BookReview

We compare the bias in binomial trees against that in certain analytical/numerical valuation techniques with which they disagree. We consider the CRR tree, the COS method and the Leisen–Reimer as well as the Prekopa–Szantai exponentially smoothed method. We conclude that the binomial trees are unbiased and that the exponentially smoothed method is biased.     

Hierarchical Bayesian Whittaker Graduation

Abstract

The Whittaker method of graduation has been known and used for a long time and has remained popular due to its possession of a number of ideal properties. They include being nonparametric and having an easy to understand foundation. The latter means that it makes sense and thus the user of the method has a good idea of what it can and cannot do. As well, there is a statistical derivation available that uses Bayesian notions. A problem with the derivation is that it is more intuitive than precise and as such does not provide a useful frame of reference for the graduator. Regardless of the point of view, the graduation cannot be completed until the smoothing parameter is selected and this has always relied on the judgment of the analyst.

In this paper, three tasks will be undertaken. The first is to replace the ad-hoc Bayesian derivation of the method with a formal Bayesian specification. The second is to show that with this specification it is possible to complete the graduation without making an arbitrary selection of the smoothing parameter. The third is to provide a Monte Carlo Bayesian approach for the incorporation of constraints in the graduated values. The ideas will be illustrated with a numerical example.     

指数平滑法在路面平整度预测中的应用

通过对路面平整度指数IRI随时间发展规律的分析与研究,提出采用指数平滑法来提高预测精度。经工程实例分析表明,使用该模型可以很好地预测路面平整度的发展,是一种有效而实用的方法。     

Exponentially weighted information criteria for selecting among forecasting models

Information criteria (IC) are often used to decide between forecasting models. Commonly used criteria include Akaike's IC and Schwarz's Bayesian IC. They involve the sum of two terms: the model's log likelihood and a penalty for the number of model parameters. The likelihood is calculated with equal weight being given to all observations. We propose that greater weight should be put on more recent observations in order to reflect more recent accuracy. This seems particularly pertinent when selecting among exponential smoothing methods, as they are based on an exponential weighting principle. In this paper, we use exponential weighting within the calculation of the log likelihood for the IC. Our empirical analysis uses supermarket sales and call centre arrivals data. The results show that basing model selection on the new exponentially weighted IC can outperform individual models and selection based on the standard IC.     

Estimation of Common Factors under Cross‐Sectional and Temporal Aggregation Constraints

The paper estimates a large‐scale mixed‐frequency dynamic factor model for the euro area, using monthly series along with gross domestic product (GDP) and its main components, obtained from the quarterly national accounts (NA). The latter define broad measures of real economic activity (such as GDP and its decomposition by expenditure type and by branch of activity) that we are willing to include in the factor model, in order to improve its coverage of the economy and thus the representativeness of the factors. The main problem with their inclusion is not one of model consistency, but rather of data availability and timeliness, as the NA series are quarterly and are available with a large publication lag. Our model is a traditional dynamic factor model formulated at the monthly frequency in terms of the stationary representation of the variables, which however becomes nonlinear when the observational constraints are taken into account. These are of two kinds: nonlinear temporal aggregation constraints, due to the fact that the model is formulated in terms of the unobserved monthly logarithmic changes, but we observe only the sum of the monthly levels within a quarter, and nonlinear cross‐sectional constraints, since GDP and its main components are linked by the NA identities, but the series are expressed in chained volumes. The paper provides an exact treatment of the observational constraints and proposes iterative algorithms for estimating the parameters of the factor model and for signal extraction, thereby producing nowcasts of monthly GDP and its main components, as well as measures of their reliability.     

Appraisals,Transaction Incentives,and Smoothing

This article is structured around three principal objectives. The first is to determine whether any incentives for appraisals support an underlying purchase offer, which may be termed a transaction bias. Appraisals that are lower than purchase prices could involve additional cost for justification and thus undermine the transaction. The second objective is to test whether appraisal data are smoothed or exhibit less volatility than purchase data. The article compares the volatility of separate appraisal and purchase data. Given separate appraisal and purchase time series, the third objective is to derive the implied optimal appraisal updating rule.The model is applied to appraisal and purchase price indices for 3.7 million repeat transactions on mortgages bought by Fannie Mae and Freddie Mac by using monthly data from January 1975 to December 1993. The estimation procedure uses generalized autoregressive conditioned heteroskedastic (GARCH) analysis to take account of persistence in means and volatility in the house price time series. The article draws three principal conclusions. First, appraisals are systematically higher than purchase data, a first-moment differential. Second, appraisal smoothing does not occur generally. Third, the appraisal updating rule for the United States appears to involve error correction whereby underappraisals from pervious periods are eventually adjusted.     

Monitoring the cross-covariances of a multivariate time series

In this paper sequential procedures are proposed for jointly monitoring all elements of the covariance matrix at lag 0 of a multivariate time series. All control charts are based on exponential smoothing. As a measure of the distance between the target values and the actual values the Mahalanobis distance is used. It is distinguished between residual control schemes and modified control schemes. Several properties of these charts are proved assuming the target process to be a stationary Gaussian process. Within an extensive Monte Carlo study all procedures are compared with each other. As a measure of the performance of a control chart the average run length is used. An empirical example about Eastern European stock markets illustrates how the autocovariance and the cross-covariance structure of financial assets can be monitored by these methods.     

调车作业智能化指挥系统的核心功能及其开发

调车作业智能化指挥系统的核心功能是指系统应具备的本质性关键功能,主要包括钩计划编制功能、作业优化功能,以及体现指挥可调性和系统适应性的柔化功能3个部分。探讨这些核心功能的开发途径和方法,并通过引例对比,说明所开发系统可实现的优化水平。     

Nominal versus indexed debt: A quantitative horse race

The main arguments in favor and against nominal and indexed debts are the incentive to default through inflation versus hedging against unforeseen shocks. We model and calibrate these arguments to assess their quantitative importance. We use a dynamic equilibrium model with tax distortion, government outlays uncertainty, and contingent-debt service. Our framework also recognizes that contingent debt can be associated with incentive problems and lack of commitment. Thus, the benefits of unexpected inflation are tempered by higher interest rates. We obtain that costs from inflation more than offset the benefits from reducing tax distortions. We further discuss sustainability of nominal debt in developing (volatile) countries.     

Efficient estimation and computation for the generalised additive models with unknown link function

The generalised additive models (GAM) are widely used in data analysis. In the application of the GAM, the link function involved is usually assumed to be a commonly used one without justification. Motivated by a real data example with binary response where the commonly used link function does not work, we propose a generalised additive models with unknown link function (GAMUL) for various types of data, including binary, continuous and ordinal. The proposed estimators are proved to be consistent and asymptotically normal. Semiparametric efficiency of the estimators is demonstrated in terms of their linear functionals. In addition, an iterative algorithm, where all estimators can be expressed explicitly as a linear function of $Y$, is proposed to overcome the computational hurdle for the GAM type model. Extensive simulation studies conducted in this paper show the proposed estimation procedure works very well. The proposed GAMUL are finally used to analyze a real dataset about loan repayment in China, which leads to some interesting findings.