and Asset Pricing
and Asset Pricing
复制标题
和资产定价
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
U. Rajan
中科院分区：
文献类型：
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作者：
R. Goettler;Christine A. Parlour;U. Rajan
我们考虑单一资产限价订单市场的交易模型，并检查微观结构噪声的属性（即交易价格与基本价值之间的差异）。资产具有共同的价值；此外，每个交易者都有自己的私人价值。交易者在选择是否购买有关共同价值的信息后随机到达市场。他们可以公布价格或接受公布的价格。如果交易者的订单尚未执行，他会随机重新进入市场，并可能更改之前的订单。因此，该模型是一个具有不对称信息的动态随机博弈。我们对模型中的均衡进行数值求解，并模拟市场结果。没有从贸易中获得内在利益的代理商具有最高的信息价值，并且也倾向于提供流动性。代理人获取信息的动机及其随后的均衡交易行为会随着资产的潜在波动性而系统性地变化。在均衡状态下，限价订单市场充当“波动性乘数”：价格比资产的基本价值波动更大。当资产的基本波动性较高或交易者之间存在信息不对称时，由于选择提供流动性的交易者类型的构成发生变化，这种影响就会增加。此外，微观结构噪声的变化与基本值本身的变化负相关。这意味着根据高频数据估计的资产贝塔值将是不正确的。因此，微观结构噪声和与其相关的其他变量可能会错误地解释横截面资产价格。最后，我们证明微观结构噪声本身具有正自相关性。
We consider a model of trade in a limit order market for a single asset, and examine the properties of the microstructure noise (i.e., the dierence between the transaction price and the fundamental value). The asset has a common value; in addition, each trader has a private value for it. Traders randomly arrive at the market, after choosing whether to purchase information about the common value. They may either post prices or accept posted prices. If a trader’s order has not executed, he randomly reenters the market, and may change his previous order. The model is thus a dynamic stochastic game with asymmetric information. We numerically solve for equilibrium in the model, and simulate market outcomes. Agents with no intrinsic benefit from trade have the highest value for information and also tend to supply liquidity. Agents’ incentives to acquire information and their subsequent equilibrium trading behavior changes systematically with the underlying volatility of the asset. In equilibrium, the limit order market acts as a “volatility multiplier”: prices are more volatile than the fundamental value of the asset. This eect increases when the fundamental volatility of the asset is higher or when there is asymmetric information across traders, due to a change in the composition of trader types that choose to provide liquidity. Further, changes in the microstructure noise are negatively correlated with changes in the fundamental value itself. This implies that asset betas estimated from high-frequency data will be incorrect. Thus, microstructure noise and other variables correlated with it may spuriously appear to explain cross-sectional asset prices. Finally, we show that the microstructure noise itself has positive autocorrelation.