基于有效波动率的市场一般性风险测度研究

鲁棒跳跃的波动率估计是波动率研究的新方向。本文首先采用蒙特卡洛模拟技术检验鲁棒跳跃波动率估计量MedRV的有效性以及预测的准确性,结果表明:MedRV能够有效鲁棒跳跃行为,得到有效波动率(EV)的估计量,同时相对于双幂次变差(BV)有更好的预测准确性。然后基于MedRV估计量构造了市场一般性风险测度,并对中国证券市场一般性风险分布特征进行了研究,结果表明:基于MedRV估计量所得到的MedRV-VaR指标可以有效摒除极端市场风险因子,得到市场一般性风险测度。     

层次贝叶斯网络聚类方法在上市公司信用风险评估中的应用

针对中小企业公司上市过程中存在的信用风险,会对广大投资者产生财产损失的危机,提出应用数据挖掘中的层次贝叶斯方法来对上市公司的信用风险进行综合评价,以准确合理预测公司在财务经营等方面的状况,对投资者具有积极的指导作用。运用层次贝叶斯方法改善了单纯使用贝叶斯方法的局限性,并且在数据有残缺以及事例很少的情况下也能起到预测作用,使上市公司信用风险评估更加可靠。     

基于VAR模型的中国居民消费水平贝叶斯单位根检验

针对经济时序DF单位根检验方法在小样本条件下功效偏低问题,应用贝叶斯统计方法对中国居民消费水平进行单位根检验,提高单位根检验的功效水平,建立向量自回归居民消费水平模型,并通过马尔可夫链蒙特卡罗仿真结合贝叶斯因子分别对农村居民消费和城镇居民消费进行单位根检验,结果表明贝叶斯单位根检验方法解决了向量自回归模型超参数估计的难题,克服了经典单位根检验在经济时序小样本下功效偏低的缺陷,提高了模型预测精度.     

基于混频向量自回归模型的宏观经济预测

本研究利用中国宏观经济指标构建了基于贝叶斯估计的混合频率向量自回归模型(MF-BVAR),并对该模型预测中国宏观经济运行情况的效果进行了检验。本文模型在允许多变量、不同频数据共存的条件下提高了模型估计的自由度,从而实现高精度预测。实证结果显示,在对宏观经济管理部门所关注的核心经济变量CPI、RPI和GDP等进行预测时,MF-BVAR模型相对于目前广泛应用的同频向量自回归模型和MIDAS模型,预测精度都有显著改善。本文亦发现房地产投资对于模型预测能力的重要作用,从样本外预测的角度佐证了房地产部门对于中国宏观经济的重要影响。本文也验证了中国股票市场表现不能对预测宏观经济运行提供额外贡献。     

基于DSGE-VAR方法预测中国宏观经济

基于DSGE-VAR方法对中国主要宏观经济变量进行预测,并将其与古典VAR和贝叶斯VAR预测以及主观性预测加以对比。由RMSE指标可以发现DSGE-VAR在样本外预测方面有较强的竞争力。在短期预测方面,DSGE-VAR不输于主观判断性的专家预测,总体优于DSGE模型;在中长期预测方面,DSGE-VAR模型与DSGE和VAR模型不相上下。     

运用贝叶斯网络量化和控制商业银行操作风险——一个基于实际业务流程的例子

操作风险量化是为降低和控制风险服务的。运用贝叶斯网络分析量化操作风险并改进控制,对提升商业银行的操作风险管理水平是一条有效途径。论文以我国银行的一个典型业务—远期结售汇为例,研究了实践中建立和运用贝叶斯网络来估计操作风险发生频率的具体方法。以流程分析和映射为基础,阐述了风险映射与节点识别、网络结构建立、网络节点描述、节点赋值的实施步骤,并说明了利用贝叶斯网进行因果推理和诊断推理的方法。     

基于贝叶斯网络的上市证券公司风险预警模型研究

上市证券公司的风险预警模型能够为政府监管、证券公司稳健发展以及投资者研判提供依据。以上市证券公司风险管理指标体系为基础,利用贝叶斯网络方法以及支持向量机、随机森林和多项Logit模型分别建立风险预警模型进行比较,并在实证中针对上市证券公司的不平衡数据特征,用 SMOTE抽样对数据进行预处理。最终实证表明：从平均准确率和标准差两个角度比较,SOMTE抽样增加了贝叶斯网络的预测效果,机器学习方法要优于多项Logit模型,贝叶斯网络方法效果最佳。     

基于贝叶斯MCMC方法的我国人口死亡率预测

鉴于我国人口死亡率统计数据质量不高的实际和传统Lee-Carter死亡率预测模型两阶段方法存在的误差累积问题,本文采用贝叶斯Markov Chain Monte Carlo方法来预测我国人口死亡率。通过WinBUGS编程,文章在一体化框架下一次性给出模型的参数估计和未来死亡率的预测值。对研究结果的比较分析表明,贝叶斯方法不仅有效减少了数据质量问题的不利影响,提高了参数估计的稳健性,而且有效克服了参数估计和预测分开进行的弊端,在BIC值和残差项方差等模型选择标准上明显优于传统方法。     

注册制改革与股票市场波动性——来自创业板的证据

本文基于主板及创业板存量股票2018―2022年的数据,利用双重差分模型对创业板注册制改革是否影响其存量股票波动性这一因果关系进行识别,并在运用“反事实估计量”克服平行趋势违背问题的基础上进行估计。“反事实估计量”估计结果显示,创业板注册制改革短期提高了存量股票的波动性,但从长期看取得了降低存量股票波动性的显著成效。机制分析显示,投资者情绪的变动、价格涨跌幅限制的放宽及定价效率的提高是创业板注册制改革影响存量股票波动性的主要途径。异质性分析显示,易成为炒作标的的股票对政策冲击更为敏感,其波动性短期上升的幅度更大,但长期下降的幅度也更明显。     

考虑模型不确定性的中国死亡率预测——基于贝叶斯模型平均方法

作为长寿风险研究领域的基础,死亡率预测近些年获得快速发展,诸多模型的提出使得历史数据的信息得以最大程度的挖掘,但也带来了模型不确定问题。本文对现有的死亡率预测模型进行了分析和整理,提出其中的模型不确定性问题,并针对死亡率预测的模型不确定问题,引入了贝叶斯模型平均方法。该方法以贝叶斯后验概率为权重,综合考虑“一揽子”预测模型的预测能力,并根据它们预测吻合程度进行加权,最终给出死亡率预测结果,结论表明,不但在理论上表现出超过单一模型的优势,也在实践中超过了任何一个单一模型。本文还给出了基于该模型的死亡率预测结果和预期寿命。     

变量序别化在信用评分模型中的应用研究

本文通过将连续数值变量进行序别化转换赋值,并基于这些变量建立Log- it信用评分模型,通过使用统计量AUC值与条件熵比率来检验序别化转换前后所建立回归模型的违约预测力。结果发现,连续数值变量经序别化转换后可提高模型的违约预测力及其韧性。     

European systemic credit risk transmission using Bayesian networks

The analysis of systemic credit risk is one of the most important concerns within the financial system. Its complexity lies in adequately measuring how the transmission of systemic default spreads through assets or financial markets. The transmission structure of systemic credit risk across several European sectoral CDS is studied by dynamic Bayesian networks. The new approach allows for a more advanced analysis of systemic risk transmission, including long-term and more complex relationships. The modelling reveals as relevant only relationships between the original series and one- and two-lagged series. Network structure learning displays a robust and stationary underlying risk transmission structure, pointing to a consolidated transmission mechanism of systemic credit risk between CDSs. Between 5 % and 40 % of sectoral CDS series variances are explained by the network relationships. The modelling allows us to ascertain which relationships between the CDS series show positive (amplifier) and negative (reducer) effects of systemic risk transmission.     

Modelling long-term investment returns via Bayesian infinite mixture time series models

This paper introduces the class of Bayesian infinite mixture time series models first proposed in Lau & So (2004) for modelling long-term investment returns. It is a flexible class of time series models and provides a flexible way to incorporate full information contained in all autoregressive components with various orders by utilizing the idea of Bayesian averaging or mixing. We adopt a Bayesian sampling scheme based on a weighted Chinese restaurant process for generating partitions of investment returns to estimate the Bayesian infinite mixture time series models. Instead of using the point estimates, as in the classical or non-Bayesian approach, the estimation in this paper is performed by the full Bayesian approach, utilizing the idea of Bayesian averaging to incorporate all information contained in the posterior distributions of the random parameters. This provides a natural way to incorporate model risk or uncertainty. The proposed models can also be used to perform clustering of investment returns and detect outliers of returns. We employ the monthly data from the Toronto Stock Exchange 300 (TSE 300) indices to illustrate the implementation of our models and compare the simulated results from the estimated models with the empirical characteristics of the TSE 300 data. We apply the Bayesian predictive distribution of the logarithmic returns obtained by the Bayesian averaging or mixing to evaluate the quantile-based and conditional tail expectation risk measures for segregated fund contracts via stochastic simulation. We compare the risk measures evaluated from our models with those from some well-known and important models in the literature, and highlight some features that can be obtained from our models.     

Bayesian estimation of truncated data with applications to operational risk measurement

Data insufficiency and reporting threshold are two main issues in operational risk modelling. When these conditions are present, maximum likelihood estimation (MLE) may produce very poor parameter estimates. In this study, we first investigate four methods to estimate the parameters of truncated distributions for small samples—MLE, expectation-maximization algorithm, penalized likelihood estimators, and Bayesian methods. Without any proper prior information, Jeffreys’ prior for truncated distributions is used. Based on a simulation study for the log-normal distribution, we find that the Bayesian method gives much more credible and reliable estimates than the MLE method. Finally, an application to the operational loss severity estimation using real data is conducted using the truncated log-normal and log-gamma distributions. With the Bayesian method, the loss distribution parameters and value-at-risk measure for every cell with loss data can be estimated separately for internal and external data. Moreover, confidence intervals for the Bayesian estimates are obtained via a bootstrap method.     

Islamic versus conventional banks in the GCC countries: A comparative study using classification techniques

This paper contributes to the empirical literature on Islamic finance by investigating the feature of Islamic and conventional banks in Gulf Cooperation Council (GCC) countries over the period 2003–2010. We use parametric and non-parametric classification models (Linear discriminant analysis, Logistic regression, Tree of classification and Neural network) to examine whether financial ratios can be used to distinguish between Islamic and conventional banks. Univariate results show that Islamic banks are, on average, more profitable, more liquid, better capitalized, and have lower credit risk than conventional banks. We also find that Islamic banks are, on average, less involved in off-balance sheet activities and have more operating leverage than their conventional peers. Results from classification models show that the two types of banks may be differentiated in terms of credit and insolvency risk, operating leverage and off-balance sheet activities, but not in terms of profitability and liquidity. More interestingly, we find that the recent global financial crisis has a negative impact on the profitability for both Islamic and conventional banks, but time shifted. Finally, results show that Logit regression obtained slightly higher classification accuracies than other models.     

A continuous time Bayesian network classifier for intraday FX prediction

Abstract

Prediction of foreign exchange (FX) rates is addressed as a binary classification problem in which a continuous time Bayesian network classifier (CTBNC) is developed and used to solve it. An exact algorithm for inference on CTBNC is introduced. The performance of an instance of these classifiers is analysed and compared to that of dynamic Bayesian network by using real tick by tick FX rates. Performance analysis and comparison, based on different metrics such as accuracy, precision, recall and Brier score, evince a predictive power of these models for FX rates at high frequencies. The achieved results also show that the proposed CTBNC is more effective and more efficient than dynamic Bayesian network classifier. In particular, it allows to perform high frequency prediction of FX rates in cases where dynamic Bayesian networks-based models are computationally intractable.     

中国商业银行操作风险损失分布甄别与分析：基于贝叶斯MCMC频率方法

确切的操作风险损失分布保障了风险度量的准确性。对银行操作风险损失数据的分析,国外学者一致认为操作风险分布近似泊松分布或负的贝奴里分布。基于中国商业银行1994～2008年的操作风险损失数据,通过对操作风险损失分布的检验、贝叶斯马尔科夫蒙特卡洛频率分析,发现中国商业银行操作风险损失分布近似服从广义极值分布(Generalized Extreme Value)。     

Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach

We use Markov Chain Monte Carlo (MCMC) methods for the parameter estimation and the testing of conditional asset pricing models. In contrast to traditional approaches, it is truly conditional because the assumption that time variation in betas is driven by a set of conditioning variables is not necessary. Moreover, the approach has exact finite sample properties and accounts for errors‐in‐variables. Using S&P 500 panel data, we analyse the empirical performance of the CAPM and the Fama and French (1993) three‐factor model. We find that time‐variation of betas in the CAPM and the time variation of the coefficients for the size factor (SMB) and the distress factor (HML) in the three‐factor model improve the empirical performance. Therefore, our findings are consistent with time variation of firm‐specific exposure to market risk, systematic credit risk and systematic size effects. However, a Bayesian model comparison trading off goodness of fit and model complexity indicates that the conditional CAPM performs best, followed by the conditional three‐factor model, the unconditional CAPM, and the unconditional three‐factor model.     

Interpretable selective learning in credit risk

Forecasting credit default risk has been an important research field for several decades. Traditionally, logistic regression has been widely recognized as a solution because of its accuracy and interpretability. Although complex machine learning models may improve accuracy over simple logistic regressions, their interpretability has prevented their use in credit risk assessment. We introduce a neural network with a selective option to increase interpretability by distinguishing whether linear models can explain the dataset. Our methods are tested on two datasets: 25,000 samples from the Taiwan payment system collected in October 2005 and 250,000 samples from the 2011 Kaggle competition. We find that, for most of samples, logistic regression will be sufficient, with reasonable accuracy; meanwhile, for some specific data portions, a shallow neural network model leads to much better accuracy without significantly sacrificing interpretability.