AI-Driven Derivatives and Collateral Optimization
DOI:
https://doi.org/10.5281/zenodo.21455188Keywords:
Artificial intelligence; derivatives; collateral; optimization; model risks; economic stability; risk analysis.Abstract
Intelligent Financial Integration Using AI for Derivatives and Collateral Optimization
Commercial banks are significant intermediaries in the global derivatives market, generating substantial derivative revenues that require effective management to ensure stable and adequate earnings with reduced risk. Within banks, the Treasury function is responsible for collateral management and optimization across the group, including intra-group trades with other market-making businesses. Collateral optimization involves maximizing the utility of collateral through careful management and control of the Securities Financing Transactions (SFT) book with market counterparts—allowing Securities Financing Transactions for any reason during normal market conditions, minimizing funding costs, and being ready for stressed market conditions.
AI can enhance decision-making and operational efficiency through automation of derivatives pricing, dynamics, P&L, back-testing, risk modelling, and liquidity management. Three types of microstructure decisions, supported by AI, can significantly improve trading profitability: optimally triggering market orders during volatility bursts; determining the best price for volume-exploratory limit orders; and screening and selecting limit orders positioned close to the market. AI can also optimize trade schedules using market meta-data extracted from liquidity provider flows. Profits from non-transparent or opaque strategies can be improved by early detection and exploitation of statistical arbitrage opportunities. Additionally, AI can detect arbitrage opportunities in collat-eral-required strategies and condition the trades to exploit minimum-risk scenarios, for example, using capital structure information from the Towersy et al. credit-suer model.
Intelligent Financial Integration Using AI for Derivatives and Collateral Optimization
Commercial banks are significant intermediaries in the global derivatives market, generating substantial derivative revenues that require effective management to ensure stable and adequate earnings with reduced risk. Within banks, the Treasury function is responsible for collateral management and optimization across the group, including intra-group trades with other market-making businesses. Collateral optimization involves maximizing the utility of collateral through careful management and control of the Securities Financing Transactions (SFT) book with market counterparts—allowing Securities Financing Transactions for any reason during normal market conditions, minimizing funding costs, and being ready for stressed market conditions.
AI can enhance decision-making and operational efficiency through automation of derivatives pricing, dynamics, P&L, back-testing, risk modelling, and liquidity management. Three types of microstructure decisions, supported by AI, can significantly improve trading profitability: optimally triggering market orders during volatility bursts; determining the best price for volume-exploratory limit orders; and screening and selecting limit orders positioned close to the market. AI can also optimize trade schedules using market meta-data extracted from liquidity provider flows. Profits from non-transparent or opaque strategies can be improved by early detection and exploitation of statistical arbitrage opportunities. Additionally, AI can detect arbitrage opportunities in collat-eral-required strategies and condition the trades to exploit minimum-risk scenarios, for example, using capital structure information from the Towersy et al. credit-suer model.
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