Capital market expectations and the London office market

The analysis of office market dynamics has generally concentrated on the impact of underlying fundamental demand and supply variables. This paper takes a slightly different approach to many previous examinations of rental dynamics. Within a Vector-Error-Correction framework the empirical analysis concentrates upon the impact of economic and financial variables on rents in the City of London and West End of London office markets. The impulse response and variance decomposition reveal that while lagged rental values and key demand drivers play a highly important role in the dynamics of rents, financial variables are also influential. Stock market performance not only influences the City of London market but also the West End, whilst the default spread plays an important role in recent years. It is argued that both series incorporate expectations about future economic performance and that this is the basis of their influence upon rental values.

Asymmetric space market adjustment in the London office market

Models of the City of London office market are extended by considering a longer time series of data, covering two cycles, and by explicit modeling of asymmetric rental response to supply and demand model. A long run structural model linking demand for office space, real rental levels and office-based employment is estimated and then rental adjustment processes are modeled using an error correction model framework. Adjustment processes are seen to be asymmetric, dependent both on the direction of the supply and demand shock and on the state of the rental market at the time of the shock. A complete system of equations is estimated: unit shocks produce oscillations but there is a return to a steady equilibrium state in the long run.

Asymmetric response of demand-supply mismatch to investor's sentiment

We look through both the demand and supply side information to understand dynamics of price determination in the real estate market and examine how accurately investors’ attitudes predict the market returns and thereby flagging off extent of any demand-supply mismatch. Our hypothesis is based on the possibility that investors’ call for action in terms of their buy/sell decision and adjustment in reservation/offer prices may indicate impending demand-supply imbalances in the market. In the process, we study several real estate sectors to inform our analysis. The timeframe of our analysis (1995-2010) allows us to observe market dynamics over several economic cycles and in various stages of those cycles. Additionally, we also seek to understand how investors’ attitude or the sentiment affects the market activity over the cycles through asymmetric responses. We test our hypothesis variously using a number of measures of market activity and attitude indicators within several model specifications. The empirical models are estimated using Vector Error Correction framework. Our analysis suggests that investors’ attitude exert strong and statistically significant feedback effects in price determination. Moreover, these effects do reveal heterogeneous responses across the real estate sectors. Interestingly, our results indicate the asymmetric responses during boom, normal and recessionary periods. These results are consistent with the theoretical underpinnings.

Does consumption-wealth ratio signal stock returns? VECM results for Germany

This paper studies the signalling effect of the consumption−wealth ratio (cay) on German stock returns via vector error correction models (VECMs). The effect of cay on U.S. stock returns has been recently confirmed by Lettau and Ludvigson with a two−stage method. In this paper, performance of the VECMs and the two−stage method are compared in both German and U.S. data. It is found that the VECMs are more suitable to study the effect of cay on stock returns than the two−stage method. Using the Conditional−Subset VECM, cay signals real stock returns and excess returns in both data sets significantly. The estimated coefficient on cay for stock returns turns out to be two times greater in U.S. data than in German data. When the two−stage method is used, cay has no significant effect on German stock returns. Besides, it is also found that cay signals German wealth growth and U.S. income growth significantly.

Price discovery of credit spreads in tranquil and crisis periods

In this paper we investigate the price discovery process in single-name credit spreads obtained from bond, credit default swap (CDS), equity and equity option prices. We analyse short term price discovery by modelling daily changes in credit spreads in the four markets with a vector autoregressive model (VAR). We also look at price discovery in the long run with a vector error correction model (VECM). We find that in the short term the option market clearly leads the other markets in the sub-prime crisis (2007-2009). During the less severe sovereign debt crisis (2009-2012) and the pre-crisis period, options are still important but CDSs become more prominent. In the long run, deviations from the equilibrium relationship with the option market still lead to adjustments in the credit spreads observed or implied from other markets. However, options no longer dominate price discovery in any of the periods considered. Our findings have implications for traders, credit risk managers and financial regulators.

A trading strategy based on the lead–lag relationship between the spot index and futures contract for the FTSE 100

This paper examines the lead–lag relationship between the FTSE 100 index and index futures price employing a number of time series models. Using 10-min observations from June 1996–1997, it is found that lagged changes in the futures price can help to predict changes in the spot price. The best forecasting model is of the error correction type, allowing for the theoretical difference between spot and futures prices according to the cost of carry relationship. This predictive ability is in turn utilised to derive a trading strategy which is tested under real-world conditions to search for systematic profitable trading opportunities. It is revealed that although the model forecasts produce significantly higher returns than a passive benchmark, the model was unable to outperform the benchmark after allowing for transaction costs.

The impact of liquidity on senior credit index spreads during the subprime crisis

This paper examines the effects of liquidity during the 2007–09 crisis, focussing on the Senior Tranche of the CDX.NA.IG Index and on Moody's AAA Corporate Bond Index. It aims to understand whether the sharp increase in the credit spreads of these AAA-rated credit indices can be explained by worse credit fundamentals alone or whether it also reflects a lack of depth in the relevant markets, the scarcity of risk-capital, and the liquidity preference exhibited by investors. Using cointegration analysis and error correction models, the paper shows that during the crisis lower market and funding liquidity are important drivers of the increase in the credit spread of the AAA-rated structured product, whilst they are less significant in explaining credit spread changes for a portfolio of unstructured credit instruments. Looking at the experience of the subprime crisis, the study shows that when the conditions under which securitisation can work properly (liquidity, transparency and tradability) suddenly disappear, investors are left highly exposed to systemic risk.

Why the hand motion proceeds the target in tracking experiment?

In the present study, to shed light on a role of positional error correction mechanism and prediction mechanism in the proactive control discovered earlier, we carried out a visual tracking experiment, in which the region where target was shown, was regulated in a circular orbit. Main results found in this research were following. Recognition of a time step, obtained from the environmental stimuli, is required for the predictive function. The period of the rhythm in the brain obtained from environmental stimuli is shortened about 10%, when the visual information is cut-off. The shortening of the period of the rhythm in the brain accelerates the motion as soon as the visual information is cut-off, and lets the hand motion precedes the target motion. Although the precedence of the hand in the blind region is reset by the environmental information when the target enters the visible region, the hand precedes in average the target when the predictive mechanism dominates the error-corrective mechanism.

Reading aloud in high schools: students and teachers across the curriculum

Reading aloud is apparently an indispensible part of teaching. Nevertheless, little is known about reading aloud across the curriculum by students and teachers in high schools. Nor do we understand teachers’ attitudes towards issues such as error correction, rehearsal time, and selecting students to read. A survey of 360 teachers in England shows that, although they have little training in reading aloud, they are extremely confident. Reading aloud by students and teachers is strongly related, and serves to further understanding rather than administrative purposes or pupils’ enjoyment. Unexpectedly, Modern Language teachers express views that set them apart from other subjects.

Efficient evaluation of highly oscillatory acoustic scattering surface integrals

We consider the approximation of some highly oscillatory weakly singular surface integrals, arising from boundary integral methods with smooth global basis functions for solving problems of high frequency acoustic scattering by three-dimensional convex obstacles, described globally in spherical coordinates. As the frequency of the incident wave increases, the performance of standard quadrature schemes deteriorates. Naive application of asymptotic schemes also fails due to the weak singularity. We propose here a new scheme based on a combination of an asymptotic approach and exact treatment of singularities in an appropriate coordinate system. For the case of a spherical scatterer we demonstrate via error analysis and numerical results that, provided the observation point is sufficiently far from the shadow boundary, a high level of accuracy can be achieved with a minimal computational cost.

Scalability of efficient parallel K-Means

Clustering is defined as the grouping of similar items in a set, and is an important process within the field of data mining. As the amount of data for various applications continues to increase, in terms of its size and dimensionality, it is necessary to have efficient clustering methods. A popular clustering algorithm is K-Means, which adopts a greedy approach to produce a set of K-clusters with associated centres of mass, and uses a squared error distortion measure to determine convergence. Methods for improving the efficiency of K-Means have been largely explored in two main directions. The amount of computation can be significantly reduced by adopting a more efficient data structure, notably a multi-dimensional binary search tree (KD-Tree) to store either centroids or data points. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient K-Means techniques in parallel computational environments. In this work, we provide a parallel formulation for the KD-Tree based K-Means algorithm and address its load balancing issues.

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Efficient and accurate experimental design for enzyme kinetics: Bayesian studies reveal a systematic approach

In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of K-M and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. (C) 2003 Elsevier Science B.V. All rights reserved.

Efficient modeling and inference for event-related fMRI data

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL. (C) 2008 Elsevier B.V. All rights reserved.

Efficient on the fly calculation of time correlation functions in computer simulations

Time correlation functions yield profound information about the dynamics of a physical system and hence are frequently calculated in computer simulations. For systems whose dynamics span a wide range of time, currently used methods require significant computer time and memory. In this paper, we discuss the multiple-tau correlator method for the efficient calculation of accurate time correlation functions on the fly during computer simulations. The multiple-tau correlator is efficacious in terms of computational requirements and can be tuned to the desired level of accuracy. Further, we derive estimates for the error arising from the use of the multiple-tau correlator and extend it for use in the calculation of mean-square particle displacements and dynamic structure factors. The method described here, in hardware implementation, is routinely used in light scattering experiments but has not yet found widespread use in computer simulations.