991 resultados para STOCHASTIC MARKETS


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"The financial system is a key influencer of the health and efficiency of an economy. The role of the financial system is to gather money from people and businesses that currently have more money than they need and transfer it to those that can use it for either business or consumer expenditures. This flow of funds through financial markets and institutions in the Australian economy is huge (in the billions of dollars), affecting business profits, the rate of inflation, interest rates and the production of goods and services. In general, the larger the flow of funds and the more efficient the financial system, the greater the economic output and welfare in the economy. It is not possible to have a modern, complex economy such as that in Australia, without an efficient and sound financial system. The global financial crisis (GFC) of late 2007–09 (and the ensuing European debt crisis), where the global financial market was on the brink of collapse with only significant government intervention stopping a catastrophic global failure of the market, illustrated the importance of the financial system. Financial Markets, Institutions and Money 3rd edition introduces students to the financial system, its operations, and participants. The text offers a fresh, succinct analysis of the financial markets and discusses how the many participants in the financial system interrelate. This includes coverage of regulators, regulations and the role of the Reserve Bank of Australia, that ensure the system’s smooth running, which is essential to a modern economy. The text has been significantly revised to take into account changes in the financial world."---publisher website Table of Contents 1. The financial system - an overview 2. The Monetary Authorities 3. The Reserve Bank of Australia and interest rates 4. The level of interest rates 5. Mathematics of finance 6. Bond Prices and interest rate risk 7. The Structure of Interest Rates 8. Money Markets 9. Bond Markets 10. Equity Markets

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There has been considerable recent work on the development of energy conserving one-step methods that are not symplectic. Here we extend these ideas to stochastic Hamiltonian problems with additive noise and show that there are classes of Runge-Kutta methods that are very effective in preserving the expectation of the Hamiltonian, but care has to be taken in how the Wiener increments are sampled at each timestep. Some numerical simulations illustrate the performance of these methods.

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The purpose of this scoping paper is to offer an overview of the literature to determine the development to date in the area of residential real estate agency academic and career education in respect to Foreign Direct Investment (FDI) transactions and implications in Australia. This paper will review studies on the issue of foreign real estate ownership and FDI in Australian real estate markets to develop an understanding of the current state of knowledge on residential real estate agency practice, career education and real estate licensing requirements in Australia. The distinction between the real estate profession education, compared to other professions such as accounting, legal and finance is based on the intensity of the professional career training prior or post formal academic training. Real estate education could be carried out with relatively higher standards in terms of licensing requirement, career and academic education. As FDI in the Australian real estate market is a complex globalisation and economic phenomenon, a simple content of residential real estate training and education may not promote proper management or capacity in dealing with relevant foreign residential property market transaction. The preliminary summarising from the literature of residential real estate agency education, with its current relevant or emerging licensing requirement are focused on its role and effectiveness and impact in residential real estate market. Particular focus will be directed to the FDI relevant residential real estate agency transactions and practices, which have been strongly influenced by the current residential real estate market and agency practices. Taken together, there are many opportunities for future research to extend our understanding and improving the residential real estate agency education and training of Foreign Direct Investment in the Australian residential real estate sector.

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Twitter and other social media have become increasingly important tools for maintaining the relationships between fans and their idols across a range of activities, from politics and the arts to celebrity and sports culture. Twitter, Inc. itself has initiated several strategic approaches, especially to entertainment and sporting organisations; late in 2012, for example, a Twitter, Inc. delegation toured Australia in order to develop formal relationships with a number of key sporting bodies covering popular sports such as Australian Rules Football, A-League football (soccer), and V8 touring car racing, as well as to strengthen its connections with key Australian broadcasters and news organisations (Jackson & Christensen, 2012). Similarly, there has been a concerted effort between Twitter Germany and the German Bundesliga clubs and football association to coordinate the presence of German football on Twitter ahead of the 2012–2013 season: the Twitter accounts of almost all first-division teams now bear the official Twitter verification mark, and a system of ‘official’ hashtags for tweeting about individual games (combining the abbreviations of the two teams, e.g. #H96FCB) has also been instituted (Twitter auf Deutsch, 2012).

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This paper focuses on Australian development firms in the console and mobile games industry in order to understand how small firms in a geographically remote and marginal position in the global industry are able to relate to global firms and capture revenue share. This paper shows that, while technological change in the games industry has resulted in the emergence of new industry segments based on transactional rather than relational forms of economic coordination, in which we might therefore expect less asymmetrical power relations, lead firms retain a position of power in the global games entertainment industry relative to remote developers. This has been possible because lead firms in the emerging mobile devices market have developed and sustained bottlenecks in their segment of the industry through platform competition and the development of an intensely competitive ecosystem of developers. Our research shows the critical role of platform competition and bottlenecks in influencing power asymmetries within global markets.

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As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

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Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety.

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This paper will develop and illustrate a concept of institutional viscosity to balance the more agentive concept of motility with a theoretical account of structural conditions. The argument articulates with two bodies of work: Archer’s (2007, 2012) broad social theory of reflexivity as negotiating agency and social structures; and Urry’s (2007) sociology of mobility and mobility systems. It then illustrates the concept of viscosity as a variable (low to high viscosity) through two empirical studies conducted in the sociology of education that help demonstrate how degrees of viscosity interact with degrees of motility, and how this interaction can impact on motility over time. The first study explored how Australian Defence Force families cope with their children’s disrupted education given frequent forced relocations. The other study explored how middle class professionals relate to career and educational opportunities in rural and remote Queensland. These two life conditions have produced very different institutional practices to make relocations thinkable and doable, by variously constraining or enabling mobility. In turn, the degrees of viscosity mobile individuals meet with over time can erode or elevate their motility.

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A spatial process observed over a lattice or a set of irregular regions is usually modeled using a conditionally autoregressive (CAR) model. The neighborhoods within a CAR model are generally formed deterministically using the inter-distances or boundaries between the regions. An extension of CAR model is proposed in this article where the selection of the neighborhood depends on unknown parameter(s). This extension is called a Stochastic Neighborhood CAR (SNCAR) model. The resulting model shows flexibility in accurately estimating covariance structures for data generated from a variety of spatial covariance models. Specific examples are illustrated using data generated from some common spatial covariance functions as well as real data concerning radioactive contamination of the soil in Switzerland after the Chernobyl accident.

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This article presents new theoretical and empirical evidence on the forecasting ability of prediction markets. We develop a model that predicts that the time until expiration of a prediction market should negatively affect the accuracy of prices as a forecasting tool in the direction of a ‘favourite/longshot bias’. That is, high-likelihood events are underpriced, and low-likelihood events are over-priced. We confirm this result using a large data set of prediction market transaction prices. Prediction markets are reasonably well calibrated when time to expiration is relatively short, but prices are significantly biased for events farther in the future. When time value of money is considered, the miscalibration can be exploited to earn excess returns only when the trader has a relatively low discount rate.

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We address the problem of finite horizon optimal control of discrete-time linear systems with input constraints and uncertainty. The uncertainty for the problem analysed is related to incomplete state information (output feedback) and stochastic disturbances. We analyse the complexities associated with finding optimal solutions. We also consider two suboptimal strategies that could be employed for larger optimization horizons.

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This paper examines the properties of various approximation methods for solving stochastic dynamic programs in structural estimation problems. The problem addressed is evaluating the expected value of the maximum of available choices. The paper shows that approximating this by the maximum of expected values frequently has poor properties. It also shows that choosing a convenient distributional assumptions for the errors and then solving exactly conditional on the distributional assumption leads to small approximation errors even if the distribution is misspecified. © 1997 Cambridge University Press.