Ajit Desai - Latest
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From LVTS to Lynx: Quantitative Assessment of Payment System Transition
We quantitatively assess the changes in participants’ payment behaviour from modernizing Canada's high-value payments system to Lynx. Our analysis suggests that Lynx's liquidity-saving mechanism encourages liquidity pooling and early payments submission, resulting in improved efficiency for participants but with slightly increased payment delays. -
Improving the Efficiency of Payments Systems Using Quantum Computing
We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays. -
Macroeconomic Predictions Using Payments Data and Machine Learning
We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges. -
Estimating Policy Functions in Payments Systems Using Reinforcement Learning
We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information. -
Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19
We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.