A Market Making Quotation Strategy Based on Dual Deep Learning Agents for Option Pricing and Bid-Ask Spread Estimation
By P. Hsu, Chin-chiang Chou, Szu-Hao Huang, An-Pin Chen. 2018
8 citations recorded by the source corpus.
Traditional professional traders and institutional investors utilized complex statistical models to price various derivative contracts and make trading decisions in the option and future markets. In recent years, with the rapid growth of algorithmic trading and program trading, the advanced information and communication technology has become an indispensable element for high-frequency traders, especially for the market makers. In addition, artificial intelligence and deep learning also plays an important role in novel financial technology (FinTech) research field. In this paper, we proposed a market making quotation strategy based on deep learning structure and practical finance domain knowledge. The proposed dual agents will simultaneously model the option prices and bid-ask spreads. The experiments demonstrate that our system can precisely estimate the value of options than famous financial engineering models. It also can be extended to develop proper market making quotation strategies to trade the options of Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX).
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Finance
In Management Science and Operations Research
Citations in the corpus
- Deep Learning for Financial Applications : A Survey (2020, methodology): “[143] proposed a novel method that predicted TAIEX option prices using bid-ask spreads and Black & Scholes option price model parameters with 3-layer DMLP.”