| Traditional financial theory based on the Efficient Market Hypothesis, which, assuming that price can fully reflect all information available at that time, only focuses on the time series characteristics of the asset price, and strives to explain and predict the price volatilities only with the price itself. Due to the complexity of the price volatility, the academia, however, has called the premise into question; with the development of the micro-structure of the financial market, volatility--the neglected factor which might possibly involve market information--has gradually come under scrutiny. Actually, the thumb rule of the "volume-price analysis" has long been applied adeptly by practitioners in technical analysis in the financial market. Therefore, it is particularly necessary to explore the possible relationship between the trading volume and the asset price theoretically and empirically.This paper studies the price-volume relation in financial markets and the principal driving factors behind it on tha basis of the Mixture Distribution Hypothesis (MDH). This study unfolds from two aspects:1) in stock market, we compare the explanatory power of trading volumes to price volatility in different countries’stock markets and develop a new method to deal with the trading volume as persistence-free series using a GARCH-V model and low-frequency data;2) in the futures market, we deeply dig the principal driving factors for the price-volume relationship in the Chinese stock index futures market using some linear models with the "realized volatility" and high-frequency data. The specific contents are as follows:First, starting from different GARCH-type models as well as non-normal GARCH-type models, the characteristics of the price volatility of China’s stock market is studied using some GARCH-type models, and a VAR measure of market risk is developed. The study shows that the EGARCH model and APARCH model perform better than other models, and the GARCH-type models under the assumption of the student t distribution or the GED distribution, in general, work better than GARCH models under the assumption of the normal distribution. The findings serve as an important reference for the selection of volatility models to research in China’s stock market.Subsequently, with the introduction of trading volume into the price volatility equation, the GARCH-V model is employed to empirically test the explanatory power of trading volume to market price volatility in seven countries’stock market. The volatility clustering of the trading volume is removed and the relationship between the persistence-free trading volume and then the price volatility is innovatively explored. The findings demonstrate that the trading volumes of mature financial markets have more explanatory power to price volatilities, and market prices assimilate and reflect information better, which implies high efficiency of the financial market. More importantly, as the explanatory power of the persistence-free trading volume to the price volatility increases with the maturity of the countries’financial markets, the persistence-free trading volume is a more desirable proxy for the information flow, and capable of enhancing the explanatory power of the trading volume for the price volatility.Next, according to Jone et al.(1994), we divide its trading volume of the CSI300stock index futures into trading times and average trading sizes, and take the jumps and the non-symmetries in the "realized" volatility into account to construct a base model, a continuous and jump volatility model and a non-symmetrical model for volume-price relationship models for China’s stock index futures. The study shows that there is an significantly positive correlation between the trading volume and the price volatility; the trading volume, the trading times, and the average trading sizes all have significantly positive effect on the continuous and the jump volatility; the positive correlation between the trading volume and the continuous volatility can reflect more accurately the aggregate volume-price relation in China’s futures index market; the downside realized semi-variance includes more market volatility information than the upside realized semi-variance; and the average trading size, as a major driving factor behind the volume-price relationship, has more explanatory power for market volatility.Then, the relationship between the volume and the price at the tail is studied using a GARCH-copula model. The model not only examines the relationship between high price rises and high volumes, and between big price falls and low volumes, but also the relationship between big price changes and high volumes, and between small price changes and low volumes. This characterizes the interdependence between price and volume at the tail under extreme market conditions, and meanwhile shows the time-varying features.Finally, we summarize the main findings and contributions, and propose directions in future research. |
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