基于贝叶斯方法的信用评级模型构建与违约概率估计

以贝叶斯方法为基础构建了信用评级和违约概率模型,指出金融机构利用已有评级信息提高债务人信用风险评估准确性的途径,并以单个债务人违约概率度量方法和Merton理论为基础,考虑异质性导致的宏观经济冲击对债务人的不同影响,度量资产组合违约风险。利用相关数据对贝叶斯模型应用给出例证,结果表明贝叶斯方法具有更为灵活的框架和较好的预测能力。     

流动性和可预测股票回报：一个实证检验

一、引言(一)股票回报可预测自从证券市场诞生以来,预测证券回报一直是人们关注的焦点之一。根据Fama(1970),回报可预测性被分为基于过去回报信息的“弱形式(weak form)”可预测性和基于过去公开信息的“半强形式(semi-strong form)”可预测性。弱形式可预测研究探讨回报的序列依赖性,这种序列依赖性也可捕获期望回报的可预测变化(predictablevariation);半强形式可预测研究使用其它公开可得的滞后变量作为工具(instrument)①。研究者发现的有效预测变量包括短期利率水平、股票市场指数的红利收益、长期和短期国债利差、低等级公司债券(高…     

VaR约束下的资产配置决策模型

在现代投资组合理论(Markowitz,1952)中,投资者通过对各种资产的有效配置以达到风险分散并获取最大的投资回报,并利用不得买空及要求必要回报率的条件下,将投资组合风险最小化,并据此形成有效前缘。为更精确度量风险的回报能力,随后Sharpe(1966,1975)利用均值─方差法(M-V)的框     

基于VaR的商业银行资本优化配置模型研究

银行资本管理是商业银行风险管理的核心,文章运用vaR方法,结合银行资产负债结构,建立了包括银行交易成本、经营策略和风险偏好的银行资本优化配置模型.并时模型进一步分析得出一些技术性结论,银行管理部门在一定的风险期限内设置的置信水平在严格的银行模型假设条件下,风险资产回报的变化受到最佳权益资产的影响,银行资产负债的最佳数量受到资产回报波动性的影响,基于VaK的最佳银行资本要求依赖于银行的资产负债政策和市场因素,为银行资本优化配置管理提供了理论支持和方法指导.     

基于风险偏好系数的半方差房地产投资组合模型

文章基于半方差度量投资风险和投资者的风险偏好行为可变,构建了带有风险偏好系数的丰方差房地产投资组合模型.经实例验证,得出结论:该模型能求出更优的投资组合;投资决策取决于风险和收益之间的对比,即单位风险的回报才是资产选择的决定因素.     

汇率风险溢价与外汇市场干预

一、建立在非抵补利率平价理论上的汇率风险溢价模型当风险厌恶者投资风险资产时,他们会要求一个溢价来补偿不确定的支付。在外汇市场上,投资者要求的溢价就是汇率风险溢价。汇率风险溢价指由于一国预期的汇率贬值或投资者因持有本币资产而非外币资本所要求的额外回报,又被称作     

基于不确定性均值-方差模型的稳健静态资产组合选择

文章借助相对熵测度风险资产收益的一阶矩和前两阶矩的不确定性对资产组合终期财富期望效用的影响,基于极大极小化理论建立了模型参数不确定下的稳健静态资产组合模型,运用稳健控制方法获得了模型的最优解;根据最优解,以上证综指1997年1月至2009年6月的月收益数据构建了两个不同区间段的样本做实证研究。结果表明,参数不确定性导致资产组合中风险资产的比例降低,并随着投资者不确定性偏好程度增加降低得越多;历史数据或信息越少,参数不确定性影响越强;均值不确定性的影响强于方差不确定性的影响;即使投资者完全不相信方差的预测功能,但仍在一定程度上相信均值的预测功能。     

海外资产配置与A股国际化是“双赢”之举吗——基于投资组合均值—方差张成检验的实证分析

资产配置效率是影响投资组合绩效的关键因素,如何衡量资产配置效率对于投资者构建与评价投资组合表现具有重要现实意义。基于改进后的均值—方差张成检验模型,分别研究A股市场投资组合引入国际资产以及国际投资组合引入A股指数后对原有投资组合表现的影响及成因,研究结果表明:在国内A股投资组合中引入发达市场股票指数可以有效控制A股投资组合的尾部风险,而在国际投资组合中引入A股指数则能够提升国际投资组合的夏普比率,并且目前A股指数尚不能够替代国际投资组合中的其他新兴市场指数;国际投资者应当引入A股资产以提升投资组合风险收益比,A股投资者应引入国际资产以有效控制投资组合尾部风险。     

基于VaR风险控制的半log-最优资产组合模型

通过建立基于VaR风险控制下的单周期半log-最优资产组合数学模型,证明了最优解的存在性与唯一性。利用遗传算法对半log-最优资产组合模型进行了实例计算与分析,并与log-最优资产组合模型进行了比较,结果表明半log-最优资产组合模型具有计算方便的特征。     

基于Knockoff的分位数回归变量选择方法及其投资组合决策应用

在数据驱动时代,变量选择广泛应用于投资组合,如何从众多资产中挑选恰当的资产并进行配比,对稳定收益、控制风险非常关键。现有选择资产的方法未考虑到控制错误发现率（FDR）,不利于作出稳健的投资决策。为此,本文在Lasso分位数回归下基于Knockoff方法控制FDR,并用于求解条件风险价值（CVaR）投资组合决策模型。其中,用Lasso惩罚实现变量选择,用Knockoff方法通过模仿解释变量的相关结构构造Knockoff变量,将变量选择的FDR控制在给定水平。模型在两步迭代算法下采用线性规划求解,模拟分析从不同的误差分布、变量分布和维度下多角度展开。结果显示,与已有模型相比,基于Knockoff的Lasso分位数回归模型能良好地控制FDR且呈现出最好的预测效果。最后基于上证50指数成分股进行实证分析,利用滚动建模技术进行投资组合决策分析,发现新模型在收益指标和风险指标上均具有一定优势。     

Cointegration and Long-Run Asset Allocation

We show that economic restrictions of cointegration between asset cash flows and aggregate consumption have important implications for return dynamics and optimal portfolio rules, particularly at long investment horizons. When cash flows and consumption share a common stochastic trend (i.e., are cointegrated), temporary deviations between their levels forecast long-horizon dividend growth rates and returns, and consequently, alter the term profile of risks and expected returns. We show that the optimal asset allocation based on the error-correction vector autoregression (EC-VAR) specification can be quite different relative to a traditional VAR that ignores the cointegrating relation. Unlike the EC-VAR, the commonly used VAR approach to model expected returns focuses on short-run forecasts and can considerably miss on long-horizon return dynamics, and hence, the optimal portfolio mix in the presence of cointegration. We develop and implement methods to account for parameter uncertainty in the EC-VAR setup and highlight the importance of the error-correction channel for optimal portfolio decisions at various investment horizons.     

Evidence for hedge fund predictability from a multivariate Student's t full-factor GARCH model

Extending previous work on hedge fund return predictability, this paper introduces the idea of modelling the conditional distribution of hedge fund returns using Student's t full-factor multivariate GARCH models. This class of models takes into account the stylized facts of hedge fund return series, that is, heteroskedasticity, fat tails and deviations from normality. For the proposed class of multivariate predictive regression models, we derive analytic expressions for the score and the Hessian matrix, which can be used within classical and Bayesian inferential procedures to estimate the model parameters, as well as to compare different predictive regression models. We propose a Bayesian approach to model comparison which provides posterior probabilities for various predictive models that can be used for model averaging. Our empirical application indicates that accounting for fat tails and time-varying covariances/correlations provides a more appropriate modelling approach of the underlying dynamics of financial series and improves our ability to predict hedge fund returns.     

Robust bootstrap densities for dynamic conditional correlations: implications for portfolio selection and Value-at-Risk

ABSTRACT

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions depend on the forecasts covariance matrix little is known about effects of outliers on the uncertainty associated with these forecasts. In this paper we analyse these effects on the context of dynamic conditional correlation models when the uncertainty is measured using bootstrap methods. We also propose a bootstrap procedure to obtain forecast densities for return, volatilities, conditional correlation and Value-at-Risk that is robust to outliers. The results are illustrated with simulated and real data.     

Bayesian estimation of the efficient frontier

In this paper, we consider the estimation of the three determining parameters of the efficient frontier, the expected return, and the variance of the global minimum variance portfolio and the slope parameter, from a Bayesian perspective. Their posterior distribution is derived by assigning the diffuse and the conjugate priors to the mean vector and the covariance matrix of the asset returns and is presented in terms of a stochastic representation. Furthermore, Bayesian estimates together with the standard uncertainties for all three parameters are provided, and their asymptotic distributions are established. All obtained findings are applied to real data, consisting of the returns on assets included into the S&P 500. The empirical properties of the efficient frontier are then examined in detail.     

Linear statistical inference for global and local minimum variance portfolios

Traditional portfolio optimization has often been criticized for not taking estimation risk into account. Estimation risk is mainly driven by the parameter uncertainty regarding the expected asset returns rather than their variances and covariances. The global minimum variance portfolio has been advocated by many authors as an appropriate alternative to the tangential portfolio. This is because there are no expectations which have to be estimated and thus the impact of estimation errors can be substantially reduced. However, in many practical situations an investor is not willing to choose the global minimum variance portfolio but he wants to minimize the variance of the portfolio return under specific constraints for the portfolio weights. Such a portfolio is called local minimum variance portfolio. Small-sample hypothesis tests for global and local minimum variance portfolios are derived and the exact distributions of the estimated portfolio weights are calculated in the present work. The first two moments of the estimator for the expected portfolio returns are also provided and the presented instruments are illustrated by an empirical study.     

Semi-parametric expected shortfall forecasting in financial markets

Bayesian methods have proved effective for quantile estimation, including for financial Value-at-Risk forecasting. Expected shortfall (ES) is a competing tail risk measure, favoured by the Basel Committee, that can be semi-parametrically estimated via asymmetric least squares. An asymmetric Gaussian density is proposed, allowing a likelihood to be developed, that facilitates both pseudo-maximum likelihood and Bayesian semi-parametric estimation, and leads to forecasts of quantiles, expectiles and ES. Further, the conditional autoregressive expectile class of model is generalised to two fully nonlinear families. Adaptive Markov chain Monte Carlo sampling schemes are developed for the Bayesian estimation. The proposed models are favoured in an empirical study forecasting eight financial return series: evidence of more accurate ES forecasting, compared to a range of competing methods, is found, while Bayesian estimated models tend to be more accurate. However, during a financial crisis period most models perform badly, while two existing models perform best.     

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama–French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.     

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama-French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.     

Aggregate Consumption and the Predictability of Asset Returns

This article analyzes the predictability of asset returns that are discounted using a consumption-based discount factor. The main objective of the analysis is to investigate how ancillary statistical assumptions affect the performance of this model. It is shown that, unlike tests of constant-discountrate models, tests of consumption-based models do not critically depend on statistical assumptions; for sufficiently high discount rates, there exist intuitively plausible rates of risk aversion for which appropriately discounted returns are unpredictable, regardless of the statistical specification. Test results are determined by serial correlation properties of prices and dividends and not by serial-correlation properties of returns.     

Mean-variance portfolios using Bayesian vector-autoregressive forcasts

Protfolio optimization is very sensitive to the forecats of returns and (co-)variances of the underlying assets. This paper applies a Bayesian vector-autoregression of the asset universe to predict the returns. Further, the co-variance matrix is forecasted by an Augmented GARCH estimation of the most volatile principle components of the return series. As an empirical illustration, the daily stock returns of the German stocks index DAX have been used to calculate some well-known mean-variance portfolios. Back-testing is used to evaluate the performance. The approach seems to be promising.