International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

An examination of the complementary volume–volatility information theories

The volume–volatility relationship during the dissemination stages of information flow is examined by analyzing various theories relating volume and volatility as complementary rather than competing models. The mixture of distributions hypothesis, sequential arrival of information hypothesis, the dispersion of beliefs hypothesis, and the noise trader hypothesis all add to the understanding of how volume and volatility interact for different types of futures traders. An integrated picture of the volume–volatility relationship is provided by investigating the dynamic linear and nonlinear associations between volatility and the volume of informed (institutional) and uninformed (the general public) traders. In particular, the trading behavior explanation for the persistence of futures volatility, the effect of the timing of private information arrival, and the response of institutional traders to excess noise trading risk is examined

Fit for planning? An evaluation of the application of development viability appraisal models in the UK planning system

The aim of this paper is to critically examine the application of development appraisal to viability assessment in the planning system. This evaluation is of development appraisal models in general and also their use in particular applications associated with estimating planning obligation capacity. The paper is organised into four themes: · The context and conceptual basis for development viability appraisal · A review of development viability appraisal methods · A discussion of selected key inputs into a development viability appraisal · A discussion of the applications of development viability appraisals in the planning system It is assumed that readers are familiar with the basic models and information needs of development viability appraisal rather than at the cutting edge of practice and/or academe

Reconciling model and information uncertainty in development appraisal

This paper investigates the effect of choices of model structure and scale in development viability appraisal. The paper addresses two questions concerning the application of development appraisal techniques to viability modelling within the UK planning system. The first relates to the extent to which, given intrinsic input uncertainty, the choice of model structure significantly affects model outputs. The second concerns the extent to which, given intrinsic input uncertainty, the level of model complexity significantly affects model outputs. Monte Carlo simulation procedures are applied to a hypothetical development scheme in order to measure the effects of model aggregation and structure on model output variance. It is concluded that, given the particular scheme modelled and unavoidably subjective assumptions of input variance, simple and simplistic models may produce similar outputs to more robust and disaggregated models.

Stable Nonlinear Receding Horizon Regulator Using RBF Neural Network Models

The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.

Predicting metal-binding site residues in low-resolution structural models

The accurate prediction of the biochemical function of a protein is becoming increasingly important, given the unprecedented growth of both structural and sequence databanks. Consequently, computational methods are required to analyse such data in an automated manner to ensure genomes are annotated accurately. Protein structure prediction methods, for example, are capable of generating approximate structural models on a genome-wide scale. However, the detection of functionally important regions in such crude models, as well as structural genomics targets, remains an extremely important problem. The method described in the current study, MetSite, represents a fully automatic approach for the detection of metal-binding residue clusters applicable to protein models of moderate quality. The method involves using sequence profile information in combination with approximate structural data. Several neural network classifiers are shown to be able to distinguish metal sites from non-sites with a mean accuracy of 94.5%. The method was demonstrated to identify metal-binding sites correctly in LiveBench targets where no obvious metal-binding sequence motifs were detectable using InterPro. Accurate detection of metal sites was shown to be feasible for low-resolution predicted structures generated using mGenTHREADER where no side-chain information was available. High-scoring predictions were observed for a recently solved hypothetical protein from Haemophilus influenzae, indicating a putative metal-binding site.

Design and management of sustainable built environments

The United Nation Intergovernmental Panel on Climate Change (IPCC) makes it clear that climate change is due to human activities and it recognises buildings as a distinct sector among the seven analysed in its 2007 Fourth Assessment Report. Global concerns have escalated regarding carbon emissions and sustainability in the built environment. The built environment is a human-made setting to accommodate human activities, including building and transport, which covers an interdisciplinary field addressing design, construction, operation and management. Specifically, Sustainable Buildings are expected to achieve high performance throughout the life-cycle of siting, design, construction, operation, maintenance and demolition, in the following areas: • energy and resource efficiency; • cost effectiveness; • minimisation of emissions that negatively impact global warming, indoor air quality and acid rain; • minimisation of waste discharges; and • maximisation of fulfilling the requirements of occupants’ health and wellbeing. Professionals in the built environment sector, for example, urban planners, architects, building scientists, engineers, facilities managers, performance assessors and policy makers, will play a significant role in delivering a sustainable built environment. Delivering a sustainable built environment needs an integrated approach and so it is essential for built environment professionals to have interdisciplinary knowledge in building design and management . Building and urban designers need to have a good understanding of the planning, design and management of the buildings in terms of low carbon and energy efficiency. There are a limited number of traditional engineers who know how to design environmental systems (services engineer) in great detail. Yet there is a very large market for technologists with multi-disciplinary skills who are able to identify the need for, envision and manage the deployment of a wide range of sustainable technologies, both passive (architectural) and active (engineering system),, and select the appropriate approach. Employers seek applicants with skills in analysis, decision-making/assessment, computer simulation and project implementation. An integrated approach is expected in practice, which encourages built environment professionals to think ‘out of the box’ and learn to analyse real problems using the most relevant approach, irrespective of discipline. The Design and Management of Sustainable Built Environment book aims to produce readers able to apply fundamental scientific research to solve real-world problems in the general area of sustainability in the built environment. The book contains twenty chapters covering climate change and sustainability, urban design and assessment (planning, travel systems, urban environment), urban management (drainage and waste), buildings (indoor environment, architectural design and renewable energy), simulation techniques (energy and airflow), management (end-user behaviour, facilities and information), assessment (materials and tools), procurement, and cases studies ( BRE Science Park). Chapters one and two present general global issues of climate change and sustainability in the built environment. Chapter one illustrates that applying the concepts of sustainability to the urban environment (buildings, infrastructure, transport) raises some key issues for tackling climate change, resource depletion and energy supply. Buildings, and the way we operate them, play a vital role in tackling global greenhouse gas emissions. Holistic thinking and an integrated approach in delivering a sustainable built environment is highlighted. Chapter two demonstrates the important role that buildings (their services and appliances) and building energy policies play in this area. Substantial investment is required to implement such policies, much of which will earn a good return. Chapters three and four discuss urban planning and transport. Chapter three stresses the importance of using modelling techniques at the early stage for strategic master-planning of a new development and a retrofit programme. A general framework for sustainable urban-scale master planning is introduced. This chapter also addressed the needs for the development of a more holistic and pragmatic view of how the built environment performs, , in order to produce tools to help design for a higher level of sustainability and, in particular, how people plan, design and use it. Chapter four discusses microcirculation, which is an emerging and challenging area which relates to changing travel behaviour in the quest for urban sustainability. The chapter outlines the main drivers for travel behaviour and choices, the workings of the transport system and its interaction with urban land use. It also covers the new approach to managing urban traffic to maximise economic, social and environmental benefits. Chapters five and six present topics related to urban microclimates including thermal and acoustic issues. Chapter five discusses urban microclimates and urban heat island, as well as the interrelationship of urban design (urban forms and textures) with energy consumption and urban thermal comfort. It introduces models that can be used to analyse microclimates for a careful and considered approach for planning sustainable cities. Chapter six discusses urban acoustics, focusing on urban noise evaluation and mitigation. Various prediction and simulation methods for sound propagation in micro-scale urban areas, as well as techniques for large scale urban noise-mapping, are presented. Chapters seven and eight discuss urban drainage and waste management. The growing demand for housing and commercial developments in the 21st century, as well as the environmental pressure caused by climate change, has increased the focus on sustainable urban drainage systems (SUDS). Chapter seven discusses the SUDS concept which is an integrated approach to surface water management. It takes into consideration quality, quantity and amenity aspects to provide a more pleasant habitat for people as well as increasing the biodiversity value of the local environment. Chapter eight discusses the main issues in urban waste management. It points out that population increases, land use pressures, technical and socio-economic influences have become inextricably interwoven and how ensuring a safe means of dealing with humanity’s waste becomes more challenging. Sustainable building design needs to consider healthy indoor environments, minimising energy for heating, cooling and lighting, and maximising the utilisation of renewable energy. Chapter nine considers how people respond to the physical environment and how that is used in the design of indoor environments. It considers environmental components such as thermal, acoustic, visual, air quality and vibration and their interaction and integration. Chapter ten introduces the concept of passive building design and its relevant strategies, including passive solar heating, shading, natural ventilation, daylighting and thermal mass, in order to minimise heating and cooling load as well as energy consumption for artificial lighting. Chapter eleven discusses the growing importance of integrating Renewable Energy Technologies (RETs) into buildings, the range of technologies currently available and what to consider during technology selection processes in order to minimise carbon emissions from burning fossil fuels. The chapter draws to a close by highlighting the issues concerning system design and the need for careful integration and management of RETs once installed; and for home owners and operators to understand the characteristics of the technology in their building. Computer simulation tools play a significant role in sustainable building design because, as the modern built environment design (building and systems) becomes more complex, it requires tools to assist in the design process. Chapter twelve gives an overview of the primary benefits and users of simulation programs, the role of simulation in the construction process and examines the validity and interpretation of simulation results. Chapter thirteen particularly focuses on the Computational Fluid Dynamics (CFD) simulation method used for optimisation and performance assessment of technologies and solutions for sustainable building design and its application through a series of cases studies. People and building performance are intimately linked. A better understanding of occupants’ interaction with the indoor environment is essential to building energy and facilities management. Chapter fourteen focuses on the issue of occupant behaviour; principally, its impact, and the influence of building performance on them. Chapter fifteen explores the discipline of facilities management and the contribution that this emerging profession makes to securing sustainable building performance. The chapter highlights a much greater diversity of opportunities in sustainable building design that extends well into the operational life. Chapter sixteen reviews the concepts of modelling information flows and the use of Building Information Modelling (BIM), describing these techniques and how these aspects of information management can help drive sustainability. An explanation is offered concerning why information management is the key to ‘life-cycle’ thinking in sustainable building and construction. Measurement of building performance and sustainability is a key issue in delivering a sustainable built environment. Chapter seventeen identifies the means by which construction materials can be evaluated with respect to their sustainability. It identifies the key issues that impact the sustainability of construction materials and the methodologies commonly used to assess them. Chapter eighteen focuses on the topics of green building assessment, green building materials, sustainable construction and operation. Commonly-used assessment tools such as BRE Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design ( LEED) and others are introduced. Chapter nineteen discusses sustainable procurement which is one of the areas to have naturally emerged from the overall sustainable development agenda. It aims to ensure that current use of resources does not compromise the ability of future generations to meet their own needs. Chapter twenty is a best-practice exemplar - the BRE Innovation Park which features a number of demonstration buildings that have been built to the UK Government’s Code for Sustainable Homes. It showcases the very latest innovative methods of construction, and cutting edge technology for sustainable buildings. In summary, Design and Management of Sustainable Built Environment book is the result of co-operation and dedication of individual chapter authors. We hope readers benefit from gaining a broad interdisciplinary knowledge of design and management in the built environment in the context of sustainability. We believe that the knowledge and insights of our academics and professional colleagues from different institutions and disciplines illuminate a way of delivering sustainable built environment through holistic integrated design and management approaches. Last, but not least, I would like to take this opportunity to thank all the chapter authors for their contribution. I would like to thank David Lim for his assistance in the editorial work and proofreading.

How much information is lost by using global-mean climate metrics? an example using the transport sector

Metrics are often used to compare the climate impacts of emissions from various sources, sectors or nations. These are usually based on global-mean input, and so there is the potential that important information on smaller scales is lost. Assuming a non-linear dependence of the climate impact on local surface temperature change, we explore the loss of information about regional variability that results from using global-mean input in the specific case of heterogeneous changes in ozone, methane and aerosol concentrations resulting from emissions from road traffic, aviation and shipping. Results from equilibrium simulations with two general circulation models are used. An alternative metric for capturing the regional climate impacts is investigated. We find that the application of a metric that is first calculated locally and then averaged globally captures a more complete and informative signal of climate impact than one that uses global-mean input. The loss of information when heterogeneity is ignored is largest in the case of aviation. Further investigation of the spatial distribution of temperature change indicates that although the pattern of temperature response does not closely match the pattern of the forcing, the forcing pattern still influences the response pattern on a hemispheric scale. When the short-lived transport forcing is superimposed on present-day anthropogenic CO2 forcing, the heterogeneity in the temperature response to CO2 dominates. This suggests that the importance of including regional climate impacts in global metrics depends on whether small sectors are considered in isolation or as part of the overall climate change.

Markov models of aging: theory and practice

We review and structure some of the mathematical and statistical models that have been developed over the past half century to grapple with theoretical and experimental questions about the stochastic development of aging over the life course. We suggest that the mathematical models are in large part addressing the problem of partitioning the randomness in aging: How does aging vary between individuals, and within an individual over the lifecourse? How much of the variation is inherently related to some qualities of the individual, and how much is entirely random? How much of the randomness is cumulative, and how much is merely short-term flutter? We propose that recent lines of statistical inquiry in survival analysis could usefully grapple with these questions, all the more so if they were more explicitly linked to the relevant mathematical and biological models of aging. To this end, we describe points of contact among the various lines of mathematical and statistical research. We suggest some directions for future work, including the exploration of information-theoretic measures for evaluating components of stochastic models as the basis for analyzing experiments and anchoring theoretical discussions of aging.

Multi-target out-of-sequence data association: tracking using graphical models

When performing data fusion, one often measures where targets were and then wishes to deduce where targets currently are. There has been recent research on the processing of such out-of-sequence data. This research has culminated in the development of a number of algorithms for solving the associated tracking problem. This paper reviews these different approaches in a common Bayesian framework and proposes an architecture that orthogonalises the data association and out-of-sequence problems such that any combination of solutions to these two problems can be used together. The emphasis is not on advocating one approach over another on the basis of computational expense, but rather on understanding the relationships among the algorithms so that any approximations made are explicit. Results for a multi-sensor scenario involving out-of-sequence data association are used to illustrate the utility of this approach in a specific context.

Regularized logistic models for probabilistic forecasting and diagnostics

Logistic models are studied as a tool to convert dynamical forecast information (deterministic and ensemble) into probability forecasts. A logistic model is obtained by setting the logarithmic odds ratio equal to a linear combination of the inputs. As with any statistical model, logistic models will suffer from overfitting if the number of inputs is comparable to the number of forecast instances. Computational approaches to avoid overfitting by regularization are discussed, and efficient techniques for model assessment and selection are presented. A logit version of the lasso (originally a linear regression technique), is discussed. In lasso models, less important inputs are identified and the corresponding coefficient is set to zero, providing an efficient and automatic model reduction procedure. For the same reason, lasso models are particularly appealing for diagnostic purposes.

Probabilistic evaluation of time series models : a comparison of several approaches

Several methods are examined which allow to produce forecasts for time series in the form of probability assignments. The necessary concepts are presented, addressing questions such as how to assess the performance of a probabilistic forecast. A particular class of models, cluster weighted models (CWMs), is given particular attention. CWMs, originally proposed for deterministic forecasts, can be employed for probabilistic forecasting with little modification. Two examples are presented. The first involves estimating the state of (numerically simulated) dynamical systems from noise corrupted measurements, a problem also known as filtering. There is an optimal solution to this problem, called the optimal filter, to which the considered time series models are compared. (The optimal filter requires the dynamical equations to be known.) In the second example, we aim at forecasting the chaotic oscillations of an experimental bronze spring system. Both examples demonstrate that the considered time series models, and especially the CWMs, provide useful probabilistic information about the underlying dynamical relations. In particular, they provide more than just an approximation to the conditional mean.

The METAFOR project: preserving data through metadata standards for climate models and simulations

Climate modeling is a complex process, requiring accurate and complete metadata in order to identify, assess and use climate data stored in digital repositories. The preservation of such data is increasingly important given the development of ever-increasingly complex models to predict the effects of global climate change. The EU METAFOR project has developed a Common Information Model (CIM) to describe climate data and the models and modelling environments that produce this data. There is a wide degree of variability between different climate models and modelling groups. To accommodate this, the CIM has been designed to be highly generic and flexible, with extensibility built in. METAFOR describes the climate modelling process simply as "an activity undertaken using software on computers to produce data." This process has been described as separate UML packages (and, ultimately, XML schemas). This fairly generic structure canbe paired with more specific "controlled vocabularies" in order to restrict the range of valid CIM instances. The CIM will aid digital preservation of climate models as it will provide an accepted standard structure for the model metadata. Tools to write and manage CIM instances, and to allow convenient and powerful searches of CIM databases,. Are also under development. Community buy-in of the CIM has been achieved through a continual process of consultation with the climate modelling community, and through the METAFOR team’s development of a questionnaire that will be used to collect the metadata for the Intergovernmental Panel on Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) model runs.

The link between temporal attention and emotion: a playground for psychology, neuroscience, and plausible artificial neural networks

In this paper, we will address the endeavors of three disciplines, Psychology, Neuroscience, and Artificial Neural Network (ANN) modeling, in explaining how the mind perceives and attends information. More precisely, we will shed some light on the efforts to understand the allocation of attentional resources to the processing of emotional stimuli. This review aims at informing the three disciplines about converging points of their research and to provide a starting point for discussion.

Pricing and hedging short sterling options using artificial neural networks

This paper compares the performance of artificial neural networks (ANNs) with that of the modified Black model in both pricing and hedging Short Sterling options. Using high frequency data, standard and hybrid ANNs are trained to generate option prices. The hybrid ANN is significantly superior to both the modified Black model and the standard ANN in pricing call and put options. Hedge ratios for hedging Short Sterling options positions using Short Sterling futures are produced using the standard and hybrid ANN pricing models, the modified Black model, and also standard and hybrid ANNs trained directly on the hedge ratios. The performance of hedge ratios from ANNs directly trained on actual hedge ratios is significantly superior to those based on a pricing model, and to the modified Black model.