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We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk. We first analyse this problem through the lens of continuous-time financial mathematics and derive an upper bound for not-arbitrage fee income that...
Persistent link: https://www.econbiz.de/10014265345
Mathematical modelling is ubiquitous in the financial industry and drives key decision processes. Any given model provides only a crude approximation to reality and the risk of using an inadequate model is hard to detect and quantify. By contrast, modern data science techniques are opening the...
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We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor, we show that this allows the fast trader to obtain risk free profits, but that these profits cannot be scaled. We...
Persistent link: https://www.econbiz.de/10013119313
Machine learning models are increasingly used in a wide variety of financial settings. The difficulty of understanding the inner workings of these systems, combined with their wide applicability, has the potential to lead to significant new risks for users; these risks need to be understood and...
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We are interested in how the relationship between the fee in a constant product market (CPM) and the volatility of the swapped pair on other liquid exchanges influences the losses / gains of the liquidity providers. We review three classical market making models: Glosten and Milgrom, Kyle and...
Persistent link: https://www.econbiz.de/10013294011
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Despite important theoretical questions that remain to be solved, Artificial Intelligence and Deep Learning are being increasingly used in the Finance and Insurance sector. Beyond straightforward data analytics, decision models are being implemented with Deep Learning. These algorithms cannot be...
Persistent link: https://www.econbiz.de/10013292066
Business reliance on algorithms are becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include VW’s Dieselgate scandal with fines worth of $34.69B, Knight Capital’s bankruptcy (~$450M) by a...
Persistent link: https://www.econbiz.de/10013246963