CAPM模型在上海股票市场的有效性检验

根据资本资产定价模型(CAPM),从上海A股市场随机抽取100支股票,计算它们的收益率,选择上证综合指数为市场组合的市场指数,并利用双层回归分析方法对2007年1月1日至2011年12月31日这段时间的100支股票进行实证检验。虽然很多国外研究表明,CAPM模型在一定程度上能够解释市场收益,并在资产估价、资本预算、投资风险分析方面已经得到了广泛应用,同时也有利于投资者构建最优的证券投资组合,但本文实证研究结果发现,CAPM模型并不适合中国的股票市场,股票预期收益率和系统风险之间不仅不存在正相关的关系,而且也不存在线性关系,除了系统风险外,非系统风险在解释股票收益上也具有一定的作用。     

Robust tracking error portfolio selection with worst-case downside risk measures

This paper proposes downside risk measure models in portfolio selection that captures uncertainties both in distribution and in parameters. The worst-case distribution with given information on the mean value and the covariance matrix is used, together with ellipsoidal and polytopic uncertainty sets, to build-up this type of downside risk model. As an application of the models, the tracking error portfolio selection problem is considered. By lifting the vector variables to positive semidefinite matrix variables, we obtain semidefinite programming formulations of the robust tracking portfolio models. Numerical results are presented in tracking SSE50 of the Shanghai Stock Exchange. Compared with the tracking error variance portfolio model and the equally weighted strategy, the proposed models are more stable, have better accumulated wealth and have much better Sharpe ratio in the investment period for the majority of observed instances.     

A Conditional Single Index model with Local Covariates for detecting and evaluating active portfolio management

The intercept of standard Single Index and Conditional Single Index models, the so-called alpha, is often used to evaluate the long-run performance of managed portfolios. However, this measure is not always appropriate for detecting the presence and impact of active management strategies. Based on the conditional factor models literature, we introduce a Conditional Single Index model where the time-varying alpha and beta parameters depend only on the past history of the underlying portfolio returns and of the benchmark returns. The dynamics of the parameters have two components: the first describes the long-term behaviour of the alpha and beta, whereas the second is associated with the short-term performance of the underlying portfolio. The interpretation of parameters allows the identification of portfolio managers who implement active management strategies. An application on a set of 1300 U.S. mutual funds shows how widespread active management is on the U.S. market.     

Fractal statistical measure and portfolio model optimization under power-law distribution

An effective portfolio selection model is constructed on the premise of measuring accurately the risk and return on assets. According to the reality that the tail of returns on assets obey power-law distribution, this paper firstly builds two fractal statistical measures, fractal expectation and fractal variance, to measure the asset returns and risks, inspired by the method of measuring curve length in the fractal theory. Then, by incorporating the fractal statistical measure into the return-risk criterion, a portfolio selection model based on fractal statistical measure is established, namely the fractal portfolio selection model, and the closed-form solution of the model is given. Finally, through empirical analysis we find that the fractal portfolio selection model is effective and can improve investment performance.     

股票久期与资产组合的利率风险度量

股票利率风险的定量研究在中国尚不多见。论文采用久期技术探讨了资产组合利率风险测量与管理问题。论文创新之处在于建立了一个基于股权自由现金流的股票久期微观模型,对微观模型和基于消费资产定价的股票宏观久期进行了比较,以这两个模型为依据,计算出我国上证50指数成分股的总体久期值分别为18和25。     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

Predicting the portfolio risk of high-dimensional international stock indices with dynamic spatial dependence

The spatial dependence of assets, which relates to similarities in economic, political, or cultural systems and other aspects, has been confirmed through empirical research; however, spatial dependence has rarely been applied to financial risk measurement. To fill this gap in the literature, a dynamic spatial GARCH-copula (sGC) model is proposed in this paper to evaluate the portfolio risk of international stock indices. In this model, a spatial GARCH is used as the marginal distribution and vine copula is adopted as the joint distribution of indices. Then, the proposed model is applied empirically to assess portfolio risk. Results show that, first, the proposed risk prediction model with spatial dependence outperforms a model neglecting spatial effects per the Kupiec test, Z test and Christoffersen test. Risk prediction during periods of economic stability is also more accurate than during times of crisis. Second, risk measures for models with spatial dependence are higher than those without such dependence but lower than for vine copula models. Third, models including either spatial dependence or vine copulas alone exhibit relatively poor performance. Fourth, the model involving extreme value theory (EVT) generates the greatest value at risk to pass the Kupiec test, Z test and Christoffersen test; however, this model is not suitable for characterizing international indices with EVT based on negative values of the shape parameters of estimates. Findings offer important implications for personal investors, institutional investors, and national regulatory authorities.     

A novel LASSO – TLBO – SVR hybrid model for an efficient portfolio construction

In this study a LASSO – TLBO – SVR hybrid model is used for portfolio construction. Relevant economic parameters are determined and used for stock selection. Along with stock selection, weights for the stocks are obtained by solving a portfolio optimization problem using three methods: GRG Nonlinear, Evolutionary method based on Genetic Algorithm, and Equal weight method. The portfolio return in the proposed model is compared with the return of the Indian market portfolio (NSE and BSE). It is observed that the proposed model outperforms the market portfolio.     

The economic benefits of market timing the style allocation of characteristic-based portfolios

Many exchange traded funds track simple characteristic-based equity portfolios such as the market capitalization, the fundamental value or the inverse volatility portfolio. This paper provides theoretical and empirical evidence for the economic benefits in exploiting the timing-gains that result from the time-varying relative performance of these characteristic-based portfolios. Under a factor model for expected returns, we show that this dynamic portfolio allocation can be efficient across the low-dimensional set of characteristic-based portfolios. We assess the out-of-sample performance on the S&P 100 universe over the period 1990–2013 and show gains in stability and significant positive risk-adjusted returns for the dynamic style portfolio. We conduct several robustness tests and extensions confirming the benefits of dynamic style allocation across characteristic-based portfolios.     

The nexus between exchange rates and stock markets: evidence from the euro-dollar rate and composite European stock indices using rolling analysis

Both the goods market hypothesis and the portfolio balance theory, suggest a nexus between exchange rates and stock prices, albeit with a different direction of causality. This paper, using daily data, takes up the issue of the linkages between stock prices and exchange rates in the case of the euro-dollar rate and two composite European stock market indices: the FTSE Eurotop 300 and FTSE eTX All-Share Index. It addresses the causal ordering issue between the two markets using rolling unit root, cointegration and Granger causality tests. This methodological approach allows for the emergence of a clearer picture of the possible dynamic linkages between exchange rates and stock prices. The empirical results provide evidence of time-varying causality between the two markets.     

Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?

Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.     

A Stein-type shrinkage estimator of the covariance matrix for portfolio selections

The covariance matrix plays a crucial role in portfolio optimization problems as the risk and correlation measure of asset returns. An improved estimation of the covariance matrix can enhance the performance of the portfolio. In this paper, based on the Cholesky decomposition of the covariance matrix, a Stein-type shrinkage strategy for portfolio weights is constructed under the mean-variance framework. Furthermore, according to the agent’s maximum expected utility value, a portfolio selection strategy is proposed. Finally, simulation experiments and an empirical study are used to test the feasibility of the proposed strategy. The numerical results show our portfolio strategy performs satisfactorily.     

基于MCMC算法的时变Copula-GARCH-t模型参数估计及应用

本文假设单变量时序的新息服从标准的学生t分布,提出多元时变Copula-GARCH-t模型,利用蒙特卡洛马尔科夫链(MCMC)算法对模型参数进行贝叶斯统计推断,给出了多个资产组合风险VaR和CVaR的度量方法,并基于风险最小化原则确立了最佳的资产配置模型。实证分析表明,MCMC方法优于经典的IFM方法,能够充分捕捉到中美股市的时变相依结构及相关系数和尾部指数的动态特征。     

Comparing density forecasts in a risk management context

We compare multivariate and univariate approaches to assessing the accuracy of competing density forecasts of a portfolio return in the downside part of the support. We argue that the common practice of performing multivariate forecast comparisons can be problematic in the context of assessing portfolio risk, since better multivariate forecasts do not necessarily correspond to better aggregate portfolio return forecasts. This is illustrated by examples that involve (skew) elliptical distributions and an application to daily returns of a number of US stock prices. In addition, time-varying test statistics and Value-at-Risk forecasts provide empirical evidence of regime changes over the last decades.     

A Portfolio Index GARCH model

This paper develops the structure of a parsimonious Portfolio Index (PI) GARCH model. Unlike the conventional approach to Portfolio Index returns, which employs the univariate ARCH class, the PI-GARCH approach incorporates the effects on individual assets, leading to a better understanding of portfolio risk management, and achieves greater accuracy in forecasting Value-at-Risk (VaR) thresholds. For various asymmetric GARCH models, a Portfolio Index Composite News Impact Surface (PI-CNIS) is developed to measure the effects of news on the conditional variances. The paper also investigates the finite sample properties of the PI-GARCH model. The empirical example shows that the asymmetric PI-GARCH-t model outperforms the GJR-t model and the filtered historical simulation with a t distribution in forecasting VaR thresholds.     

Portfolio management with robustness in both prediction and decision: A mixture model based learning approach

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying distribution can be always well approximated by utilizing a mixture distribution, if we are able to ensure that the component list of a mixture distribution includes all possible distributions corresponding to the scenario analysis of potential market modes. Adopting a mixture distribution enables us to (1) reduce the problem of distribution prediction to a parameter estimation problem in which the mixture weights of a mixture distribution are estimated under a Bayesian learning scheme and the corresponding credible regions of the mixture weights are obtained as well and (2) harmonize information from different channels, such as historical data, market implied information and investors׳ subjective views. We further formulate a robust mean-CVaR portfolio selection problem to deal with the inherent uncertainty in predicting the future return distributions. By employing the duality theory, we show that the robust portfolio selection problem via learning with a mixture model can be reformulated as a linear program or a second-order cone program, which can be effectively solved in polynomial time. We present the results of simulation analyses and primary empirical tests to illustrate a significance of the proposed approach and demonstrate its pros and cons.     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

长记忆条件下中国股市VaR的估计

本文首先对上证综合指数、深圳成份指数、香港恒生指数进行了一个长记忆性检验,在收益波动率序列中我们发现了高度显著的长记忆性。然后我们用GARCH(1,1)、FIGARCH(1,d,1)和FIEGARCH (1,d,1)模型计算各指数在三个置信水平下的VaR值。实证结果表明在估计95％置信度下的VaR值时基于GED分布的FIGARCH(1,d,1)模型表现最佳。