Modeling Feedback Effects with Stochastic Liquidity

Angelika Esser, Burkart Mönch
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引用次数: 4

Abstract

We model the interactions between the trading activities of a large investor, the stock price and the market liquidity. Our framework generalizes the model of Frey (2000), where liquidity is constant by introducing a stochastic liquidity factor. This innovation has two implications. First, we can analyse trading strategies for the large investor that are affected by a changing market depth. Second, the sensitivity of stock process to the trading strategy of the large investor can vary due to changes in liquidity. Features of our model are demonstrated using Monte Carlo simulation for different scenarios. The flexibility of our framework is illustrated by an application that deals with the pricing of a liquidity derivative. The claim under consideration compensates a large investor who follows a stop loss strategy for the liquidity risk that is associated with a stop loss order. The derivative matures when the asset price falls below a stop loss limit for the first time and then pays the price difference between the asset price immediately before and after the execution of the stop loss order. The setup to price the liquidity derivative is calibrated for one example using real world limit order book data so that one gets an impression about the order of magnitude of the liquidity effect.
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基于随机流动性的反馈效应建模
我们建立了大型投资者的交易活动、股票价格和市场流动性之间的相互作用模型。我们的框架推广了Frey(2000)的模型,其中通过引入随机流动性因子,流动性是恒定的。这种创新有两个含义。首先,我们可以分析受市场深度变化影响的大型投资者的交易策略。其次,由于流动性的变化,股票交易过程对大型投资者交易策略的敏感性会有所不同。我们的模型的特点是使用蒙特卡罗模拟不同的场景。一个处理流动性衍生品定价的应用程序说明了我们框架的灵活性。正在考虑的索赔补偿了遵循止损策略的大型投资者,以应对与止损订单相关的流动性风险。当资产价格首次跌破止损限价时,衍生品到期,然后支付止损指令执行前后资产价格之间的差价。为流动性衍生品定价的设置是使用真实世界的限价订单数据校准的一个例子,以便人们对流动性效应的数量级有一个印象。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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