Asset pricing with time-varying betas for stocks traded on S&P 500

This study uses a novel approach for capturing time variation in betas whose pattern is treated as a function of market returns. A two-factor model (TFM) is constructed using estimated coefficients of a nonlinear regression. The model is tested against the CAPM and the Fama and French three-factor model in the context of time series regressions. The used stocks are traded on S&P 500. The period spans from 1993 to 2011. The time series regression results depict the superiority of the TFM in explaining portfolio returns including momentum ones. We also provide evidence that the particular portfolios employed at the construction of the new model accommodate different fundamental characteristics and different risk levels.     

Dynamic multiproduct optimal hedging in the soybean complex – do time-varying correlations provide hedging improvements?

Optimal multiproduct time-varying hedge ratios are determined – for a soybean complex – and their risk-mitigating impact is contrasted over single-commodity time-varying and naive hedge ratios. A parsimonious regime-switching dynamic correlation model is employed, with the estimated dynamic correlation matrix among prices varying between two different levels, and the time-varying correlations being applied to the multiproduct setting. Findings obtained are three-fold. First, there is significant evidence that estimated simultaneous correlations among different commodities’ prices (e.g. soybean spot and soybean meal futures) attain different values along the time series. Second, there is a substantial reduction in margin variance provided by the optimal multiproduct time-varying hedge ratios over single time-varying and naive hedge ratios, for both in- and out-of-sample data. Third, average optimal multiproduct time-varying hedge ratios for soybean and soybean meal (0.82 and 0.74, respectively; for out-of-sample data) are significantly below the naive full hedge ratio, providing risk mitigation at lower costs.     

时变环境下选址问题的一个启发式算法

配送中心选址作为物流管理中的一个重要研究问题受到广泛重视。然而,文献中所涉及的选址模型大都是静态的,即沿一条路径行进时所需的成本(或时间)是与出发时间无关的函数。但在实际生活中,它往往是会随时间的变化而变化的,这类问题被称为时变环境下的选址问题。文中讨论了时变环境下的单配送中心到多个零售点带容量约束的选址问题。由于该问题是NP-完备的,我们给出了一个启发式算法。     

一种指数形式时变加权的机载双基雷达STAP算法

针对机载双基雷达杂波距离向分布非平稳,统计型空时自适应处理技术杂波抑制性能急剧下降问题,提出一种基于指数形式时变加权算法。该方法引入距离的非线性函数表示杂波的变化特性,通过对样本数据进行指数形式扩展,实现杂波非平稳分布的补偿。理论分析和仿真实验表明,所提方法能有效抑制来自不同距离的双基杂波,在急剧变化的杂波环境下性能优于时变加权法和局域处理法。     

多变时滞中立型系统稳定性研究

本文研究了一类多时变时滞中立型系统的渐近稳定性问题。基于Lyapunov第二方法,推导出新的时滞依赖稳定性判据。所给得判据可以保证多变时滞中立型系统渐进稳定,结论以线性矩阵不等式(LMI)形式表示,便于应用。仿真结果表明本文稳定性判定准则具有较小的保守性。     

An analysis of dynamic risk in the Greater China equity markets

This study looks at the time-varying nature of systematic risk in the Greater China equity markets. The Shanghai and Shenzhen markets both have a low average systematic risk when measured against the world market. The short outbursts in systematic risk for these two markets seem to be directly related to policy shifts. The Hong Kong and Taiwan markets are more integrated with world markets and they show signs of large variations in systematic risk over time. Furthermore, conditional betas in the Shanghai and Shenzhen markets are stationary, while the Hong Kong and Taiwan betas are integrated of order one. In addition, long memory tests show that all four markets exhibit a long-run dependence in their conditional betas. While the two mainland China market betas are covariance stationary, the Hong Kong and Taiwan betas are not.     

我国国际储备资产的最优结构研究——基于时变Copula及VaR的投资组合模型分析

文章通过构建效用评估函数,使用时变相关T-Copula模型、Monte Carlo模拟和VaR计算方法系统研究了我国国际储备的最优结构。结果发现,我国黄金的最优占比应至少为23%。据此,文章对多个国家储备资产的变化情况进行了动态和静态比较,发现不同类别的演化路径,而以我国和其他金砖四国为代表的类别处于收益递减、风险增大的状态中,亟须加大黄金储备至优化区间。     

The dynamic causality between commodity prices,inflation and output in China: a bootstrap rolling window approach

In this article, we empirically study the time-varying bilateral causality between commodity prices, inflation and output in China. We first perform a series of parameter stability tests and find strong evidence of instability in the parameters estimated for Granger causality tests. We then use the bootstrap rolling window approach to test the causality and find that the causality from commodity prices to both inflation and output is time-varying in the entire sample period and asymmetric in different phases of the business cycle. We also find evidence of the causality from both inflation and output to commodity prices in certain sub-periods. Further discussion on the cost-price mechanism through which the economy fluctuates cyclically suggests that the dynamic causality between commodity prices and inflation contributes to understanding the nature of economic fluctuations and to forecasting economic crises. Overall, our results provide a new perspective to disentangle economic fluctuations.     

Dynamic conditional score models of degrees of freedom: filtering with score-driven heavy tails

This article extends the quasi-autoregressive (QAR) plus Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) dynamic conditional score (DCS) model. For the new DCS model, the degrees of freedom parameter is time varying and tail thickness of the error term is updated by the conditional score. We compare the performance of QAR plus Beta-t-EGARCH with constant degrees of freedom (benchmark model) and QAR plus Beta-t-EGARCH with time-varying degrees of freedom (extended model). We use data from the Standard and Poor’s 500 (S&P 500) index, and a random sample of its 150 components that are from different industries of the United States (US) economy. For the S&P 500, all likelihood-based model selection criteria support the extended model, which identifies extreme events with significant impact on the US stock market. We find that for 59% of the 150 firms, the extended model has a superior statistical performance. The results suggest that the extended model is superior for those industries, which produce products that people usually are unwilling to cut out of their budgets, regardless of their financial situation. We perform an application to compare the density forecast performance of both DCS models. We perform an application to Monte Carlo value-at-risk for both DCS models.     

GMM estimation of panel data models with time-varying slope coefficients

A difference/system generalized method of moments (GMM) model that imposes time-constant coefficients is common in empirical studies using panel data. However, a rejection by the Sargan–Hansen test is sometimes a serious concern for researchers. We highlight the fact that the Sargan–Hansen test for GMM estimators applied to panel data is a joint test of valid orthogonality conditions and coefficient stability over time. A possible reason for a rejection is therefore that the slope coefficients vary over time. One solution is to estimate an empirical model in which the coefficients are time specific. We apply this solution to the system GMM estimator of simple nondynamic Cobb–Douglas production functions for a selection of Swedish industries and find that relaxing the assumption of constant slope coefficients results in more satisfactory outcomes of the Sargan–Hansen test.