基于NS族模型的中国公司债信用利差预测

本文以中国公司债为研究对象, 基于NS族模型研究了信用利差的预测问题。通过对不同期限、不同信用评级公司债信用利差的样本内外预测效果进行实证比较, 得到主要结论如下:(1)模型对中长期公司债信用利差的预测误差低于短期公司债。(2)不同信用评级公司债信用利差的预测效果受剩余到期期限的影响:1年期的AAA级公司债的预测误差低于AA+和AA级公司债; 5年期的AA+级公司债的预测误差低于AAA和AA级公司债; 10年期的AA级公司债的预测误差低于AAA和AA+级公司债。成果为各经济主体预测信用利差提供了具体思路和方法, 有利于做出合理的金融决策。     

信用利差期权定价模型选择研究

遵循衍生品定价的思路,分别采用均值回复过程、平方根扩散过程、带跳的均值回复过程来刻画信用利差的动态变化,进而构建了三类信用利差期权定价模型.分别选取解析解方法、傅里叶变换法和蒙特卡洛模拟法等方法来进行信用利差期权定价仿真,并对不同模型的定价效果进行实证比较分析.结果表明:不同信用利差期权定价模型之间存在着较大差异,其到...     

中美两国公司债信用利差动态过程比较研究

选取中美两国2011年1月至2017年4月的公司债和国债月度交易数据,基于SV模型得到两国公司债的信用利差序列,进而对中美两国公司债的信用利差进行时间序列比较分析.实证发现,中国公司债信用利差序列表现出自回归和移动平均特征,而美国公司债信用利差序列则仅呈现自回归特征;在方差结构方面,中国公司债信用利差序列的残差不具有ARCH效应,而美国公司债信用利差序列的残差具有明显的ARCH效应.同时,对中美两国公司债信用利差建立VAR模型并进行脉冲响应分析,发现中美两国信用利差序列的相关性不强,对彼此的冲击的反应均较弱,为债券市场投资者构建跨国市场债券组合来分散信用风险提供决策支持.     

基于信用利差与Logistic回归的公司违约概率测算模型与实证研究

用Logistic模型计算公司违约概率在实际应用中存在两个问题:一是在缺乏公司违约记录数据库或违约记录数据库不典型的情况下,无法应用该模型或模型计算结果不准确;二是现有Logistic违约概率模型忽视了不同行业财务指标分布特征的差异性,导致公司违约概率计算结果的准确性降低。针对问题一,本文通过公司债券信用利差计算市场隐含的公司违约概率,在Logistic变换的基础上进一步确定Logistic线性回归的参数,使得公司违约概率的计算结果符合债券市场的实际状况。针对问题二,通过不同行业关键财务指标的单因子方差分析,证实了行业间财务指标的分布特征具有显著性差异,通过拟合优度证实了区分行业建立Logistic违约概率模型可显著提高违约概率测算的准确性。本文Logistic违约概率模型的构建过程如下:通过初选财务指标的相关性分析,删除反映信息重复的财务指标;通过Logistic回归中财务指标系数的显著性检验,删除对违约概率解释能力弱的财务指标;以Logistic回归的拟合优度为标准,选取各样本行业Logistic违约概率模型的关键财务指标,建立了机械设备等5个样本行业的Logistic违约概率模型,为样本内行业公司违约概率的准确测算提供模型与方法。本文的创新与特色:一是在无套利条件下,通过公司债券信用利差计算市场隐含的公司违约概率,并对其进行Logistic变换,作为Logistic线性回归的被解释变量,解决了在缺乏公司违约记录数据情况下Logistic违约概率模型的参数估计问题;二是通过单因子方差分析方法,证实了行业间财务指标的分布特征具有显著性差异,说明应区分行业建立Logistic违约概率模型;三是通过财务指标间的相关分析删除反映信息重复的财务指标,通过财务指标系数的显著性检验删除对公司违约概率解释能力弱的财务指标,保证了Logistic违约概率模型中关键财务指标选取的合理性;四是实证研究结果表明,不同行业的Logistic违约概率模型的关键财务指标不同,同一财务指标的参数也存在显著差异。实证研究结果还表明,区分行业建立Logistic违约概率模型与不区分行业相比,前者可将拟合优度及调整后的拟合优度提高近1倍。本文研究结果对于提高公司违约概率测算的准确性具有重要参考意义,对于商业银行贷款定价、公司债券发行定价、银行信用风险管理具有重要参考意义。     

基于联合估计法的债券信用利差期限结构拟合及应用

基于2016~2018年月度数据,通过独立估计的单曲线样条模型和SV 模型、联合估计的多曲线样条模型和SV模型拟合公司债信用利差期限结构,进而对模型拟合效果进行比较,讨论模型在宏观经济预测中的应用,得到以下结论:(1)拟合模型的函数形式是导致理论信用利差期限结构曲线翻折的原因。样条模型和SV模型拟合的信用利差曲线形状完全不同,且模型函数变动引起的误差变动大于曲线变动引起的误差变动。(2)联合估计模型可以修正独立估计模型的人为扭曲形式。多曲线模型的结果更接近实际信用利差,误差波动性明显减小,曲线更为平滑,且联合估计的多曲线样条模型优于独立估计的单曲线样条模型、独立估计的SV 模型和联合估计的SV模型。(3)公司债信用利差期限结构在一定程度上蕴含了市场对未来宏观经济的预期信息,且在短期内预测结果随先行期限延长而改善。因此,宏观经济政策制定者需关注信用利差和期限结构模型拟合研究,重视对信用利差期限结构的深度信息挖掘,从而提高中国宏观政策制定者调控手段的前瞻性和有效性。     

高频视角下中国股市动态VaR预测模型研究

如何充分挖掘交易数据中有价值的信息对金融风险管理极其重要,现有研究中基于低频波动模型的风险测度方法几乎已经做到了极致,而能达到的预测效果却并不稳健,对高频波动模型的研究相对比较匮乏。那么高频模型能否从高频数据中挖掘出更有价值的信息以便用于风险管理之中呢？本研究通过建立12个低频和9个高频波动模型对上证综指进行样本外动态VaR的滚动预测发现,高频模型相对于低频模型具有更好的稳定性,并且在多数情况下高频模型优于低频模型;多头与空头的风险预测效果具有显著差异,多头风险在高风险情况下高频模型表现出色,低风险情况下并不理想,空头风险则在所有情况下都表现较好。     

基于主成分分析的支持向量机模型对上海房价的预测研究

采用基于主成分分析的支持向量机方法对上海房价进行预测.首先利用主成分分析法对原始数据进行降维处理,然后利用具有高水平的小样本学习能力的支持向量机进行预测模型的建立,对上海房价进行预测.实证显示,经过主成分分析的支持向量机模型能够较好地处理复杂的房地产数据,具有较高的预测能力,为上海房地产业的发展提供参考.特别地,该模型可以普遍应用于影响因素众多,时效性较强的短期小样本数据问题的预测,具有较高的泛化能力和很好的预测精度.     

基于量子场论的信用价差期限结构研究

文章采用量子金融的方法,在Heath-Jarrow-Morton模型(以下称HJM模型)基础上引入量子场,对企业债信用价差期限结构进行研究.在量子场论模型中,不同剩余到期时间的信用价差波动率是不完全相关的,而信用价差具有周期性变化特征,在不同周期内的波动率相关性是不同的.因此文章对信用价差进行周期性分析,并对实证数据在...     

投资者注意的风险补偿对股市回报的预测

利用历史累积交易金额数据,本文构造了中国股票市场增量注意风险补偿和存量注意风险补偿,并检验其对中国股票市场收益率的预测能力。样本外检验结果显示,以上两种注意风险补偿均能显著预测下个月中国股市的超额收益率,其R²分别达到了2.68%和2.50%;与中国股票市场中其他预测变量相对比,增量注意和存量注意风险补偿表现出更强的预测能力。此外,基于不同的样本外检验期、不同的风险厌恶参数以及五种不同的变量构造方式,投资者注意风险补偿均产生显著的预测能力。围绕着经济周期波动,本文对注意风险补偿的预测能力进行了解释,同时还发现,相较于经济衰退期间,经济繁荣期间的投资者注意风险补偿样本外预测能力更强。     

基于人工神经网络和随机游走模型的汇率预测

由于金融数据具有随机性特征,使得建模和预测变得极其困难.提出一种组合预测方法,即假定任何金融时序数据由线性和非线性两部分组成,将其中线性部分的数据通过随机游走(RW)模型进行模拟,剩余的非线性残差部分由前馈神经网络(FANN)和诶尔曼神经网络(EANN)协同处理.从实证结果可知,该组合方法相比单独使用RW、FANN或EANN模型有更高的预测精度.     

A Bayesian Approach to Short-term Forecasting

A new approach to short-term forecasting is described, based on Bayesian principles. The performance of conventional systems is often upset by the occurrence of changes in trend and slope, or transients. In this approach events of this nature are modelled explicitly, and successive data points are used to calculate the posterior probabilities of such events at each instant of time.The system produces not only single-figure forecasts but distributions of trend and slope values which are relevant to subsequent decisions based on forecasts.     

Clinical and Operational Risk: A Bayesian Approach

In health care organizations (HCOs) adverse events may provoke dangerous consequences on patients, such as death, a longer hospital stay, and morbidity. As a consequence, HCO’s department needs to manage legal issues and economic reimbursements. Governances and physicians are interested in operational (OR) and clinical risk (CR) assessment, mainly for forecasting and managing losses and for a correct decision making. Currently, scientific researches, which are objected to a quantification of CR and OR in HCO, are scarce; absence of regulatory constraints and limited awareness of benefits due to risk management do not provide incentives to elaborate on how risks can be quantified. This paper is aimed at proposing Bayesian methods to manage operational and clinical adverse events in health care. Bayesian Networks (BNs) are useful for assessing risks given end stage renal disease (ESRD) as a context of application; some prior probability distributions are advised for representing knowledge before experimental results and Bayesian utility functions for making the optimal decision. The method is described as from the theoretical as from the empirical point of view, thanks to the health care and haemodialysis department, for this application. The ultimate goal is to introduce a methodology useful for managing operational and clinical risk for haemodialysis patients and departments.      

Fashion Forecasting for a Mail Order Company Using a Bayesian Approach

Traditional forecasting methods assume a large amount of product history. New product launches take place in the absence of any product sales statistics and initially it is necessary to formulate beliefs as to the product future and then to combine these with sales data as it becomes available. The particular situation considered in this paper concerns a mail order company which sells a different group of ladies dresses on each of its catalogues. The life of a catalogue is of the order of 6 months and material ordering decisions tend to be relevant only at the beginning and during the first few weeks of the catalogue. The task of the Distribution Department is not helped by the presence of a large number of returns from customers. This paper describes a Bayesian approach to the forecasting problem and the "live" performance of the method on one catalogue is discussed.     

Modeling Variability Order: A Semiparametric Bayesian Approach

In comparing two populations, sometimes a model incorporating a certain probability order is desired. In this setting, Bayesian modeling is attractive since a probability order restriction imposed a priori on the population distributions is retained a posteriori. Extending the work in Gelfand and Kottas (2001) for stochastic order specifications, we formulate modeling for distributions ordered in variability. We work with Dirichlet process mixtures resulting in a fully Bayesian semiparametric approach. The details for simulation-based model fitting and prior specification are provided. An example, based on two small subsets of time intervals between eruptions of the Old Faithful geyser, illustrates the methodology.     

Regularizing Portfolio Risk Analysis: A Bayesian Approach

It is important for a portfolio manager to estimate and analyze portfolio volatility, to keep the portfolio’s risk within limit. Though the number of financial instruments in the portfolio can be very large, sometimes more than thousands, daily returns considered for analysis are only for a month or even less. In this case rank of portfolio covariance matrix is less than full, hence solution is not unique. It is typically known as the “ill-posed” problem. In this paper we discuss a Bayesian approach to regularize the problem. One of the additional advantages of this approach is to analyze the source of risk by estimating the probability of positive ‘conditional contribution to total risk’ (CCTR). Each source’s CCTR would sum up to the portfolio’s total volatility risk. Existing methods only estimate CCTR of a source, and does not estimate the probability of CCTR to be significantly greater (or less) than zero. This paper presents Bayesian methodology to do so. We propose a simple Monte Carlo (MC) approach to achieve our objective, which can be paralleled. Estimation of various risk measures, such as Value at Risk and Expected Shortfall, becomes a by-product of this Monte-Carlo approach.     

A Comparison of an Analytical Approach and a Standard Simulation Approach in Bayesian Forecasting Applied to Monthly Data from Insurance of Companies

In this paper, we apply the theory of Bayesian forecasting and dynamic linear models, as presented in West and Harrison (1997), to monthly data from insurance of companies. The total number reported claims of compensation is chosen to be the primary time series of interest. The model is decomposed into a trend block, a seasonal effects block and a regression block with a transformed number of policies as regressor. An essential part of the West and Harrison (1997) approach is to find optimal discount factors for each block and hence avoid the specification of the variance matrices of the error terms in the system equations. The BATS package of Pole et al. (1994) is applied in the analysis. We compare predictions based on this analytical approach with predictions based on a standard simulation approach applying the BUGS package of Spiegelhalter et al. (1995). The motivation for this comparison is to gain knowledge on the quality of predictions based on more or less standard simulation techniques in other applications where an analytical approach is impossible. The predicted values of the two approaches are very similar. The uncertainties, however, in the predictions based on the simulation approach are far larger especially two months or more ahead. This partly indicates the advantages of applying optimal discount factors and partly the disadvantages of at least a standard simulation approach for long term predictions.     

Bond Index Funds — A Duration Approach

Index funds can be used by investment managers as a method of ensuring that their portfolio performs as well as the general market. Methods have been proposed for creating index funds for the stock market, and in this paper a method for creating an index fund for a sector of the bond market is suggested. The underlying indexing mechanism uses the duration moments of the portfolio to capture the various aspects of the sector and its response to changes in the yield curve. The initial results suggest that a relatively small basic set of bonds can be used to model the movements of a particular market segment.     

一个贝叶斯聚类方法及应用

聚类分析是研究对样品或指标进行综合分类的一种多元统计分析方法.聚类结果常表现为树状图的形式.如何合理确定聚类的个数,一直是一个比较困难的问题,至今没有很好的解决方案,尤其当样本量较大时,树状图层次较多,很难直观确定聚类个数.介绍一种基于贝叶斯理论的聚类方法,通过对后验似然最大化的原则确定最佳聚类个数和方案,避免了聚类个数选择的主观性.一个已知分类情况的实际数据验证了该聚类方法的有效性.