Estimating Yield Curves by Kernel Smoothing Methods

We introduce a new method for the estimation of discount functions, yield curves and forward curves for coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various important restrictions in the estimation. Our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition. The solution to the sample problem is not explicit and our estimation procedure is iterative, rather like the backfitting method of estimating additive nonparametric models. We establish the asymptotic normality of our methods using the asymptotic representation of our estimator as an infinite series with declining coefficients. The rate of convergence is standard for one dimensional nonparametric regression.Coupon bonds; forward curve; Hilbert space; local linear; nonparametric regression; yield curve

Yield Curve Estimation by Kernel Smoothing Methods

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for the discount function although we do show how to impose various restrictions in the estimation. Our method is based on kernel smoothing and is defined as the minimum of some localized population moment condition. The solution to the sample problem is not explicit and our estimation procedure is iterative, rather like the backfitting method of estimating additive nonparametric models. We establish the asymptotic normality of our methods using the asymptotic representation of our estimator as an infinite series with declining coefficients. The rate of convergence is standard for one dimensional nonparametric regression. We investigate the finite sample performance of our method, in comparison with other well-established methods, in a small simulation experiment.Coupon bonds, kernel estimation, Hilbert space, nonparametric regression, term structure estimation, yield curve, zero coupon.

Dispersed Trading and the Prevention of Market Failure: The Case of the Copenhagen Stock Exchange

With augmented demands on power grids resulting in longer and larger blackouts combined with heightened concerns of terrorist attacks, trading institutions and policy makers have widened their search for systems that avoid market failure during these disturbing events. We provide insight into this issue by examining trading behavior at the Copenhagen Stock Exchange during a major blackout. We find that although market quality declined, markets remained functional and some price discovery occurred during the blackout period suggesting that the NOREX structure of interlinked trading systems combined with widely dispersed trading locations may be a viable means of protection against market failure during massive power disruptions or terrorist attacks.Power failure; Fragmented markets; Market failure;

Evaluating New Interactions in Healthcare: Challenges and Approaches

New technologies for supporting the provision of healthcare are increasingly pervasive. While healthcare computing previously referred to a desktop computer within the consulting room, we are now seeing an ever broader range of software, hardware and settings. This workshop is concerned with how to conduct evaluations which allow assessment of the overall impact of technology. The workshop will explore challenges and approaches for evaluating new interactions in healthcare. In this paper we outline the goals for this workshop and summarize the issues and questions it intends to explore

Global Polynomial Kernel Hazard Estimation

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically reduces bias with unchanged variance. A simulation study investigates the finite-sample properties of GPA. The method is tested on local constant and local linear estimators. From the simulation experiment we conclude that the global estimator improves the goodness-of-fit. An especially encouraging result is that the bias-correction works well for small samples, where traditional bias reduction methods have a tendency to fail

Local linear density estimation for filtered survival data, with bias correction

A class of local linear kernel density estimators based on weighted least-squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to accommodating unusual patterns of exposure as well as occurrence. It is shown that the local linear estimators corresponding to all different weightings have the same pointwise asymptotic properties. However, the weighting previously used in the literature in the i.i.d. case is seen to be far from optimal when it comes to exposure robustness, and a simple alternative weighting is to be preferred. Indeed, this weighting has, effectively, to be well chosen in a 'pilot' estimator of the survival function as well as in the main estimator itself. We also investigate multiplicative and additive bias-correction methods within our framework. The multiplicative bias-correction method proves to be the best in a simulation study comparing the performance of the considered estimators. An example concerning old-age mortality demonstrates the importance of the improvements provided

The Future of Qualitative Research in Psychology: Accentuating the Positive.

In this paper we reflect on current trends and anticipate future prospects regarding qualitative research in Psychology. We highlight various institutional and disciplinary obstacles to qualitative research diversity, complexity and quality. At the same time, we note some causes for optimism, including publication breakthroughs and vitality within the field. The paper is structured into three main sections which consider: 1) the positioning of qualitative research within Psychology; 2) celebrating the different kinds of knowledge produced by qualitative research; and 3) implementing high quality qualitative research. In general we accentuate the positive, recognising and illustrating innovative qualitative research practices which generate new insights and propel the field forward. We conclude by emphasising the importance of research training: for qualitative research to flourish within Psychology (and beyond), students and early career researchers require more sophisticated, in-depth instruction than is currently offered