The field of artificial intelligence (AI) was distinguished earlier than some might imagine in the mid 20th century. The relevance of AI and its components of machine learning and generative AI (Gen AI) were not established until much later though due to the eventual development of computing power. Market manipulation, liquidity and collusion in OTC markets
The benefits provided by AI in today’s financial markets and specifically in market making and trading strategies are clear. The ability to process vast amounts of market data to make sense of the data to then build models to define bid-offer spreads for market makers or appropriate entry and exit trade points. AI has allowed the end-to-end trade process to be fully automated in both exchange and more recently the OTC markets.
Liquidity has always been an important topic in OTC markets especially at times of market stress when liquidity and activity quickly drop. There has been research to suggest that AI can assist with providing extra opportunities to liquidity where traditional methods might have missed1.
There can be some drawbacks with AI however such as unintentional collusion of AI agents especially by largely data-driven and tech-driven market making firms2. The latest state-of-the-art AI algorithms will likely be quickly leveraged by similar firms to define optimal bid-offer spreads for the market. The risk in OTC markets arises due to the limited availability of data used to build AI models due to the inherent lack of trading activity. Similar data will create similar models leading to a high risk of collusion. Risks of speed and automation of Generative AI
Gen AI has been a popular term for the last few years, but direct and specific use of gen AI in investment and trading has been less so. Gen AI agents will be tasked with the same objective – maximise profit margins with minimal risk1. Eventually a natural convergence of model and algorithms adopted by financial firms will be met, more so in OTC markets where there is generally limited availability of market data. Additionally, the rapid trade execution speed of gen AI agents could lead to increased price volatility2.
Despite this, there is a general trend towards speed and automation here that started in liquid exchange markets and now moving towards the OTC space. The centralisation and normalisation of OTC data driven by regulatory requirements and client demands ensure that this transition to AI-driven decision making is inevitable. Parameta Solutions consistently looks to update data and data formats in-line with industry standards.
Arbitrage-free implied volatility surfaces
The innate complexity of pricing of the derivatives and options markets is a significant barrier for leveraging AI in this space. Calculation of price, implied volatility and volatility surfaces of options in particular have largely been conducted using traditional models like Black-Scholes and SABR. Generative AI models, like VolGAN3, have started to emerge showing real-world application in the production of arbitrage-free volatility surfaces.
In our recent OTC market survey we asked over 500 market participants about their views on AI and the impact over the last year and the future impact. Find out more about this and the other topics impacting the OTC market here.
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