The Welfare Effects of a Liquidity-Saving Mechanism
This paper considers the welfare effect of introducing a liquidity-saving mechanism (LSM) in a real-time gross settlement (RTGS) payment system. We study the planner's problem to get a better understanding of the economic role of an LSM and find that an LSM can achieve the planner's allocation for some parameter values. The planner's allocation cannot happen without an LSM, as long as some payments can be delayed without cost. We show that, in equilibrium with an LSM, there can be either too few or too many payments settled early compared with the planner's allocation, depending on the parameter values. Using Fedwire data to calibrate our model, we describe the equilibrium that would arise with an LSM and compare welfare with and without an LSM. Our results suggest that introducing an LSM could have significant benefits.
Diagnostics for the financial markets : computational studies of payment system : Simulator Seminar Proceedings 2009-2011
Chapter 1 Matti Hellqvist - Tatu Laine Introduction 9 Chapter 2 Klaus Abbink - Ronald Bosman - Ronald Heijmans - Frans van Winden Disruptions in large value payment systems: An experimental approach 15 Chapter 3 Edward Denbee - Rodney Garratt - Peter Zimmerman Methods for evaluating liquidity provision in real-time gross settlement payment systems 53 Chapter 4 Ronald Heijmans - Richard Heuver Is this bank ill? The diagnosis of doctor TARGET2 77 Chapter 5 Tatu Laine - Tuomas Nummelin - Heli Snellman Combining liquidity usage and interest rates on overnight loans: an oversight indicator 119 Chapter 6 Ronald Heijmans - Richard Heuver - Danielle Walraven Monitoring the unsecured interbank money market using TARGET2 data 135 Chapter 7 Luca Arciero Evaluating the impact of shocks to the supply of overnight unsecured money market funds on the TARGET2- Banca d'Italia functioning: a simulation study 169 Chapter 8 Ashwin Clarke - Jennifer Hancock Participant operational disruptions: the impact of system design 193 Chapter 9 Horatiu Lovin Systemically important participants in the ReGIS payment system 219 Chapter 10 Marc Propper - Iman van Lelyveld - Ronald Heijmans Network dynamics of TOP payments 235 Chapter 11 Carlos Leon - Clara Machado - Freddy Cepeda - Miguel Sarmiento Systemic risk in large value payments systems in Colombia: a network topology and payments simulation approach 267 Chapter 12 Horatiu Lovin - Andra Pineta Operational risk in ReGIS - a systemically important payment system 315 Chapter 13 Robert Oleschak - Thomas Nellen Does SIC need a heart pacemaker? 341 Chapter 14 Robert Arculus - Jennifer Hancock - Greg Moran The impact of payment system design on tiering incentives 379 Chapter 15 Martin Diehl - Uwe Schollmeyer Liquidity-saving mechanisms: quantifying the benefits in TARGET2 411 Chapter 16 Biliana Alexandrova-Kabadjova - Francisco Solis-Robleda The Mexican experience in how the settlement of large payments is performed in the presence of high volume of small payments 431
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.