Vladimir Skavysh - Latest

• Digital Payments in Firm Networks: Theory of Adoption and Quantum Algorithm

  Staff Working Paper 2024-17 Sofia Priazhkina, Samuel Palmer, Pablo Martín-Ramiro, Román Orús, Samuel Mugel, Vladimir Skavysh

  We build a network formation game of firms with trade flows to study the adoption and usage of a new digital currency as an alternative to correspondent banking.

  Content Type(s): Staff research, Staff working papers Topic(s): Central bank research, Digital currencies and fintech, Digitalization, Economic models, Financial institutions, Payment clearing and settlement systems, Sectoral balance sheet JEL Code(s): C, C6, C7, C71, D, D4, D8, D85, G, L, L2, L22

• Market structure of cryptoasset exchanges: Introduction, challenges and emerging trends

  Staff Analytical Note 2024-2 Vladimir Skavysh, Jacob Sharples, Sofia Priazhkina, Salman H. Hasham

  This paper provides an overview of cryptoasset exchanges. We contrast their design with exchanges in traditional financial markets and discuss emerging regulatory trends and innovations aimed at solving the problems cryptoasset exchanges face.

  Content Type(s): Staff research, Staff analytical notes Topic(s): Digital currencies and fintech, Payment clearing and settlement systems JEL Code(s): G, G1, G15, L, L1

• Improving the Efficiency of Payments Systems Using Quantum Computing

  Staff Working Paper 2022-53 Christopher McMahon, Donald McGillivray, Ajit Desai, Francisco Rivadeneyra, Jean-Paul Lam, Thomas Lo, Danica Marsden, Vladimir Skavysh

  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.

  Content Type(s): Staff research, Staff working papers Topic(s): Digital currencies and fintech, Financial institutions, Financial services, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): C, C6, C61, C63, D, D8, D83, E, E4, E42, E5, E58

• Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning

  Staff Working Paper 2022-29 Vladimir Skavysh, Sofia Priazhkina, Diego Guala, Thomas Bromley

  Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

  Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Central bank research, Econometric and statistical methods, Economic models, Financial stability JEL Code(s): C, C1, C15, C6, C61, C63, C68, C7, E, E1, E13, G, G1, G17, G2, G21