SearcharxivSearch

arXiv · 1502.06163

Threadneedle: An Experimental Tool for the Simulation and Analysis of Fractional Reserve Banking Systems

Abstract

Threadneedle is a multi-agent simulation framework, based on a full double entry book keeping implementation of the banking system's fundamental transactions. It is designed to serve as an experimental test bed for economic simulations that can explore the banking system's influence on the macro-economy under varying assumptions for its regulatory framework, mix of financial instruments, and activities of borrowers and lenders. Support is provided for Basel Capital and central bank reserve regulatory frameworks, inter-bank lending and correct handling of loan defaults within the bank accounting framework. In this paper we provide an overview of the design of Threadneedle, and the rational for the double entry book keeping approach used in its implementation. We then provide evidence from a series of experiments using the simulation that the macro-economic behaviour of the banking system is in some cases sensitive to double entry book keeping ledger definitions, and in particular that loss provisions can be systemically affecting. We also show that credit and money expansion in Basel regulated systems is now dominated by the Basel capital requirements, rather than the older central bank reserve requirements. This implies that bank profitability is now the main factor in providing new capital to support lending, meaning that lowering interest rates can act to restrict loan supply, rather than increasing borrowing as currently believed. We also show that long term liquidity flows due to interest repayment act in favour of the bank making the loan, and do not provide any long term throttling effect on loan expansion and money expansion as has been claimed by Keynes and others.

Explore related subjects

Keep this discovery

BibTeXRIS

Jacky Mallett. 2015-02-22. Threadneedle: An Experimental Tool for the Simulation and Analysis of Fractional Reserve Banking Systems. https://arxiv.org/abs/1502.06163

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers

The twenty-first century's transformative technology, artificial intelligence, is increasingly constrained by the twentieth century's transformative technology, the electricity grid. Rapid growth in electricity demand from data centers is leading to higher electricity prices, without a compensating supply-side response. We develop a framework linking data-center load growth, available generation capacity, and market-clearing prices to understand this phenomenon. We first analyze a deterministic model to show how differing estimates of demand and supply growth rates affect prices. We then model the expansion of new data centers and their associated electricity demand, together with build-outs of new electricity supply, as stochastic processes,resulting in probabilistic distributions of supply, demand, and prices rather than a single forecast. Finally, we formulate generation expansion as a stochastic control problem in which a revenue-maximizing investor dynamically chooses the intensity of supply-side investments. The analysis highlights a central challenge of the data-center build-out: even when rapid demand growth increases the need for new generation, the uncertainties related to load forecasts, development execution risks, and value cannibalization from overbuilding capacity may weaken incentives to invest at the pace required to keep electricity prices stable.

q-fin.GN

Measuring DeFi Risk

Decentralized finance (DeFi) lending has grown from nonexistent in 2017 to nearly 40 billion US Dollars in deposited funds in May 2022. Using cryptocurrency as collateral, the platforms match speculative margin trading with yield-seeking depositors lending coins pegged to the dollar (stable coins). Depositors receive claims guaranteed by a basket of collateral, akin to new stable coins. We develop a framework requiring only knowledge of aggregate deposits and borrowings to measure overall system risks to lenders and borrowers. Using evidence from major protocols, the measures identify an increase in system fragility beyond prudent levels around mid 2021, with a potential loss of peg for extreme variations in coin prices. Overall, the model offers an easily implementable aggregate risk metric capturing the perspectives of synthetic investors and offers early warning signals as the industry is moving from deposits guaranteed by collateral to fiat money.

q-fin.GN

Historical Reflections on Interest Rates and the Emergence of the Yield Curve

This text grew out of a historical introduction initially written for a study of interest rates in cryptocurrency markets. The difficulty of defining a term structure for a currency without a conventional bond market led naturally to a more fundamental question: under what historical conditions does a yield curve become observable at all? Credit existed long before modern money, and interest-bearing loans are documented as early as ancient Mesopotamia. For much of history, the surviving evidence lacks the institutional features that facilitate reliable comparisons of interest rates by maturity: standardised debt instruments, sufficiently homogeneous borrowers, regular issuance over a range of maturities, observable market prices, and liquid secondary markets. We trace the gradual emergence of these conditions from ancient Mesopotamia, Greece, and Rome, through medieval and early modern Europe, to the development of modern sovereign debt markets in the nineteenth and twentieth centuries.

q-fin.GN