Contemporary and long-run correlations: A covariance component model and studies on the S&P 500 cash and futures markets

In this article, a multivariate component model for conditional asset return covariance is developed as an extension to the univariate volatility component model of Engle & Lee (1999). The conditional covariance now is decomposed into a long-run (trend) component and a short-run (transitory) component. Through the decomposition, relationships like the long-run correlation and volatility copersistence can be studied solely upon examining the long-run trend of the conditional covariance. The decomposition also has important implications in studying portfolio hedging problems such as the multi-period minimum-variance hedging for long-term portfolio management. The empirical study in this article focuses on estimating the covariance component structure between the S&P 500 cash and futures markets and their contemporary and long-run correlation relationship and the volatility copersistence relationship. © John Wiley & Sons, Inc. Jrl Fut Mark 19: 877–894, 1999     

“五星酒店如雨后春笋 有压力更有动力”——访重庆洲际酒店总经理范纳伦先生

重庆洲际酒店总经理:Franck Naulleau,法国人,中文名:范纳伦。出生于1966年。他拥有超过20年的酒店管理经验,足迹遍及亚洲、非洲、欧洲、美洲和中东,丰富的阅历让他在工作中拥有独到的见解和创造力。2008年9月,范纳伦先生来到中国重庆,任重庆洲际酒店总经理。他有广泛的爱好,特别在运动方面。在闲暇时间,他喜欢参加网球、高尔夫、足球等运动,并都是高手。此外,他还喜欢研究国际文学和电影。范纳伦先生更是热心慈善的爱心人士。在他去过的诸多国家,都留下了他乐善好施的义举。范纳伦先生相信,爱心没有国界,每个人的一点慈善之举,都终能汇成强大的力量,将幸福和快乐带给需要帮助的人。     

Hypothesis testing based on goodness-of-fit in the moving average time series model

Small sample properties of t-tests are compared with those of tests based on relative goodness- of-fit in the context of the first order moving average time series model. Monte Carlo experiments reported in the paper suggest that the actual size of these t-tests greatly exceeds theoretical large sample significance levels, while conformity of goodness-of-fit statistics to the appropriate chi-square or F-distributions is much closer. The evidence presented suggests that practitioners are well advised to employ goodness-of-fit tests as a check on results of t-tests particularly when the latter indicate ‘significance’.     

Asymmetric Information and the Predictability of Real Estate Returns

This article examines the relation between systematic price changes and the heterogeneity of investors information sets in real estate asset markets. The empirical implications rely on a theoretical economy in which information asymmetry alters the dynamic relation between returns and trading volume. We employ a filter-rule methodology to determine predictability in returns and augment the return-based conditioning set with trading volume. The additional conditioning information is necessary since the model is underspecified when predictability is based on returns alone. Our results provide new insight into the coexistence of informational and noninformational exchange in the speculative markets for real estate assets. Specifically, we find that the predictability of real estate returns is generally more indicative of portfolio rebalancing effects than an adverse-selection problem. These results are unique in addressing the time-variation in information asymmetry.