结构性产品以其设计的灵活性、降低融资成本和风险管理成本以及丰富投资品种等优势，一经产生便获得迅猛发展，很快成长为全球固定收益市场中重要的组成部分。然而2007年次贷危机爆发，结构性产品成为某些分析者眼中的替罪羊，为结构性产品进行信用评级的评级机构也随之声名狼藉，成为众矢之的。首先简要介绍结构性产品的评级过程和三大评级机构（穆迪、标准普尔和惠誉）的评级方法，然后分析评级中可能存在的模型风险、评级套利(rating model arbitrage, 又称为rating shopping)、内在利益冲突(inherent conflicts of interest)以及其他衍生的问题，最后探讨我国改进结构性产品信用评级质量的方向。

This article provides a survey on term structure models designed for pricing fixed income securities and their derivatives

Using the daily data of Chinese 7-day repo rates from January 1, 1997 to December 31, 2008, this paper tests a variety of popular spot rate models, including single-factor diffusion, GARCH, Markov regime-switching and jump-diffusion models. We document that Chinese spot rates are subject to both market forces and administrative forces. GARCH, regime-switching and jump-diffusion models capture some important features of the dynamics of Chinese spot rates, but all models under study are overwhelmingly rejected. We further explore possible sources of model misspecification using diagnostic tests.

In this paper, due to the important influence of corporate governance on corporate financial behaviors and from an angle of corporate governance, we develop six hypotheses based on overseas and home relevant researches and data of Chinese listed companies from 1999 to 2003, and then we do hypotheses testes with regression models to examine the impact of share percent of the top 5 shareholders and counterbalance mechanism among blockholders on cash dividend distribution of listed companies, and to explore influence and counterbalance mechanisms in tunneling of cash dividend which derived from the special phenomena of “same shares same rights but different price”. We find that share percent of top 5 shareholders, control ability (or combined control ability) and balance degree (or combined balance degree) of blockholders have important influence on tunneling of cash dividend distribution. Lastly, the paper proposes five suggestions to restrict controlling shareholders to enlist private interests from tunneling of cash dividend and to protect rights and interests of small and medium shareholders.

Businesses often face the problem of providing incentives for agents to work effectively together on projects that develop over time. The agents' costly and unobservable effort jointly affects the survival of the project and thus the expected value of its cash now. A key feature of many contracting problems with multiple agents is that the agents exert effort at different times: some at the outset and some over time. The optimal timing of compensation reflects the timing of effort with payment for up-front effort preceding compensation for continuous effort. Deferring payment for agents exerting effort over time improves their incentives without impairing incentives for the up-front effort because this effort is sunk once the project is set up. The exact pattern of compensation between the agents with continuous effort depends on the relative severity of their moral hazard problems. In a special case where moral hazards are equally severe, the agents equally split the cash flow once it becomes available. This study suggests an approach to understanding a broad set of contracting problems in economics and finance. It rationalizes business conventions such as deferred compensation for top executives, the 50:50 split between law firm partners, and profit shares of influential directors (or lead actors) and residual claims of producers in the movie industry. Furthermore, the model predicts business failures such as the crisis in the mortgage industry due to the lack of characteristics suggested in the optimal contract.

By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the logperiodic power law (LPPL) model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite index (US ticker symbol SSEC) and Shenzhen Stock Exchange Component index (SZSC) exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in October 2007 and 2) from November 2008, bursting in the beginning of August 2009. We successfully predicted time windows for both crashes in advance [24, 1] with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble [37] and the peak in July 2008 of the global oil bubble [26]. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them. We complement the detection of log-periodic behavior with Lomb spectral analysis of detrended residuals and (H, q)-derivative of logarithmic indexes for both bubbles. We perform unit-root tests on the residuals from the log-periodic power law model to confirm the Ornstein-Uhlenbeck property of bounded residuals, in agreement with the consistent model of ‘explosive’ financial bubbles [16].

This paper focuses on evidence from stock markets as it investigates the spillovers from the United States to mainland China and Hong Kong SAR during the subprime crisis. Using both univariate and multivariate GARCH models, this paper finds that China's stock market is not immune to the financial crisis, as evidenced by the price and volatility spillovers from the United States. In addition, HK's equity returns have exhibited more significant price and volatility spillovers from the United States than China's returns, and past volatility shocks in the United States have a more persistent effect on future volatility in HK than in China, reflecting HK's role as an international financial center. Moreover, the impact of the volatility from the United States on China's stock markets has been more persistent than that from HK, due mainly to the United States as the origin of the subprime crisis. Finally, as expected, the conditional correlation between China and HK has outweighed their conditional correlations with the United States, echoing increasing financial integration between China and HK.

The expected equity risk premium is a key input of many asset prcing models in finance. There exist a number of methods to estimate the risk premium. It is also well documented that the risk premium is time-varying. This paper briefly reviews two different approaches. More specifically, the historical average and relative estimation are taken into closer examination. The first approach is applied to estimate equity risk premium for stock markets in Great China when the stock markets were recovering from the bottom. Then the relative estimation approach is also adopted to empirical data to justify the findings in the first one, which takes into consideration the lower required rate of return for Chinese investors due to lack of investment opportunities. After making these adjustments, we find that risk premium in mainland China is close to risk premium for Hong Kong and Taiwan markets. All of those markets have higher risk premium compared to US market. The risk premium for Shanghai and Shenzhen market are about 8% and 10% respectively. For Hong Kong and Taiwan these numbers become 8% and 9%, where the long-term forward-looking risk premium for US market is about 4%.

This paper studies the optimal scale and asset allocation of Sovereign Wealth Fund (SWF), taking China’s SWF as an example. We use the AR (1) process to simulate the future foreign exchange earnings of China and generate three patterns of the future earnings. With these three scenarios and based on Deaton’s precautionary saving model, we find that the optimal scale and asset allocation of China’s SWF mainly depend on the expected trend and fluctuation of the future foreign exchange earnings and expected yields that SWF can get. When foreign exchange earnings shows an upward trend, the scale of SWF should not be expanded even the expected investment yield is very high, and ratio of risky assets should be kept stable and high. When foreign exchange earnings is stabilized as its growth rate slows down, the scale of SWF has the positive correlation with the degree of earnings fluctuation and expected yield of investment, and also ratio of risky assets is generally lower. When foreign exchange earnings decrease, the scale of SWF should be expanded even the expected investment yield is not so high, and the ratio of risky assets is dependent on the characteristics of expected investment yields. We also conclude that investment policy of China’s SWF should follow Temasek’s investment model, under the current trend of China’s foreign exchange trend, and strive for high yield investment chances.

We empirically examine the role of venture capital in VC-backed listed firms in Mainland China. It is found that the VC-backed firms experience higher underpricing as the investors in the second market are too optimistic about the prospect of the VC-backed firms, but not about the lower pricing in the primary market. In addition, the pre- and post-IPO operating performance of VC-backed firms are found to be significantly better than that of non VC-backed ones. The result supports the monitoring model. Meanwhile, there is no evidence to support the certification model. Furthermore, it is consistent with the grandstanding model proposed by Gompers that high-reputation VC-backed firms have a better post-IPO operating and market performance compared to low-reputation VC-backed ones.