A numerical study of tournament structure and seeding policy for the soccer World Cup Finals

Tournament outcome uncertainty depends on: the design of the tournament; and the relative strengths of the competitors – the competitive balance. A tournament design comprises the arrangement of the individual matches, which we call the tournament structure, the seeding policy and the progression rules. In this paper, we investigate the effect of seeding policy for various tournament structures, while taking account of competitive balance. Our methodology uses tournament outcome uncertainty to consider the effect of seeding policy and other design changes. The tournament outcome uncertainty is measured using the tournament outcome characteristic which is the probability P_q,R that a team in the top 100q pre‐tournament rank percentile progresses forward from round R, for all q and R. We use Monte Carlo simulation to calculate the values of this metric. We find that, in general, seeding favours stronger competitors, but that the degree of favouritism varies with the type of seeding. Reseeding after each round favours the strong to the greatest extent. The ideas in the paper are illustrated using the soccer World Cup Finals tournament.     

Patterns on an ancient artifact: a coincidence?

The Phaistos Disk is an ancient artifact from Crete. At each side of the disk, a series of unknown signs is written along a spiral. Professional archaeologists expect that we will only learn what it is until similar objects are found. A statistical analysis in this article shows what it is not: it is not a one‐dimensional text, since there are relations between the signs in adjacent windings of the spiral. Three patterns of such relations have been identified. A Monte Carlo simulation of one of them has been performed, using a model of the spiral form. It is concluded that the probability of this pattern being coincidental is small, well below the conventional threshold.     

专利实物期权定价的蒙特卡罗模拟方法及其改进技术

专利作为现代企业重要的无形资产,对企业募集发展资金、扩大收益回流、形成竞争优势发挥着巨大的作用,因此专利定价问题也成为现代企业管理决策中的重要内容.本文基于专利的实物期权特征,运用蒙特卡罗模拟方法对专利的实物期权定价问题进行研究与探讨.首先,分析探讨专利投资项目的决策过程及其实物期权特征;在此基础上,建立专利定价的实物期权蒙特卡罗模拟模型,并引入对偶变量技术用以提高蒙特卡罗模拟的效率;最后,以生物医药企业专利定价为例进行实证模拟.研究结论认为,引入适当方差减少技术的蒙特卡罗模拟则成为专利实物期权定价的一种有效的分析方法.     

公司债定价：首中时模型的蒙特卡罗实现

论文借鉴信用风险度量的首中时结构模型,通过该模型的蒙特卡罗模拟对我国公司债进行定价,同时探讨了拟合公司债价值最佳的违约回复率设定和动态波动率的模型选择问题。研究表明首中时模型用于公司债定价是可行的,且不同的债券确实存在不同的回复率,并且对同一公司的不同债券使用同样的回复率也是不准确的,另外论文也进一步肯定了GARCH（1,1）模型在估计波动率方面的适用性。     

分整阶数半参数估计的有限样本性质研究

随着对经济和金融时间序列长记忆性的研究,分整阶数估计已成为当前理论研究的焦点问题。以对数周期图回归和局部Whittle方法为代表的半参数分整阶数估计方法在实践中得到广泛应用,但对这两类半参数估计方法的有限样本性质的比较则鲜有涉及,影响了在实践中对估计方法的选择。利用蒙特卡洛模拟方法,在不同数据产生的过程下,这两种半参数估计方法有限样本性质的研究结果表明：在ARFIMA(0, d, 0)过程下,LW类估计量具有较好的小样本性质;在平稳ARFIMA(1, d, 0)过程下,本文建议的QGPH估计量的有限样本性质要优于其他对数周期图估计量;在非平稳过程下,MGPH的偏差最小。     

空间加权矩阵设置与空间面板参数估计效率

总结了常用的空间加权矩阵的一般构建方法和研究领域内新提出的空间加权矩阵的构建方法,从宏观与微观层面,量化分析了空间加权矩阵设置对于空间面板参数估计效率、空间效应识别的影响效应。结论表明：宏观数据层面,随着空间加权矩阵复杂程度的提高,无论是空间面板固定效应模型还是空间面板随机效应模型,参数估计的有效性与一致性都显著提高并且广义矩参数估计方法优于拟极大似然估计方法,复合的空间加权矩阵条件下,拉格朗日乘子检验方法的功效更高;微观数据层面,回归结果表明四种不同类型的空间加权矩阵的设置,对于聚集外部性引致的企业全要素生产率增长的空间边界的识别具有显著影响,复合的空间加权矩阵更有效。     

Exact confidence sets and goodness-of-fit methods for stable distributions

Usual inference methods for stable distributions are typically based on limit distributions. But asymptotic approximations can easily be unreliable in such cases, for standard regularity conditions may not apply or may hold only weakly. This paper proposes finite-sample tests and confidence sets for tail thickness and asymmetry parameters (α$\alpha$ and β$\beta$) of stable distributions. The confidence sets are built by inverting exact goodness-of-fit tests for hypotheses which assign specific values to these parameters. We propose extensions of the Kolmogorov–Smirnov, Shapiro–Wilk and Filliben criteria, as well as the quantile-based statistics proposed by McCulloch (1986) in order to better capture tail behavior. The suggested criteria compare empirical goodness-of-fit or quantile-based measures with their hypothesized values. Since the distributions involved are quite complex and non-standard, the relevant hypothetical measures are approximated by simulation, and p$p$-values are obtained using Monte Carlo (MC) test techniques. The properties of the proposed procedures are investigated by simulation. In contrast with conventional wisdom, we find reliable results with sample sizes as small as 25. The proposed methodology is applied to daily electricity price data in the US over the period 2001–2006. The results show clearly that heavy kurtosis and asymmetry are prevalent in these series.     

Benchmark priors for Bayesian model averaging

In contrast to a posterior analysis given a particular sampling model, posterior model probabilities in the context of model uncertainty are typically rather sensitive to the specification of the prior. In particular, ‘diffuse’ priors on model-specific parameters can lead to quite unexpected consequences. Here we focus on the practically relevant situation where we need to entertain a (large) number of sampling models and we have (or wish to use) little or no subjective prior information. We aim at providing an ‘automatic’ or ‘benchmark’ prior structure that can be used in such cases. We focus on the normal linear regression model with uncertainty in the choice of regressors. We propose a partly non-informative prior structure related to a natural conjugate g-prior specification, where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g_0j. The consequences of different choices for g_0j are examined. We investigate theoretical properties, such as consistency of the implied Bayesian procedure. Links with classical information criteria are provided. More importantly, we examine the finite sample implications of several choices of g_0j in a simulation study. The use of the MC³ algorithm of Madigan and York (Int. Stat. Rev. 63 (1995) 215), combined with efficient coding in Fortran, makes it feasible to conduct large simulations. In addition to posterior criteria, we shall also compare the predictive performance of different priors. A classic example concerning the economics of crime will also be provided and contrasted with results in the literature. The main findings of the paper will lead us to propose a ‘benchmark’ prior specification in a linear regression context with model uncertainty.     

A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL-mapping

A tutorial derivation of the reversible jump Markov chain Monte Carlo (MCMC) algorithm is given. Various examples illustrate how reversible jump MCMC is a general framework for Metropolis-Hastings algorithms where the proposal and the target distribution may have densities on spaces of varying dimension. It is finally discussed how reversible jump MCMC can be applied in genetics to compute the posterior distribution of the number, locations, effects, and genotypes of putative quantitative trait loci.     

Scaledt分布、杠杆效应和上证综指的VaR风险

与正态分布相比，上证指数收益率的经验分布具有尖峰厚尾特征，但用Scaled t-分布比正态分布可以更好地拟合上证指数收益率的经验分布。本文以Scaled t-分布假设下的GJR模型为基础，测量了上证指数收益率波动性的杠杆效应，即信息对波动性的不对称影响：并根据GJR模型应用Monte Carlo模拟方法，测定上证指数日收益率和持有期收益率的风险价值（VaR）。根据GJR模型提供的结果，上证指数30天、60天和90天持有期收益率的风险值分别为12．1％、17．8％、22．0％。用GJR模型比均值-方差模型和历史模拟方法计算的5％显著性水平VaR值更接近实际收益率。