rules of pricing parameters adaptation. The results of a case study show that the agents in the system build flexible portfolios (partial or full supply contracts, supply portfolio with several producers, spot contracts ...). The agents learning process tend to satisfy their objectives of profit and market shares. The bilateral market transaction volume represents between 70 and 90% of the total transaction volume (10 to 30% of spot contracts) as a result of the tradeoffs between the two markets. "Electricity generation capacity expansion under competition: A multi-agent dynamic programming model" The last model proposed in this thesis intends to model the decentralized investment decision making in a competitive electricity market. The capacity expansion model proposed aims at evaluating the medium to long term investment decisions according to criteria of risks and profits maximisation. We use the agent-based multiple-auction module to simulate he electricity market. The investment decisions of each supplier agent in the system are assessed according to a dynamic programming algorithm that explores different potential expansion plans in predefined periods. We consider that capital rationing, preservation of capital under risk and market share targeted ranges are three constraints that bound the expansion plans. Two major sources of uncertainties are considered: market price risk and strategic risk. Strategic risks are evaluated through a customer and competitors focus by putting into perspective the investment decision relatively to the competitors' potential expansion plans. Hence, the evaluation of an expansion plan is built as the convolution of the realizable incomes through the potential configurations of competitors strategic investment decisions. In our model, a supplier agent is seeking to take market positions relatively to its competitor through the market share that he can expect to secure its investment decisions. It appears that risk reduction highlighted here through bilateral transactions is a more realistic approach when assessing investment decisions in a competitive and uncertain electricity market.