一类金融贝叶斯分位数GARCH模型及其在我国汇率市场中的应用研究

现有GARCH模型依赖于参数条件分布形式假设,依然不能有效刻画金融资产收益偏态厚尾特性,分位数回归能给条件分布提供更加全面的描述.在分位数回归和GJR-GARCH模型基础上建立分位数GJR-GARCH模型,并在贝叶斯框架下对模型进行分析;同时利用中国金融市场数据检验分位数GJR-GARCH模型在风险价值预测方面的实际效果.     

半参数平滑转换分位数自回归模型及应用

已有针对平滑转换自回归模型(STAR)的研究多是将转换函数设定为Logistic函数或指数函数形式,并在均值回归框架下获得模型的估计、检验及预测结果.文章基于重心权有理插值和分位数回归方法,构建一类新的半参数平滑转换分位数自回归模型,其主要特点表现在:第一,基于重心权有理插值方法构造的平滑转换函数,形式更加灵活自由,有效减少了模型误设的风险.第二,在分位数回归框架下,利用遗传算法获得新模型在不同分位点处的平滑转换自回归系数估计,比单纯的均值回归得到的信息更为丰富.数值模拟结果显示,新模型的平滑转换自回归系数估计在无偏性、有效性和一致性方面均具有较好表现.最后,将新模型应用于上证综指日收益率的动态趋势及预测研究,细致揭示了收益率序列在不同阶段、不同分位点处的非线性和异质性变化特征.     

分位数惩罚整合模型及其在商业银行系统性风险中的应用

大数据通常由不同来源的数据组合而成,且具有高维特征,挖掘不同来源数据间的异质性和关联性并降维是亟需解决的问题.基于此,文章提出了分位数惩罚整合模型,并给出其模型表示和模型算法.该模型既可以对不同来源数据进行建模和变量选择,又同时考虑了不同来源数据间的异质性和关联性.数值模拟结果表明:分位数惩罚整合模型在预测性能和变量选择方面都具有明显的优势.此外,将该模型应用于商业银行系统性风险测度中发现,分位数惩罚整合模型在实际应用中也有较好的表现.     

分层次人力资本与全要素生产率基于分位数回归的解析

首先在考虑到人力资本质量的基础上,利用1993-2008的我国30个地区面板数据基于Mamquist指数法测度了我国地区TFP值,然后运用分位数回归技术研究了异质性人力资本对TFP增长不同阶段的作用机制,回归结果表明非熟练劳动只在TFP增长的初级阶段产生正向显著的促进作用,当TFP增长到一定水平(80%分位数以上)抑制了TFP增长.熟练劳动资本从TFP增长的20%分位数处对发达地区产生显著正向拉动作用,而且这种作用一致增强,地区间技术差异从30%分位数处开始一致地对TFP增长产生显著正向推动用.     

比例响应数据的分位数回归建模

由于比例响应数据具有有界性、偏态性和异方差性等特征,基于常规的回归建模方法往往不尽人如意。本文基于分位数方法研究比例响应数据的回归建模及其统计推断问题,重点研究了半连续型比例数据的分位数估计方法,并给出了估计的大样本性质。数值模拟研究和实例分析验证了所提方法的有效性。     

银行业如何影响房地产业?Copula分位数回归及预测方法

众所周知,房地产业与银行业是高度相关的,如何确定银行业股票收益率对房地产业股票收益率的影响以及如何根据银行业股票收益率预测房地产业股票收益率的波动是非常重要的问题。本文首先使用Copula分位数回归建立了银行业股票收益率对房地产业股票收益率的回归模型,并且给出了Copula分位数回归基础上的CopuIa选择新标准,即分位数损失函数距离意义下的Copula函数选择准则,依据该准则我们选取Clayton Copula分位数回归模型刻画了低迷时期银行业股票收益率如何影响房地产业股票收益率,并据此对房地产业股票收益率的波动进行了预测     

基于GARCH族类模型的开放式基金收益率风险分析

基于中国上证50ETF的单位净值,实证分析了国内开放式基金的风险,并在此基础上研究了分位数回归改良情况.选取GARCH族类模型中的GARCH,TARCH和EGARCH,在95%和99%置信水平下计算VaR值,失败率检验结果表明:三种模型在同一置信水平和分布下估计结果差不多,t分布高估风险,而正态分布在99%置信水平下低估风险,GED是最理想的分布.将分位数0.01和0.05回归前后的VaR值作分析比较,发现0.01分位数回归后对风险的估计过于保守不可信,而0.05分位数回归则可以有效改善t分布高估VaR值现象.最后,提出了完善基金市场风险管理的政策建议.     

Box-Cox变换用于分位数回归曲线相交问题的研究

将Box-Cox变换与分位数回归模型相结合(两阶段法),是分位数回归研究领域的一大进步。该法虽然两步都与分位数回归的检验函数紧密结合,但是由于没有利用分位数回归的优良性质,而是引入了中间参变量,因此增加了模型的累进误差,降低了模型精度。更重要的是,两阶段法没有对于分位数回归领域中普遍出现的分位数回归曲线的相交问题给出解决方法。针对这些问题,经研究应该首先确定Box-Cox变换的参数,避免模型中不确定因素的引入,然后对数据进行整体变换并结合分位数检验函数,直接利用分位数回归的优良性质,最终确定分位数回归模型的参数。实例证明,该方法提高了模型的精度,可以有效地解决分位数回归曲线的相交问题。     

最优分位水平及其衍生应用

分位数回归方法由于其具有稳健性,不仅能够全面刻画响应变量的条件分布,还能提供更有现实意义的回归参数,已经逐渐成为各个领域统计分析的强有力的工具.但在许多实际应用中,人们不仅想要探寻不同水平下(即不同分位数)响应变量与解释变量之间的关系,更希望找到一个最优水平,也即最优分位数,使其上的回归结果最真实可靠,最好地反映总体情况.文中提出一种新的回归方法一最优分位回归方法,给出此类问题一个完美的解决方案.该方法的灵感主要来源于稀疏函数的定义,可以证实与传统均值回归相比最优分位回归方法更具优势:(1)稳健性.不受误差分布的限制;(2)有效性.回归结果蕴含信息更丰富;(3)灵活性.对任意模型及数据均适用.文中的模拟结果也对以上三条性质给予极大的支持.最后食品消费数据的分析结果表明当考虑食品消费与人均收入的关系时,中下等收入人群的消费模式为社会的主流模式.     

基于弹性网分位数回归的开放型基金绩效研究

开放型基金是证券投资业务的重要形式之一,其风格识别和绩效评价对投资者来说都有很好的借鉴意义。本文将逐步均值回归,分位数回归(Lasso Quantile Regression)和弹性网分位数回归(Elastic Net Quantile Regression)三种方法对18只基金进行建模,观察三种方法对风险因子的识别作用,结果发现分位数回归能够很好地对基金收益的尾部进行风格识别,弹性网分位数回归要比LASSO分位数回归包容了更多的风险因子。然后在不同的厌恶参数γ^*情况下,用这三种方法对18只基金的进行了绩效评价,为了判断这些绩效评价的效果,本文设置了三种不同的投资组合方案,在不同的投资组合方案下比较三种方法对基金绩效评价的可靠性,结果表明无论在哪种方案下弹性网分位数回归的评价效果要优于另外两种方法,也间接证明了这种方法识别的风险因子是有效的。     

Conditional value at risk and related linear programming models for portfolio optimization

Many risk measures have been recently introduced which (for discrete random variables) result in Linear Programs (LP). While some LP computable risk measures may be viewed as approximations to the variance (e.g., the mean absolute deviation or the Gini’s mean absolute difference), shortfall or quantile risk measures are recently gaining more popularity in various financial applications. In this paper we study LP solvable portfolio optimization models based on extensions of the Conditional Value at Risk (CVaR) measure. The models use multiple CVaR measures thus allowing for more detailed risk aversion modeling. We study both the theoretical properties of the models and their performance on real-life data.     

Value-at-Risk with quantile regression neural network: New evidence from internet finance firms

Traditional risk measurements have proven inadequate in capturing tail risk and nonlinear correlation. This study proposes a novel approach to measure financial risk in the Internet finance industry: a new Value-at-Risk (VaR) measurement based on quantile regression neural network (QRNN). Sparrow Search Algorithm (SSA) is utilized to optimize the QRNN model, which improves the model's performance in predicting internet finance risk. By comparing the TGARCH-VaR and QR-VaR approaches, our study demonstrates the effectiveness of the QRNN-VaR approach and its potential to improve the accuracy of risk prediction in the Internet finance industry. This study further examines and compares the risks between the traditional and internet finance industries. It also considers the unique impact of COVID-19 on industry risk based on statistical testing for differences and machine learning models. Our results indicate that the level of risk in the Internet finance industry is higher than in the traditional finance industry. Moreover, COVID-19 has contributed to increased risk within the Internet finance industry. These findings have significant implications for investors and policymakers seeking to better understand and manage risks within the Internet finance industry, particularly in the ongoing COVID-19 pandemic.     

Adaptive local linear quantile regression

In this paper we propose a new method of local linear adaptive smoothing for nonparametric conditional quantile regression. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on a simulated example and compare it with other methods. The simulation results demonstrate a reasonable performance of our method proposed especially in situations when the underlying image is piecewise linear or can be approximated by such images. Generally speaking, our method outperforms most other existing methods in the sense of the mean square estimation (MSE) and mean absolute estimation (MAE) criteria. The procedure is very stable with respect to increasing noise level and the algorithm can be easily applied to higher dimensional situations.     

CVaR鲁棒均值-CVaR投资组合模型与求解

传统的均值-风险(包括方差、VaR、CVaR等)组合选择模型在计算最优投资组合时,常假定均值是已知的常值,但在实际资产配置中,收益的均值估计会有偏差,即存在着估计风险.在利用CVaR测度估计风险的基础上,研究了CVaR鲁棒均值-CVaR投资组合选择模型,给出了另外两种不同的求解方法,即对偶法和光滑优化方法,并探讨了它们的相关性质及特征,数值实验表明在求解大样本或者大规模投资组合选择问题上,对偶法和光滑优化方法在计算上是可行且有效的.     

On the combination of kernel principal component analysis and neural networks for process indirect control

ABSTRACT

A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.     

小微企业信用风险评估的IDGSO-BP集成模型构建研究

针对传统BP神经网络在小微企业信用风险评估实际应用中,随机初始权值和阈值导致网络学习速度慢、易陷入局部解以及运算结果误差较大等缺陷,借助群智能萤火虫(GSO)算法,提出一种基于改进离散型萤火虫(IDGSO)算法的BP神经网络集成学习算法的小微企业信用风险评估IDGSO-BP模型。该模型以BP神经网络为基本框架,在学习过程中引入离散型萤火虫算法,优化设计神经网络的网络结构与连接权值,得到一组相对合适的权值与阈值,再进行新一轮网络训练,以“均平方误差最小”为评价准则,产生网络的输出结果,以此建立小微企业信用风险评估模型。其仿真实验结果表明,该模型在收敛速度及运算精度方面较传统BP神经网络模型、遗传GA-BP模型及连续GSO-BP模型有较明显优势。因此,IDGSO-BP模型可以有效提高小微企业信用风险评估的准确性。     

Robust asset allocation with conditional value at risk using the forward search

The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.     

Pyramid Quantile Regression

We describe a Bayesian model for simultaneous linear quantile regression at several specified quantile levels. More specifically, we propose to model the conditional distributions by using random probability measures, known as quantile pyramids, introduced by Hjort and Walker. Unlike many existing approaches, this framework allows us to specify meaningful priors on the conditional distributions, while retaining the flexibility afforded by the nonparametric error distribution formulation. Simulation studies demonstrate the flexibility of the proposed approach in estimating diverse scenarios, generally outperforming other competitive methods. We also provide conditions for posterior consistency. The method is particularly promising for modeling the extremal quantiles. Applications to extreme value analysis and in higher dimensions are also explored through data examples. Supplemental material for this article is available online.     

On solving the dual for portfolio selection by optimizing Conditional Value at Risk

This note is focused on computational efficiency of the portfolio selection models based on the Conditional Value at Risk (CVaR) risk measure. The CVaR measure represents the mean shortfall at a specified confidence level and its optimization may be expressed with a Linear Programming (LP) model. The corresponding portfolio selection models can be solved with general purpose LP solvers. However, in the case of more advanced simulation models employed for scenario generation one may get several thousands of scenarios. This may lead to the LP model with huge number of variables and constraints thus decreasing the computational efficiency of the model. To overcome this difficulty some alternative solution approaches are explored employing cutting planes or nondifferential optimization techniques among others. Without questioning importance and quality of the introduced methods we demonstrate much better performances of the simplex method when applied to appropriately rebuilt CVaR models taking advantages of the LP duality.     

Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks

This paper illustrates a dynamic model of conditional value-at-risk (CVaR) measure for risk assessment and mitigation of hazardous material transportation in supply chain networks. The well-established market risk measure, CVaR, which is commonly used by financial institutions for portfolio optimizations, is investigated. In contrast to previous works, we consider CVaR as the main objective in the optimization of hazardous material (hazmat) transportation network. In addition to CVaR minimization and route planning of a supply chain network, the time scheduling of hazmat shipments is imposed and considered in the present study. Pertaining to the general dynamic risk model, we analyzed several scenarios involving a variety of hazmats and time schedules with respect to optimal route selection and CVaR minimization. A solution algorithm is then proposed for solving the model, with verifications made using numerical examples and sensitivity analysis.