中国金融学术研究网
CHINA FINANCIAL RESEARCH NETWORK

资本市场--资产定价
工作论文
2009-04-21 第2卷 第3期

编: 麻省理工学院斯隆管理学院金融学讲席教授,清华大学经管学院特聘教授。

执行主编: 杨之曙清华大学经济管理学院金融学副教授。


本期目录

The Characteristics and Pricing of Option-Type Derivatives: Evidence from Chinese Warrant Market

Gang Xiao University of South CarolinaMoore School of Business

UNDERSTANDING WORLD COMMODITY PRICES: Returns, Volatility and Diversification

Mei-Hsiu Chen UWA Business SchoolThe University of Western Australia

Testing for GARCH Effect at Different Time-scales

Peng Xuan-hua Chongqing UniversitySchool of Economics and Business Management
Fu Qiang University of New South WalesAustralian School of Business

On the Dividends of the Risk Model with Markovian Barrier

Qingbin Meng Nankai UniversityDepartment of Finance
Menghai Wang Nankai UniversityDepartment of Finance
Aimin Zhou Nankai UniversityDepartment of Finance

Volatility Spillovers between the US and the China Stock Market: Structural Break Test with Symmetric and Asymmetric GARCH Approach

Gyu-Hyen Moon Kyonggi UniversityDepartment of Business Administration
Wei-Choun Yu Winona State University Economics and Finance Department


论文摘要

The Characteristics and Pricing of Option-Type Derivatives: Evidence from Chinese Warrant Market

Gang Xiao University of South CarolinaMoore School of Business

This paper explores whether the pricing of the option-type derivative is affected by some of fundamental characteristics, such as size and liquidity of the derivative itself and the underlying asset, which are not involved in the standard pricing theory. Considering the unique status of warrants in China due to the relatively more flexible trading mechanism, I empirically examine the pricing of Chinese covered warrants to develop this study. Empirical results show that market prices of Chinese warrants are significantly higher than theoretical prices predicted by traditional pricing models such as Black-Scholes, Jump-Diffusion, and CEV model. For call warrants, about 25 percent of the market price can not be explained by pricing models, and this figure rises to over 60 percent for put warrants. Further regression tests show that both size and liquidity of warrants and underlying stocks significantly affect warrant pricing errors. The way in which the size and liquidity affect the pricing error depends on the type of warrants. In addition, it is evident that movements of put warrant prices in China do not follow movements of stock prices. To explain the above pricing puzzles,the concept of functional asset pricing is proposed. According to this concept, these pricing puzzles just reflect the existence of functional value of financial instruments that has long been neglected by traditional pricing models. In fact, Due to the high level of liquidity and popularity, the Chinese warrant may well function as a good tool for obtaining short-term profits. The pricing of Chinese warrants by the market may correctly re°ect the value of this function, and thus is rational in essence.

UNDERSTANDING WORLD COMMODITY PRICES: Returns, Volatility and Diversification

Mei-Hsiu Chen UWA Business SchoolThe University of Western Australia

In recent times, the prices of internationally-traded commodities have reached record highs and are expected to continue growing in the foreseeable future. This phenomenon is partially driven by strong demand from a small number of emerging economies, such as China and India. This paper places the recent commodity price boom in historical context, drawing on an investigation of the long-term time-series properties, and presents unique features for 33 individual commodity prices. Using a new methodology for examining cross-sectional variation of commodity returns and its components, we find strong evidence that the prices of world primary commodities are extremely volatile. In addition, prices are roughly 30 percent more volatile under floating than under fixed exchange rate regimes. Finally, using the capital asset pricing model as a loose framework, we find that global macroeconomic risk components have become relatively more important in explaining commodity price volatility.

Testing for GARCH Effect at Different Time-scales

Peng Xuan-hua Chongqing UniversitySchool of Economics and Business Management
Fu Qiang University of New South WalesAustralian School of Business

In this paper, we propose a new approach to test the presence of GARCH Effects of China stock market. Our method is based on Maximal Overlap Discrect Wavelet Transform (MODWT)that provides a natural platform to investigate the volatility behavior at different time scales without losing any information.The empirical results show that GARCH effects are more significant at short time horizons as compared to long. Furthermore, when compared the modeling results of GARCH-t with that of EGARCH-t, it yields very higher effectiveness to capture the leverage effect of financial time series at relavant time scales.

On the Dividends of the Risk Model with Markovian Barrier

Qingbin Meng Nankai UniversityDepartment of Finance
Menghai Wang Nankai UniversityDepartment of Finance
Aimin Zhou Nankai UniversityDepartment of Finance

This paper studies the dividend problem when the asset of the company is driven by a diffusion process and the dividend barrier follows a Markov process. The explicit expressions for dividends is derived and a numerical example is given.

Volatility Spillovers between the US and the China Stock Market: Structural Break Test with Symmetric and Asymmetric GARCH Approach

Gyu-Hyen Moon Kyonggi UniversityDepartment of Business Administration
Wei-Choun Yu Winona State University Economics and Finance Department

The paper examines the short-run spillover effect of daily stock returns and volatilities between the S&P 500 in the U.S. and Shanghai SSE composite in China. First, we find that a structural break happened in the SSE stock return mean in December 2005. Second, analyzing modified GARCH (1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the U.S. to the China stock market in the post-break period. Third, the symmetric volatility spillover effect from China to the U.S. is also observed in the post-break period.


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