A liquid future market serves not only to reduce the transaction cost and discover price,but also helps design investment strategies and lower risk for investors.But the effective transaction cost is usually hard to measure due to the incompleteness of data and we need to estimate the effective transaction cost.Liquidity is the key indicator of future market's efficiency and the key challenge is to identify good measures of liquidity.A growing literature has focused on the effect of liquidity (and transaction cost) on expected stock return in both academia and financial industry but there is no systematic research on the liquidity effects in the future market.Therefore we need to propose a model to liquidity from the perspective of market micro-structure and asset pricing perspective and to study if liquidity is priced cross-sectionally in the future market.High frequency data contains large amount of information and we need to focus on the research questions,such as the characteristic and measure of liquidity and effect of liquidity and transaction cost in the future market.Therefore we propose a sequential trading model by using the Bayesian method to compare different measures of liquidity and choose the best measure of transaction cost.Then the measures are corrected for information asymmetry and micro-structure noise.Combined with realized return with transaction costs,we come up with an asset pricing model which also accounts for the economics of scale and cyclical effect in the future market.The data comes from Chinese Future Market.We show that liquidity measure based on Bayesian estimation using high-frequency data have large advantages over the traditional method of moments.The empirical results are as follows: Order has a significant price impact and it means private information is incorporated in the order.Liquidity measures based on complete model and transaction data are better since they have a higher correlation compared with the estimation of defined method.Transaction cost is included in the excess return.Transaction cost has a cyclical effect on asset prices.Therefore liquidity has a large impact on the return of futures.We propose a sequential trading model,using Bayesian estimation with high frequency data to study the liquidity effect in the future market.The paper will draw a much clearer picture of liquidity,transaction cost and return for the participants in the future market.Also it will shed light on the regulatory policy to increase market quality,liquidity and efficiency and to reduce the transaction cost in the future market.%近年来交易成本和流动性对于股票资产的预期收益或定价影响受到学术界和业界的关注,期货市场的各种交易产生大量数据,这些数据中隐藏着重要的信息,但采用逐笔高频交易数据对期货市场交易成本、流动性和资产定价问题进行系统研究的比较少,对期货市场交易成本和流动性的内涵、特征、度量方法,以及交易成本和流动性在期货资产定价中的作用等问题有待深入探讨.从序贯交易模型的视角,基于贝叶斯参数估计方法及逐笔高频交易数据和每日收盘价格数据测量期货市场的交易成本,对不同交易成本和流动性测量方法进行比较研究,探讨各种交易成本与流动性的相互关系,选出合适的流动性测量方法.同时,从逐笔高频交易数据存在报价离散化、价格聚集和非对称信息等方面对交易成本模型修正和扩展.将交易成本与真实收益率结合并考虑市场规模和周内效应的作用,构建期货市场资产定价模型,从中国期货市场选取不同品种的主力合约数据进行实证研究.研究结果表明,基于贝叶斯参数估计和逐笔高频交易数据的交易成本的测量方法具有明显的优点,可以克服传统基于矩估计交易成本测量交易成本的不足,更适合用来作为流动性的代理变量.①订单对价格存在比较显著的冲击现象,这些冲击表明私人信息被包含在这些合约的交易里.②基于完整模型的交易成本更适合用来作为流动性的代理变量,与定义法的流行性成本估计值的相关系数更高,基于逐笔高频交易数据和完整模型的交易成本是最优的流动性的代理变量.③交易成本确实被包含在超额收益中,总体来说,交易成本对资产收益率的影响具有比较明显的周内效应.因此,流动性对投资期货的收益率有很大贡献,为了达到更高的收益,通常需要为获得好的流动性而付出更高的代价.从序贯交易模型的视角,基于贝叶斯参数估计方法和逐笔高频交易数据测量期货市场的交易成本,有助于市场参与主体更好地认识和分析期货市场的交易成本、流动性和资产收益之间的关系,对市场监管机构有效评估市场质量、设计合理的期货市场交易制度、有效降低市场交易者的交易成本、增强期货市场流动性、提高市场运行效率具有一定的参考价值.
展开▼
