Pricing European Options With A Hard-to-borrow Model

Financial derivatives play an irreplaceable role in the financial markets as price discovery and risk transfer due to their non-linear and leveraged nature..Options,as the originator of modern derivatives pricing theory,have been occupying academic and practical research hotspots.The Black-Scholes model is the fundamental work of modern option pricing,but there has been a strong focus on market micro structure lacking sufficient attention.Modern financial markets are rife with innovation,and not only are new financial products being developed,but financial regulators are also continuously Regulatory innovation to meet market needs.The main object of this paper is a special price model arising from a particular regulatory provision.The main work in this paper focuses on three aspects of the model: grounded theory modifications,numerical algorithms and empirical comparisons.In 2005,the U.S.Securities and Exchange Commission(SEC)implemented the SHO regulatory rule to govern short selling,especially naked short selling.The rule requires short sellers to repurchase a portion of the underlying to reduce the counterparty's liquidation risk after their losses reach a certain level..And the repurchase price order must be higher than the market traded price,causing this repurchase to have a price impact.Options traders,especially market makers,are significantly affected by price shocks as they require frequent long and short trades to hedge their options positions.Although it is not subjectively intended to profit from price declines,incorporating price shocks into option pricing models becomes a necessary topic.Avellaneda and Lipkin(2009)propose a Poisson process with compensation(Hard-to-borrow model),which couples the strength of the Poisson process to stock prices,is used to describe this particular The phenomenon of price shocks triggered by shortselling-buyback behavior.This paper is based on previous work by Guiyuan Ma(2018),Guiyuan Ma(2019)Based on the HTB model risk-neutral measurement,the errors are corrected,and the improvement method based on Monte Carlo simulation is given.China's financial market market is in a booming emerging market,with high market volatility and its own trading restrictions Examples include upside and downside stops and T+1 delivery.China is also actively developing its derivatives market,notably launching an ETF based on the SSE 50 in 2015 Options,which provide investors with new investment channels and risk transfer tools.However,the development of derivatives cannot be separated from the accumulation of the spot market,which has an important feature of the so-called short selling restrictions.In 2005,China legislated the introduction of the financing and securities financing system,but the underlying stocks of financing and securities financing are still limited.In this paper,we use the Heston model,SABR model,and HTB model to estimate the parameters of the SSE 50 ETF.The paper uses Markov Monte Carlo method to estimate the Heston model when estimating the SABR model.The nonlinear coefficient parameter estimation method is used.For the estimation of volatility,the GARCH family is chosen in this paper.The empirical results in this paper show that the HTB model performs slightly worse than the SABR model and better than the Heston model.The innovation of this paper is to extend the research perspective of option pricing to the microstructure of financial markets,taking into account the regulatory provisions.The trading behavior of assets resulting from changes.The increasing complexity of financial markets requires researchers to analyze the various types of market participants in more detail.The work to be done in this paper focuses on the calibration of the model and parameter estimation.