SSRN Author: Jay CaoJay Cao SSRN Content
https://privwww.ssrn.com/author=3240817
https://privwww.ssrn.com/rss/en-usFri, 22 May 2020 01:16:37 GMTeditor@ssrn.com (Editor)Fri, 22 May 2020 01:16:37 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Deep Hedging of Derivatives Using Reinforcement LearningThis paper investigates how reinforcement learning can be used to derive optimal hedging strategies for derivatives. We assume that the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when there are transaction costs. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process. The paper extends the standard reinforcement learning approach by using two different Q-functions.
https://privwww.ssrn.com/abstract=3514586
https://privwww.ssrn.com/1899692.htmlThu, 21 May 2020 09:00:14 GMTREVISION: A Neural Network Approach to Understanding Implied Volatility MovementsWe employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the return being observed. This produces a further improvement of 62.12% and shows that the expected response of the volatility surface to movements in the index is quite different in high and low volatility environments.
https://privwww.ssrn.com/abstract=3288067
https://privwww.ssrn.com/1885190.htmlMon, 13 Apr 2020 15:36:53 GMTREVISION: Deep Hedging of Derivatives Using Reinforcement LearningThis paper investigates how reinforcement learning can be used to derive optimal hedging strategies for derivatives. We assume that the objective is to minimize a function equal to the mean hedging cost plus a constant times the standard deviation of the hedging cost. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedging for a short position in a call option when there are transaction costs. Two situations are considered. In the first, the asset price follows a geometric Brownian motion. In the second, the asset price follows a stochastic volatility process, but the volatility exposure cannot be hedged. The paper shows how the standard deviation of the cost of hedging can be accurately incorporated into the objective function when reinforcement learning is used.
https://privwww.ssrn.com/abstract=3514586
https://privwww.ssrn.com/1860749.htmlMon, 27 Jan 2020 04:09:12 GMTREVISION: A Neural Network Approach to Understanding Implied Volatility MovementsWe employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the return being observed. This produces a further improvement of 62.12% and shows that the expected response of the volatility surface to movements in the index is quite different in high and low volatility environments.
https://privwww.ssrn.com/abstract=3288067
https://privwww.ssrn.com/1840414.htmlMon, 11 Nov 2019 09:43:49 GMT