The Risk Microstructure of Corporate Bonds: A Case Study from the German Corporate Bond Market

This article presents joint econometric analysis of interest rate risk, issuer‐specific risk (credit risk) and bond‐specific risk (liquidity risk) in a reduced‐form framework. We estimate issuer‐specific and bond‐specific risk from corporate bond data in the German market. We find that bond‐specific risk plays a crucial role in the pricing of corporate bonds. We observe substantial differences between different bonds with respect to the relative influence of issuer‐specific vs. bond‐specific spread on the level and the volatility of the total spread. Issuer‐specific risk exhibits strong autocorrelation and a strong impact of weekday effects, the level of the risk‐free term structure and the debt to value ratio. Moreover, we can observe some impact of the stock market volatility, the respective stock's return and the distance to default. For the bond‐specific risk we find strong autocorrelation, some impact of the stock market index, the stock market volatility, weekday effects and monthly effects as well as a very weak impact of the risk‐free term structure and the specific stock's return. Altogether, the determinants of the spread components vary strongly between different bonds/issuers.     

Stochastic model specification search for Gaussian and partial non-Gaussian state space models

Model specification for state space models is a difficult task as one has to decide which components to include in the model and to specify whether these components are fixed or time-varying. To this aim a new model space MCMC method is developed in this paper. It is based on extending the Bayesian variable selection approach which is usually applied to variable selection in regression models to state space models. For non-Gaussian state space models stochastic model search MCMC makes use of auxiliary mixture sampling. We focus on structural time series models including seasonal components, trend or intervention. The method is applied to various well-known time series.     

Forward and spot exchange rates in a bivariate TAR framework

Structural exchange rate models explain only a small part of the movements in dollar exchange rate. Recent empirical work has focused on the failure to account for nonlinearities in the data generating mechanism, as an explanation of this bad performance. Here two bivariate threshold autoregressive models for the spot and forward exchange rates are considered. In the first model the regimes are determined by the log difference of the two rates; in the second one the regimes are driven by the forward spot no-arbitrage condition. These processes are able to capture the ‘swing’ behaviour observed in the exchange rate market. Finally the forecasting ability of the models for the dollar/DM exchange rate is evaluated by stochastic simulation.     

On the Application of Markov Chain Monte Carlo Methods to Genetic Analyses on Complex Pedigrees

Markov chain Monte Carlo methods are frequently used in the analyses of genetic data on pedigrees for the estimation of probabilities and likelihoods which cannot be calculated by existing exact methods. In the case of discrete data, the underlying Markov chain may be reducible and care must be taken to ensure that reliable estimates are obtained. Potential reducibility thus has implications for the analysis of the mixed inheritance model, for example, where genetic variation is assumed to be due to one single locus of large effect and many loci each with a small effect. Similarly, reducibility arises in the detection of quantitative trait loci from incomplete discrete marker data. This paper aims to describe the estimation problem in terms of simple discrete genetic models and the single-site Gibbs sampler. Reducibility of the Gibbs sampler is discussed and some current methods for circumventing the problem outlined.     

Scale and structure in economic modelling

In this paper an attempt is made to put together, within the same model, the supply and the demand side of the economic process. This is done by integrating the progress attained in two fields which have traditionally been considered separate: the input-output approach and the macroeconometric final demand approach. The composition of each traditional macroeconomic variable, determined within the model, is analysed into the functional headings which are peculiar for each variable (family budgets headings for personal consumption expenditures, investing sectors for investments, I-O sectors for output and so on). We can then identify for each variable a scale component - absolute level of the aggregated variable - and a structure component - its inner composition - which are mutually consistent. Two types of applications of the same model are then compared: simulation, where the parametric set is used to obtain the dynamic path of a specific scenario; and integrated impact multiplier analysis where scale and structure effects are evaluated with reference to all the potential behaviour of the system.     

基于ATM的MPLS仿真模型的总体设计

讨论了基于ATM的MPLS仿真模型的总体设计和实现。该设计基于OPNET的模型库基础上完成。实现的模型可以丰富OPNET仿真模型库，可以为使用OPNET进行有关基于ATM的MPLS仿真研究提供了一个途径和基础。     

Forecasting and turning point predictions in a Bayesian panel VAR model

We provide methods for forecasting variables and predicting turning points in panel Bayesian VARs. We specify a flexible model, which accounts for both interdependencies in the cross section and time variations in the parameters. Posterior distributions for the parameters are obtained for hierarchical and for Minnesota-type priors. Formulas for multistep, multiunit point and average forecasts are provided. An application to the problem of forecasting the growth rate of output and of predicting turning points in the G-7 illustrates the approach. A comparison with alternative forecasting methods is also provided.     

On Jumps and ARCH Effects in Natural Resource Prices: An Application to Pacific Northwest Stumpage Prices

Continuous-time models of natural resource prices usually preclude the possibility of large changes (jumps) resulting from unexpected events. To test for the presence of jumps and/or ARCH effects, we combine bounds and the Monte Carlo test technique to obtain finite-sample, level-exact p -values. We apply this methodology to stumpage prices from the Pacific Northwest and find evidence of jumps and ARCH effects. To assess the impact of neglecting jumps on the decision to harvest old-growth timber, we develop an autonomous, infinite-horizon stopping model for which we provide a new method of resolution. Our numerical results show the importance of modeling jumps explicitly.     

Assessing the impact of cost optimization based on infrastructure modelling on CO2 emissions

Traditionally, logistics design is driven by a need to reduce costs and improve customer service. Recently, the environmental concerns from transport have been increasingly discussed. The traffic levels and associated energy consumption are influenced by supply chain structure, modal split and vehicle utilization. This paper aims to assess the impact of the traditional cost optimization approach to strategic modelling on overall logistics costs and CO₂ emissions by taking into account the supply chain structure (number of depots) and different freight vehicle utilization ratios (90%, 75% and 60%). The simulation model, based on a European case study from the automotive industry, considers strategic and operational level decisions simultaneously. The analysis shows that the optimum design based on costs does not necessary equate to an optimum solution for CO₂ emissions, therefore there is a need to address economical and environmental objectives explicitly as part of the logistics design.