Uniform Ergodicity of the Particle Gibbs Sampler

The particle Gibbs sampler is a systematic way of using a particle filter within Markov chain Monte Carlo. This results in an off‐the‐shelf Markov kernel on the space of state trajectories, which can be used to simulate from the full joint smoothing distribution for a state space model in a Markov chain Monte Carlo scheme. We show that the particle Gibbs Markov kernel is uniformly ergodic under rather general assumptions, which we will carefully review and discuss. In particular, we provide an explicit rate of convergence, which reveals that (i) for fixed number of data points, the convergence rate can be made arbitrarily good by increasing the number of particles and (ii) under general mixing assumptions, the convergence rate can be kept constant by increasing the number of particles superlinearly with the number of observations. We illustrate the applicability of our result by studying in detail a common stochastic volatility model with a non‐compact state space.     

基于中国家庭动态跟踪调查的广东省居民消费平滑研究

文章运用2008年和2009年广东省659户的住户调查数据对广东省居民的消费平滑程度进行考察,研究发现:两种效用函数形式下的全消费保险模型假设均不能拒绝,广东省居民特异性消费风险完全被分散,居民消费的改变与村庄平均消费的变动一一对应,居民收入的提高对居民消费无显著影响.考虑金融发展变量的消费保险模型估计结果表明,金融发展对广东省居民的风险分担有显著作用,且金融发展水平的提高将削弱居民收入对居民消费的影响.     

中国A、B、H股市间尾部相依性的趋势研究——基于多机制平滑转换混合Copula模型的实证分析

尾部相依性与金融市场间的风险紧密相联系,鉴于传统研究方法存在低估或高估市场间相依性的可能,提出了多机制平滑转换混合Copula模型,从极值风险视角出发考察了中国A、B、H股票市场间尾部相依性的长期变化趋势,发现不同股市的尾部相依性呈现不同运动趋势,而且左右尾部相依性存在明显的非对称性和结构性变化,其非对称程度、相依性强度以及结构变化的时间、位置和速度都存在一定差异.几次重大事件如1997年亚洲金融危机、2001年B股对境内开放、2002年引入QFII、2005年的股权分置改革、2006年引入QDII以及2007年的次贷危机对股市间尾部相依性产生的影响程度是不一样的.     

中国利率期限结构平滑样条拟合改进研究

在FNZ模型和Waggoner模型的基础上,结合中国国债市场的发展现状,从拟合对象、粗糙度惩罚项的函数形式、参数估计方法、最优化目标函数形式以及估计样本等五方面进行修正和改进,提出利用可变粗糙度惩罚项三次平滑样条改进模型来拟合中国的利率期限结构,并利用上交所国债市场2002年1月1日至2003年12月31日的国债收盘价格数据对该模型进行实证研究.结果发现,笔者提出的改进模型能够较合理、有效地估计较为完整的中国静态利率期限结构.     

Short-term forecasting with a computationally efficient nonparametric transfer function model

In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.     

Robust optimal estimation of location from discretely sampled functional data

Estimating location is a central problem in functional data analysis, yet most current estimation procedures either unrealistically assume completely observed trajectories or lack robustness with respect to the many kinds of anomalies one can encounter in the functional setting. To remedy these deficiencies we introduce the first class of optimal robust location estimators based on discretely sampled functional data. The proposed method is based on M-type smoothing spline estimation with repeated measurements and is suitable for both commonly and independently observed trajectories that are subject to measurement error. We show that under suitable assumptions the proposed family of estimators is minimax rate optimal both for commonly and independently observed trajectories and we illustrate its highly competitive performance and practical usefulness in a Monte-Carlo study and a real-data example involving recent Covid-19 data.     

稳定型机构投资者、盈余平滑与股价同步性

稳定型机构投资者秉承长期投资和价值投资理念,在提高上市公司信息披露质量、增强资本市场韧性等方面能够发挥重要作用。基于2010—2021年中国沪深A股上市公司数据,以盈余平滑为会计信息质量的衡量维度,考察了稳定型机构投资者对不同属性盈余平滑的影响,并探讨了其在降低股价同步性方面的治理作用。研究发现:稳定型机构投资者可以促进企业的信息属性盈余平滑,抑制机会主义属性盈余平滑；稳定型机构投资者通过促进信息属性盈余平滑降低了资本市场的股价同步性；异质性分析表明,当企业内部控制较差、市场竞争程度较高时,稳定型机构投资者对信息属性盈余平滑的促进作用更大。因此,要加快建设机构投资者体系,发挥稳定型机构投资者提高企业会计信息质量的积极作用,以提升资本市场资源配置效率,推动资本市场高质量发展。     

盈余信息与股价崩盘风险——基于盈余平滑的分解检验

以我国A股上市公司2003年～2015年的数据作为研究样本,研究了不同类型盈余平滑对股价崩盘风险的影响.实证结果表明盈余平滑对股价崩盘风险没有显著影响,但将盈余平滑分解成有效信息盈余平滑和机会主义盈余平滑之后,发现有效信息盈余平滑能够显著地降低股价崩盘风险,而机会主义盈余平滑则会显著增加股价崩盘风险.进一步检验发现,信息环境对盈余平滑与股价崩盘风险的关系存在调节作用,在信息环境较差的公司中,有效信息(机会主义)盈余平滑会更显著地降低(增加)股价崩盘风险.这些研究结论不但丰富了股价崩盘风险的影响因素研究,而且有助于深入了解盈余平滑在资本市场中扮演的角色,对降低股价崩盘风险、稳定我国资本市场具有重要的意义.