带漂移项的DF检验式中漂移项t统计量的分布特征研究

本文推导了带漂移项的DF检验式中漂移项的t统计量的极限分布,它们是Wiener过程的泛函。并用蒙特卡罗模拟方法给出该统计量的估计分布。该分布是双峰的,分布方差比t分布的大。依据模拟结果,估计出该分布的6个百分位数对样本容量的响应面函数,并给出带漂移项的DF检验式中漂移项是否为零的检验用表。     

Assessing the Behaviour of Non-Survey Methods for Constructing Regional Input–Output Tables through a Monte Carlo Simulation

The paper aims to analyse the behaviour of a battery of non-survey techniques of constructing regional I-O tables in estimating impact. For this aim, a Monte Carlo simulation, based on the generation of ‘true’ multiregional I-O tables, was carried out. By aggregating multi-regional I-O tables, national I-O tables were obtained. From the latter, indirect regional tables were derived through the application of various regionalisation methods and the relevant multipliers were compared with the ‘true’ multipliers using a set of statistics. Three aspects of the behaviour of the methods have been analysed: performances to reproduce ‘true’ multipliers, variability of simulation error and direction of bias. The results have demonstrated that the Flegg et al. Location Quotient (FLQ) and its augmented version (AFLQ) represent an effective improvement of conventional techniques based on the use of location quotients in both reproducing ‘true’ multipliers and generating more stable simulation errors. In addition, the results have confirmed the existence of a tendency of the methods to over/underestimate impact. In the cases of the FLQ and the AFLQ, this tendency depends on the value of the parameter δ.     

基于VaR的投资组合保险策略绩效评价研究

本文用引入VaR作为投资组合保险策略绩效评价指标,分析CPPI策略、TIPP策略、OBPI策略的绩效,并与CM策略和B&H策略进行比较。发现基于VaR的绩效评价与基于SHARP比率的绩效评价相悖。     

Mining big data using parsimonious factor,machine learning,variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of $h=1$, and for four variables when $h=3$ or $12$. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for $h=1$, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when $h>1$. Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.     

金融资产厚尾分布及常用的风险度量

本文首先运用正态分布、带有位置-尺度参数的t分布、logistic分布、极值分布、-stable分布和核密度估计对上证综指收益率分布进行拟合,结果表明核密度估计优于其他分布。其次,在进行尾部风险拟合和度量风险方面,通过设定相关指标,在显著性水平为1%时,-stable分布更适合衡量风险程度,在此基础上提出了调和-stable分布,并得到一个同构表示解。最后,本文给出了蒙特卡洛-stable分布模拟和经验值下的MDD、DaR和CDaR,并得到了模型值和经验值之间的乘离率。     

基于蒙特卡洛方法对风力发电建设项目投资财务风险分析

项目投资的可行性分析和风险管理是项目可行性研究的重要内容。目前风电建设项目常用的经济可行性评价方法主要有确定性指标分析方法和不确定性指标分析方法。其中确定性指标包括净现值、内部收益率、静态投资回收期、投资利润率、资本金净利润率等;不确定性分析方法主要是敏感性分析。蒙特卡洛仿真属于不确定性分析,它建立在概率和随机抽样的基础上。本文将对具体风电建设项目进行经济可行性分析,并采用蒙特卡洛仿真方法对该项目中不确定因素引起的投资风险进行仿真模拟。     

Bayesian median autoregression for robust time series forecasting

We develop a Bayesian median autoregressive (BayesMAR) model for time series forecasting. The proposed method utilizes time-varying quantile regression at the median, favorably inheriting the robustness of median regression in contrast to the widely used mean-based methods. Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure as autoregressive models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting. We estimate model parameters by Markov chain Monte Carlo. Bayesian model averaging is used to account for model uncertainty, including the uncertainty in the autoregressive order, in addition to a Bayesian model selection approach. The proposed methods are illustrated using simulations and real data applications. An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performance compared to the selected mean-based alternatives under various loss functions that encompass both point and probabilistic forecasts. The proposed methods are generic and can be used to complement a rich class of methods that build on autoregressive models.     

Trends and cycles in economic time series: A Bayesian approach

Trends and cyclical components in economic time series are modeled in a Bayesian framework. This enables prior notions about the duration of cycles to be used, while the generalized class of stochastic cycles employed allows the possibility of relatively smooth cycles being extracted. The posterior distributions of such underlying cycles can be very informative for policy makers, particularly with regard to the size and direction of the output gap and potential turning points. From the technical point of view a contribution is made in investigating the most appropriate prior distributions for the parameters in the cyclical components and in developing Markov chain Monte Carlo methods for both univariate and multivariate models. Applications to US macroeconomic series are presented.     

GARCH模型下的美式期权模拟定价——来自中国权证市场的经验

分别运用GBM模型和GARCH模型对我国市场上的股票价格进行参数估计,并使用最小二乘蒙特卡罗模拟方法对我国具有百慕大性质的权证进行蒙特卡罗模拟定价,发现GARCH模型的定价效果明显优于GBM假设下的定价,虚值程度越高的权证,定价误差越大。定价误差的对数与上证综合指数的对数之间存在明显的协整关系,权证没有卖空机制,使得套利无法实现,投机气氛较浓,是我国权证的市场价格明显高估的重要原因。实证对GARCH条件下的定价误差更加具有解释力。     

Is there an asymmetric effect of monetary policy over time? A Bayesian analysis using Austrian data.

The present paper assesses whether monetary policy effects are asymmetric over the business cycle by estimating a univariate model for GDP including additionally the first difference of the 3-month Austrian interest rate as a measure for monetary policy. The asymmetry of the effects is captured by allowing for state-dependent parameters where the latent state variable follows a Markov switching process. The model is estimated within a Bayesian framework using Markov Chain Monte Carlo simulation methods. Model selection and specification tests are performed by means of marginal likelihood. The results document significant negative effects of monetary policy during periods of below-average growth, while the effect seems insignificant during periods of normal or above-average growth. These results corroborate those derived in theoretical models assuming price rigidities and implying a convex supply curve. Additionally, the concern of using appropriate state-identifying restrictions is raised to obtain an unbiased posterior inference. Finally, the analysis concludes by assessing the robustness of the results with respect to alternative measures of monetary policy. First Version Received: December 2000/Final Version Received: May 2001