含景气参数的非负lasso改进算法及其在指数跟踪中的应用

以lasso算法为模型基础,在中国市场不允许做空的条件下,改进非负lasso算法,将市场景气因素应用到模型中,得到含景气参数的超额收益与跟踪误差的平衡模型。以上证180指数为投资标的,模拟熊市和牛市投资状态下的指数跟踪。在牛市时,设定景气参数为0,最小化跟踪误差以获取市场平均收益;熊市时,跟踪组合对超额收益的获取表现出了明显的优势,同时在熊市获取超额收益需要承担更大风险。用含景气参数的非负lasso算法跟踪指数,为业界提供了一种新的指数跟踪方法。     

模糊随机环境下考虑交易费用的投资组合模型

文章对投资市场的随机性与模糊性两种不确定性因素共存的投资组合问题进行了研究,提出了一种新的模型和求解方法.模型用均值表示收益,用绝对偏差表示风险,并考虑了交易费用的因素,给出了将模糊随机空间下的投资组合模型等价地转化为概率空间下的非线性规划模型的方法,并利用差分进化算法来求解该模型.最后通过一个例子来说明所提出方法的可行性.     

基于Copula-SV模型的投资组合风险分析

文章将Copula函数和SV模型相结合,建立了投资组合风险分析的Copula-SV模型,对我国股票市场实际的组合投资问题进行了实证风险分析;并与Copula-GARCH模型下的投资组合风险值进行比较,取得了满意的结果。     

基于深度学习的收益率预测与投资组合模型

在传统投资组合模型中,备选资产的预期超额收益率的预测误差将导致模型的投资绩效劣化。文章采用Transformer深度学习模型对备选资产的收益率进行预测,以提升预期收益率的准确度,进而提高投资组合模型的绩效。以中证800指数的成分股为备选资产,进行了72期投资,将实证结果与LSTM和SVR模型进行对比,验证了Transformer模型在提升预测准确度和提高投资组合模型绩效上的优越性。     

基于原对偶内点算法求解投资组合优化模型

研究了标准均值方差投资组合选择模型,针对目前求解方法不具有多项式算法复杂性,文章给出了求解均值方差投资组合优化模型的原对偶内点算法.该算法具有多项式复杂性,因此可以快速求解大规模的投资组合优化模型.仿真结果表明,原对偶内点算法可以较好地应用于投资组合问题,具有较广泛的应用空间和一定的推广价值.     

单指数模型的稳健回归及实证

文章针对投资组合理论中经典的夏普单指数投资组合模型,引入了稳健统计的思想,将稳健回归方法应用到该投资组合模型,降低了证券市场中证券收益率历史数据中因短期内重大利好或利空导致的超高或超低收益率离群值对投资组合决策的影响,并结合我国证券市场的特点,对沪市A股市场进行了实证分析,得到了证券投资组合的有效前沿.     

基于AR模型的Kalman滤波在股票价格预测中的应用

文章在分析AR(n)模型和Kalman滤波模型具有的预测功能的基础上,将二者结合起来而提出一种基于AR模型的卡尔曼滤波模型.该模型用1至n阶的AR模型组合建立新的多维状态空间模型,再应用Kal-man滤波方法预测股票价格.通过对股票价格预测的具体实验表明,提出的新模型克服了单一方法使用的缺点,具有较高的预测精度.     

基于分目标乘除法的双目标投资组合模型

基于马克维茨投资组合的均值-方差模型,文章构建一个投资组合预期收益率在一定范围的双目标混合投资组合摸型,并应用分目标乘除法来求解该模型。最后,给出一实际算例,对一具体投资组合模型进行分析,结果表明：所构建的模型和所采用的方法是可行的、有效的;同时,也得到了该模型的有效边界。     

行业投资中的战略风险预算方法研究

本文将风险预算技术应用于机构投资者的行业投资战略风险管理中。为了准确地界定战略风险预算中的风险源,在国内基准证券组合比较少,不可能把战略风险源用可投资的战略基准证券组合进行刻画的情况下,引入多因素模型来刻画行业收益的风险源,建立了基于多因素模型的行业投资战略风险预算模型,并结合三因素模型进行了实证分析。在对行业战略风险进行预算的基础上配置风险,找到了一种能更好地控制行业投资战略风险的方法。     

基于卡尔曼滤波的投资组合时变风险估计

本文采用状态空间表示式,提出了一个具有时变的系统风险系数β的条件CAPM,然后利用卡尔曼滤波递归算法来估计时变β系数,最后通过夏普的对角线模型计算投资组合的VaR并进行返回检验.结果表明,该模型能够捕捉到金融市场的波动,而且对计算起到简化作用,特别适合对大投资组合的VaR估计.     

Bayesian model-averaged regularization for Gaussian graphical models

The graphical lasso has now become a useful tool to estimate high-dimensional Gaussian graphical models, but its practical applications suffer from the problem of choosing regularization parameters in a data-dependent way. In this article, we propose a model-averaged method for estimating sparse inverse covariance matrices for Gaussian graphical models. We consider the graphical lasso regularization path as the model space for Bayesian model averaging and use Markov chain Monte Carlo techniques for the regularization path point selection. Numerical performance of our method is investigated using both simulated and real datasets, in comparison with some state-of-art model selection procedures.     

Stratified Gaussian graphical models

Gaussian graphical models represent the backbone of the statistical toolbox for analyzing continuous multivariate systems. However, due to the intrinsic properties of the multivariate normal distribution, use of this model family may hide certain forms of context-specific independence that are natural to consider from an applied perspective. Such independencies have been earlier introduced to generalize discrete graphical models and Bayesian networks into more flexible model families. Here, we adapt the idea of context-specific independence to Gaussian graphical models by introducing a stratification of the Euclidean space such that a conditional independence may hold in certain segments but be absent elsewhere. It is shown that the stratified models define a curved exponential family, which retains considerable tractability for parameter estimation and model selection.     

Labelled Graphical Models

A class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space. Theoretical results concerning model identifiability, decomposability and estimation are derived. A decision theoretical framework and a search algorithm for the identification of plausible models are described. Real data sets are used to illustrate that LGMs may provide a simpler interpretation of a dependence structure than GMs.     

Graphical Network Models for International Financial Flows

The late-2000s financial crisis stressed the need to understand the world financial system as a network of countries, where cross-border financial linkages play a fundamental role in the spread of systemic risks. Financial network models, which take into account the complex interrelationships between countries, seem to be an appropriate tool in this context. To improve the statistical performance of financial network models, we propose to generate them by means of multivariate graphical models. We then introduce Bayesian graphical models, which can take model uncertainty into account, and dynamic Bayesian graphical models, which provide a convenient framework to model temporal cross-border data, decomposing the model into autoregressive and contemporaneous networks. The article shows how the application of the proposed models to the Bank of International Settlements locational banking statistics allows the identification of four distinct groups of countries, that can be considered central in systemic risk contagion.     

基于图方法的MA和ARMA模型系数检验

应用图模型方法来讨论传统的MA和ARMA模型,证明了MA和ARMA模型的系数为去掉其他时间序列分量线性效应的条件下的偏相关系数,且利用图模型推断算法提出了一种新的参数估计和检验方法。     

Fraction of design space plots for generalized linear models

The use of graphical methods for comparing the quality of prediction throughout the design space of an experiment has been explored extensively for responses modeled with standard linear models. In this paper, fraction of design space (FDS) plots are adapted to evaluate designs for generalized linear models (GLMs). Since the quality of designs for GLMs depends on the model parameters, initial parameter estimates need to be provided by the experimenter. Consequently, an important question to consider is the design's robustness to user misspecification of the initial parameter estimates. FDS plots provide a graphical way of assessing the relative merits of different designs under a variety of types of parameter misspecification. Examples using logistic and Poisson regression models with their canonical links are used to demonstrate the benefits of the FDS plots.     

Estimating finite mixture of continuous trees using penalized mutual information

Abstract

In this paper we introduce continuous tree mixture model that is the mixture of undirected graphical models with tree structured graphs and is considered as multivariate analysis with a non parametric approach. We estimate its parameters, the component edge sets and mixture proportions through regularized maximum likalihood procedure. Our new algorithm, which uses expectation maximization algorithm and the modified version of Kruskal algorithm, simultaneosly estimates and prunes the mixture component trees. Simulation studies indicate this method performs better than the alternative Gaussian graphical mixture model. The proposed method is also applied to water-level data set and is compared with the results of Gaussian mixture model.     

Reml and best linear unbiased prediction in state space models

This article takes a hierarchical model approach to the estimation of state space models with diffuse initial conditions. An initial state is said to be diffuse when it cannot be assigned a proper prior distribution. In state space models this occurs either when fixed effects are present or when modelling nonstationarity in the state transition equation. Whereas much of the literature views diffuse states as an initialization problem, we follow the approach of Sallas and Harville (1981,1988) and incorporate diffuse initial conditions via noninformative prior distributions into hierarchical linear models. We apply existing results to derive the restricted loglike-lihood and appropriate modifications to the standard Kalman filter and smoother. Our approach results in a better understanding of De Jong's (1991) contributions. This article also shows how to adjust the standard Kalman filter, the fixed inter- val smoother and the state space model forecasting recursions, together with their mean square errors, for he presence of diffuse components. Using a hierarchical model approach it is shown that the estimates obtained are Best Linear Unbiased Predictors (BLUP).     

Objective Bayes Covariate‐Adjusted Sparse Graphical Model Selection

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models having a sparse regression and covariance structure, the latter being Markov with respect to a directed acyclic graph (DAG). Our procedure can be easily complemented with a variable selection step, so that variable and graphical model selection can be performed jointly. In this way, we offer a solution to a problem of growing importance especially in the area of genetical genomics (eQTL analysis). The input of our method is a single default prior, essentially involving no subjective elicitation, while its output is a closed form marginal likelihood for every covariate‐adjusted DAG model, which is constant over each class of Markov equivalent DAGs; our procedure thus naturally encompasses covariate‐adjusted decomposable graphical models. In realistic experimental studies, our method is highly competitive, especially when the number of responses is large relative to the sample size.