Finite sample properties of the QMLE for the Log-ACD model: Application to Australian stocks

This paper concerns the properties of the Quasi Maximum Likelihood Estimator (QMLE) of the Logarithmic Autoregressive Conditional Duration (Log-ACD) model. Proofs of consistency and asymptotic normality of QMLE for the Log-ACD model with log-normal density are presented. This is an important issue as the Log-ACD is used widely for testing various market microstructure models and effects. Knowledge of the distribution of the QMLE is crucial for purposes of valid inference and diagnostic checking. The theoretical results developed in the paper are evaluated using Monte Carlo experiments. The experimental results also provide insights into the finite sample properties of the Log-ACD model under different distributional assumptions. Finally, this paper presents two extensions to the Log-ACD model to accommodate asymmetric effects. The usefulness of these novel models will be evaluated empirically using data from Australian stocks.     

随机数在随机事件与概率教学中的应用探索

本文对在随机事件与概率中的常见问题提出了一种新的借助于计算机实现的解决思路,即通过机器自动产生随机数后构建相应的数学模型,再通过编程计算得到最后结果。该方法既为概率论的教学带来了创新性思维,也为当前的教师提出了新的挑战。     

The Candy model: properties and inference

In this paper we study the Candy model, a marked point process introduced by S toica et al. (2000) . We prove Ruelle and local stability, investigate its Markov properties, and discuss how the model may be sampled. Finally, we consider estimation of the model parameters and present a simulation study.     

Finite sample properties for the semiparametric estimation of the intercept of a censored regression model

Financial support for this paper was provided by a C.A. Anderson Fellowship of the Cowles Foundation. I wish to thank Donald Andrews, Moshe Buchinsky, Oliver Linton, and Peter Robinson for helpful discussions. I also wish to thank three anonymous referees for their comments and suggestions. I am, of course, responsible for any remaining errors. A popular two-step estimator of the intercept of a censored regression model is compared with consistent asymptotically normal semiparametric alternatives. Using a root mean squared error criterion, the semiparametric estimators perform better for a range of bandwidth parameter choices for a variety of distributions of the errors and regressors. For error distributions that are close to the normal, however, the two-step parametric estimator performs better.     

Precise computation of a competitive equilibrium of the discrete land market model

This paper provides a finite algorithm to compute a competitive equilibrium of the Alonso-type discrete land market model (known as bid-rent equilibrium) without assuming any condition on the spatial configuration such as monocentricity. The existence of the finite algorithm implies that the discrete land market model is sufficiently tractable for computer simulation analysis (sensitivity analysis) even if the spatial configuration is variable. Specifically, we can evaluate numerically the effects of new routes in a transportation network upon equilibrium rents and commuting mode.     

蒙特卡罗方法在核类本科专业的教学初探

蒙特卡罗方法是以概率统计理论为基础、通过计算机编程而实现的一种数值计算方法,在核科学与技术等领域有着广泛的应用。新时期下,初步尝试面向核类本科专业开设蒙特卡罗方法这门课程,将概率统计思想和编程实践训练融入到蒙特卡罗方法教学中,教学取得了一定成效。     

Forecasting returns in the VIX futures market

This paper introduces a new forecasting model for VIX futures returns. The model is structural in nature and parsimonious, and contains parameters that are relatively easy to estimate. The forecasts of next day VIX futures returns based on this model are superior to those produced by a linear forecasting model that uses the same set of predictors. Moreover, the profits to a market-timing model based on the proposed forecasts are statistically and economically significant, and are robust to both the method used for adjusting for risk and transaction costs (up to around 15 basis points). In contrast, the forecasts generated by the linear forecasting model are not.     

变结构门限t-GARCH模型及其伪持续性研究

为了反映金融时间序列的波动集聚性、非对称性、厚尾性以及在实证研究中表现出的伪持续性,本文结合门限GARCH模型以及变结构的方法提出了变结构门限t—GARCH模型。首先用Monte Carlo模拟的方法考虑了变结构GARCH模型中存在的伪持续性问题;其次针对金融时间序列非对称性、厚尾性以及强持续性的特点提出了变结构门限t—GARCH模型,总结了关于变结构点检验的几个主要方法;最后用该模型来拟合沪市和深市两个股市的周收益率序列,得到了比GARCH模型更好的拟合结果。     

Term structure estimation in the presence of autocorrelation

This paper assesses the effects of autocorrelation on parameter estimates of affine term structure models (ATSM) when principal components analysis is used to extract factors. In contrast to recent studies, we design and run a Monte Carlo experiment that relies on the construction of a simulation design that is consistent with the data, rather than theory or observation, and find that parameter estimation from ATSM is precise in the presence of serial correlation in the measurement error term. Our findings show that parameter estimation of ATSM with principal component based factors is robust to autocorrelation misspecification.     

Beating the market with a bad predictive model

It is a common misconception that in order to make consistent profits as a trader, one needs to possess some extra information leading to an asset value estimation that is more accurate than that reflected by the current market price. While the idea makes intuitive sense and is also well substantiated by the widely popular Kelly criterion, we prove that it is generally possible to make systematic profits with a completely inferior price-predicting model. The key idea is to alter the training objective of the predictive models to explicitly decorrelate them from the market. By doing so, we can exploit inconspicuous biases in the market maker’s pricing, and profit from the inherent advantage of the market taker. We introduce the problem setting throughout the diverse domains of stock trading and sports betting to provide insights into the common underlying properties of profitable predictive models, their connections to standard portfolio optimization strategies, and the commonly overlooked advantage of the market taker. Consequently, we prove the desirability of the decorrelation objective across common market distributions, translate the concept into a practical machine learning setting, and demonstrate its viability with real-world market data.     

基于Monte Carlo方法的光学分子散射研究

综述了分子影像中的各种方法,特别针对光学分子成像技术结合生物组织的混浊介质实例介绍了Monte Carlo方法及其模拟传播流程。目前在非接触式光学断层成像的MC方法研究有生物组织中光传输模型和自由空间光传输模型两种方向,分别介绍了其在本领域的研究现状。由此可知MC方法对活体组织安全快速的无损检测有重要作用。     

Estimation methods for male labor supply functions How to take account of nonlinear taxes

Nonlinear taxes create econometric difficulties when estimating labor supply functions. One estimation method that tackles these problems accounts for the complete form of the budget constraint and uses the maximum likelihood method to estimate parameters. Another method linearizes budget constraints and uses instrumental variables techniques. Using Monte Carlo simulations I investigate the small-sample properties of these estimation methods and how they are affected by measurement errors in independent variables. No estimator is uniquely best. Hence, in actual estimation the choice of estimator should depend on the sample size and type of measurement errors in the data. Complementing actual estimates with a Monte Carlo study of the estimator used, given the type of measurement errors that characterize the data, would often help interpreting the estimates. This paper shows how such a study can be performed.     

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.     

A Portfolio Index GARCH model

This paper develops the structure of a parsimonious Portfolio Index (PI) GARCH model. Unlike the conventional approach to Portfolio Index returns, which employs the univariate ARCH class, the PI-GARCH approach incorporates the effects on individual assets, leading to a better understanding of portfolio risk management, and achieves greater accuracy in forecasting Value-at-Risk (VaR) thresholds. For various asymmetric GARCH models, a Portfolio Index Composite News Impact Surface (PI-CNIS) is developed to measure the effects of news on the conditional variances. The paper also investigates the finite sample properties of the PI-GARCH model. The empirical example shows that the asymmetric PI-GARCH-t model outperforms the GJR-t model and the filtered historical simulation with a t distribution in forecasting VaR thresholds.     

Evolving densities in continuous strategy games through particle simulations

Many cases of strategic interaction between agents involve a continuous set of choices. It is natural to model these problems as continuous space games. Consequently, the population of agents playing the game will be represented with a density function defined over the continuous set of strategy choices. Simulating evolutionary dynamics on continuous strategy spaces is a challenging problem. The classic approach of discretizing the strategy space is ineffective for multidimensional strategy spaces. We present a principled approach to simulation of adaptive dynamics in continuous space games using sequential Monte Carlo methods. Sequential Monte Carlo methods use a set of weighted random samples, also named particles to represent density functions over multidimensional spaces. Sequential Monte Carlo methods provide computationally efficient ways of computing the evolution of probability density functions. We employ resampling and smoothing steps to prevent particle degeneration problem associated with particle estimates. The resulting algorithm can be interpreted as an agent based simulation with elements of natural selection, regression to mean and mutation. We illustrate the performance of the proposed simulation technique using two examples: continuous version of the repeated prisoner dilemma game and evolution of bidding functions in first-price closed-bid auctions.     

Autocorrelation and Bias in Short Time Series: An Alternative Estimator

The conventional first-order autocorrelationcoefficient r₁ generates an empiricalbias when it is applied to short time series.The properties of this estimator have beenexamined with a Monte Carlo simulation studyusing the MATLAB program (version5.2). This study also analyzes the functionof the empirical bias with the polynomicregression and derives a polynomic fittingmodel for different sample sizes. In thisway, a new estimator that has been correctedby the absolute value of the fitting model(r_1') is proposed. Having analyzed thestatistical properties of the estimator r_1',it is shown that the empirical bias generatedby r_1' is less in relationship to r₁ andr₁₊. The results of the study make itpossible to verify that the mean squared errorassociated to the estimator r₁ isless than that of r₁. Thus, the coefficient r_1'is recommended to estimate the lag-oneautocorrelation coefficient in samples under 50observations.     

Testing inter-group ranking heterogeneity: do patient characteristics matter for prioritization of quality improvements in healthcare service?

In many research contexts, such as social science, marketing, education, psychology and medicine, it is frequently of interest to compare two or more groups of subjects (e.g. people of different gender, age or nationality), who are asked to rank a set of alternatives according to their personal liking or opinion, for investigating the presence of group effect. The common investigation aim is to detect customers with homogeneous preferences (or priorities) in order to serve each group as properly as possible. Several approaches have been proposed in the literature for testing ranking heterogeneity among groups of subjects. This paper focuses on an approach considering diversity as a generalization of the notion of variation and investigates the performance of a testing procedure for ranking heterogeneity based on the index of segregation power. The performance of the testing procedure has been investigated via a Monte Carlo simulation study under several scenarios, differing for group size, number of ranked alternatives and system of hypothesis. Furthermore, using a real data set, the testing procedure is exploited for investigating whether patient age and gender matter for patient prioritization of quality improvement in healthcare service.     

低放固体废物处置场项目基本预备费估算研究——基于蒙特卡罗模拟法

目前我国对于核设施退役与放射性废物治理相关项目的需求日益增长,为确定现有规定中的各设计阶段的基本预备费费率是否能满足工程实际需求,本文针对低放固体废物处置场项目使用CRYSTAL BALL软件进行蒙特卡罗模拟,对工程的基本预备费进行计算。本文通过对两种方案分别进行Beta-PERT分布和正态分布模拟,得到不同置信区间下的基本预备费费用和费率并进行验证,得出基本预备费费率基本目前项目的实际情况,验证了蒙特卡罗模拟方法在低放固体废物处置场可行性研究阶段基本预备费的估算具有可行性,有良好的应用和推广价值。     

Prices,debt and market structure in an agent-based model of the financial market

We develop an agent-based model in which heterogeneous and boundedly rational agents interact by trading a risky asset at an endogenously set price. Agents are endowed with balance sheets comprising the risky asset as well as cash on the asset side and equity capital as well as debt on the liabilities side. A number of findings emerge when simulating the model: we find that the empirically observable log-normal distribution of bank balance sheet size naturally emerges and that higher levels of leverage lead to a greater inequality among agents. Furthermore, greater leverage increases the frequency of bankruptcies and systemic events. Credit frictions, which we define as the stickiness of debt adjustments, are able to explain a key difference in the relation between leverage and assets observed for different bank types. Lowering credit frictions leads to an increasingly procyclical behavior of leverage, which is typical for investment banks. Nevertheless, the impact of credit frictions on the fragility of the model financial system is complex. Lower frictions do increase the stability of the system most of the time, while systemic events become more probable. In particular, we observe an increasing frequency of severe liquidity crises that can lead to the collapse of the entire model financial system.