中央风险账簿中delta套期保值的Leland模型

IF 1.6 3区 经济学 Q3 BUSINESS, FINANCE
Johannes Muhle-Karbe, Zexin Wang, Kevin Webster
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引用次数: 0

摘要

使用Leland(1985)模型的可处理扩展,我们研究了如何在一个集中、自动化的做市台中使用市场和限额订单来实际实施德尔塔对冲策略,该做市台集成了期权及其基础的交易和流动性供应。在连续时间限制中,最优极限阶暴露可以通过逐点最大化明确计算。它是由逆向选择、买卖价差和波动性的相对幅度决定的。相应的期权价格(可以使用市场和限价订单复制期权)是通过非线性PDE来表征的。我们的结果强调了为反向交易策略提供战术流动性的好处,即使对于不是竞争做市商的交易台也是如此。更普遍地说,该论文还展示了简化模型如何与市场微观结构的“蛮力”数值方法相竞争。微观结构参数的估计和最优交易策略的模拟都是具体的,并与现实生活中的高频数据相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Leland model for delta hedging in central risk books

A Leland model for delta hedging in central risk books

Using a tractable extension of the model of Leland (1985), we study how a delta-hedging strategy can realistically be implemented using market and limit orders in a centralized, automated market-making desk that integrates trading and liquidity provision for both options and their underlyings. In the continuous-time limit, the optimal limit-order exposure can be computed explicitly by a pointwise maximization. It is determined by the relative magnitudes of adverse selection, bid–ask spreads, and volatilities. The corresponding option price—from which the option can be replicated using market and limit orders—is characterized via a nonlinear PDE. Our results highlight the benefit of tactical liquidity provision for contrarian trading strategies, even for a trading desk that is not a competitive market maker. More generally, the paper also showcases how reduced-form models are competitive with “brute force” numerical approaches to market microstructure. Both the estimation of microstructure parameters and the simulation of the optimal trading strategy are made concrete and reconciled with real-life high frequency data.

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来源期刊
Mathematical Finance
Mathematical Finance 数学-数学跨学科应用
CiteScore
4.10
自引率
6.20%
发文量
27
审稿时长
>12 weeks
期刊介绍: Mathematical Finance seeks to publish original research articles focused on the development and application of novel mathematical and statistical methods for the analysis of financial problems. The journal welcomes contributions on new statistical methods for the analysis of financial problems. Empirical results will be appropriate to the extent that they illustrate a statistical technique, validate a model or provide insight into a financial problem. Papers whose main contribution rests on empirical results derived with standard approaches will not be considered.
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