As a result of structural changes in financial markets and the introduction of mandatory central ... more As a result of structural changes in financial markets and the introduction of mandatory central clearing obligations for standardised over-the-counter (OTC) derivatives, central clearing has expanded significantly in recent years. In parallel, public authorities have devoted greater attention to strengthening the global safeguards for central clearing, notably with the adoption of the CPMI-IOSCO Principles for Financial Market Infrastructures in 2012, a complementary CPMI-IOSCO report on recovery of financial market infrastructures in 2014, and dedicated Financial Stability Board guidance on how to apply the “Key Attributes of Effective Resolution Regimes for Financial Institutions” to financial market infrastructures in 2014. In 2015 global standard-setting bodies launched a comprehensive work plan on central counterparty (CCP) resilience, recovery, resolution and clearing interdependencies to further enhance this framework. This article takes stock of the latest achievements in this area and outlines future priorities, concerning the finalisation of the CCP work plan, interactions between requirements for central counterparties and those for banks, greater granularity of central counterparty supervision and oversight, cross-border cooperation between authorities as well as macroprudential safeguards for central clearing.
Advances in finance, accounting, and economics book series, 2013
ABSTRACT A database driven multi-agent model has been developed with automated access to US bank ... more ABSTRACT A database driven multi-agent model has been developed with automated access to US bank level FDIC Call Reports that yield data on balance sheet and off balance sheet activity, respectively, in Residential Mortgage Backed Securities (RMBS) and Credit Default Swaps (CDS). The simultaneous accumulation of RMBS assets on US banks’ balance sheets and also large counterparty exposures from CDS positions characterized the $2 trillion Collateralized Debt Obligation (CDO) market. The latter imploded at the end of 2007 with large scale systemic risk consequences. Based on US FDIC bank data, that could have been available to the regulator at the time, the authors investigate how a CDS negative carry trade combined with incentives provided by Basel II and its precursor in the US, the Joint Agencies Rule 66 Federal Regulation No. 56914, which became effective on January 1, 2002, on synthetic securitization and Credit Risk Transfer (CRT), led to the unsustainable trends and systemic risk. The resultant market structure with heavy concentration in CDS activity involving 5 US banks can be shown to present too interconnected to fail systemic risk outcomes. The simulation package can generate the financial network of obligations of the US banks in the CDS market. The authors aim to show how such a Multi-Agent Financial Network (MAFN) model is well suited to monitor bank activity and to stress test policy for perverse incentives on an ongoing basis.
The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for f... more The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for financial inclusion in India, marked only by a much greater scope and ambition than previous roll outs. This top down approach to close the gap on the unbanked of India relies primarily on public sector banks with targets set for rural outreach. We develop an innovative approach using cross sectional bank level data from 2014 till 2017 to quantify the incentives and costs involved in targeting unbanked households. This gives a monetary estimate of the economic shortfalls or surpluses for participating banks, measured as bank balances relative to outlay costs and subsidies per PMJDY beneficiary. We model the double bind problem faced by banks to achieve economies of scale that arise from spreading the fixed infrastructure costs over the number of below poverty line (BPL) customers when there is a dearth of balances in these accounts. This lack of economic viability of PMJDY accounts is found in most public sector banks, a matter which is problematic in view of their extant financial fragility in India. We provide evidence for cross subsidization of rural bank accounts by urban accounts. We give estimates using fixed effects panel methods as to what cost public sector banks bear and also quantify the extent to which account ineffectiveness is ameliorated with exogenous factors, primarily the tie up of PMJDY accounts with bio-metric Aadhar cards and electronic direct benefit transfer of G2P payments.
Self-referential calculations of oppositional or contrarian structures and the necessity to innov... more Self-referential calculations of oppositional or contrarian structures and the necessity to innovate to outsmart hostile agents in an arms race are ubiquitous in socio-economic systems, immunology and evolutionary biology. However, such phenomena with strategic innovation, which entails novel actions beyond listable sets, are outside the ambit of extant game theory. How can strategic innovation with novel actions be a Nash equilibrium of a game? Based on the only known Godel-Turing-Post (GTP) axiomatic framework on meta-analyses of offline simulations that involve recursive operations on encoded information, we show that mutually mentalising agents capable of such offline simulations can “think outside the box” and embark on an arms race in novelty or surprises. A key logical ingredient of this is the self-referential encoding of a proposition on mutual negation or opposition, often referred to as the Godel sentence. The only recursive best response function of a two-person game with an oppositional structure that can implement strategic innovation in a lock-step formation of an arms race is the productive function of the Emil Post set theoretic proof of the Godel incompleteness result.
ABSTRACT Relational neural networks as a concept offers a unique opportunity for improving classi... more ABSTRACT Relational neural networks as a concept offers a unique opportunity for improving classification accuracy by exploiting relational structure in data. The premise is that a relational classification technique, which uses information implicit in relationships, should classify more accurately than techniques that only examine objects in isolation. In this paper, we study the use of relational neural networks for predicting bank failure. Alongside classical financial ratios normally used as predictor variables, we introduced new relational variables for the network. The relational neural network structure, specified as a combination of feed forward and recurrent neural networks, is determined by bank data through neuro‐evolution. We discuss empirical results comparing performance of the relational approach to standard propositional methods used for bank failure prediction.
The complexity of the financial markets, represents a big challenge to the specialist in the area... more The complexity of the financial markets, represents a big challenge to the specialist in the area. The traditional way of coping with the analysis of such systems is the use of analytical models. However, the analytical models present some difficulties and this has leaded to the development of alternative methods for the analysis of such markets. In this paper we
Social and economic networks are becoming increasingly popular in the last ten years, because of ... more Social and economic networks are becoming increasingly popular in the last ten years, because of both the application of game theory to the network formation processes 4, and the study of stochastic processes that fit the statistical properties of real world social networks. 5 In the very recent years there have also been attempts to combine the contribution of these two streams of research, trying to find strategic models whose equilibria resemble the empirical data. 6 A well known source of debate in the game theoretical ...
As a result of structural changes in financial markets and the introduction of mandatory central ... more As a result of structural changes in financial markets and the introduction of mandatory central clearing obligations for standardised over-the-counter (OTC) derivatives, central clearing has expanded significantly in recent years. In parallel, public authorities have devoted greater attention to strengthening the global safeguards for central clearing, notably with the adoption of the CPMI-IOSCO Principles for Financial Market Infrastructures in 2012, a complementary CPMI-IOSCO report on recovery of financial market infrastructures in 2014, and dedicated Financial Stability Board guidance on how to apply the “Key Attributes of Effective Resolution Regimes for Financial Institutions” to financial market infrastructures in 2014. In 2015 global standard-setting bodies launched a comprehensive work plan on central counterparty (CCP) resilience, recovery, resolution and clearing interdependencies to further enhance this framework. This article takes stock of the latest achievements in this area and outlines future priorities, concerning the finalisation of the CCP work plan, interactions between requirements for central counterparties and those for banks, greater granularity of central counterparty supervision and oversight, cross-border cooperation between authorities as well as macroprudential safeguards for central clearing.
Advances in finance, accounting, and economics book series, 2013
ABSTRACT A database driven multi-agent model has been developed with automated access to US bank ... more ABSTRACT A database driven multi-agent model has been developed with automated access to US bank level FDIC Call Reports that yield data on balance sheet and off balance sheet activity, respectively, in Residential Mortgage Backed Securities (RMBS) and Credit Default Swaps (CDS). The simultaneous accumulation of RMBS assets on US banks’ balance sheets and also large counterparty exposures from CDS positions characterized the $2 trillion Collateralized Debt Obligation (CDO) market. The latter imploded at the end of 2007 with large scale systemic risk consequences. Based on US FDIC bank data, that could have been available to the regulator at the time, the authors investigate how a CDS negative carry trade combined with incentives provided by Basel II and its precursor in the US, the Joint Agencies Rule 66 Federal Regulation No. 56914, which became effective on January 1, 2002, on synthetic securitization and Credit Risk Transfer (CRT), led to the unsustainable trends and systemic risk. The resultant market structure with heavy concentration in CDS activity involving 5 US banks can be shown to present too interconnected to fail systemic risk outcomes. The simulation package can generate the financial network of obligations of the US banks in the CDS market. The authors aim to show how such a Multi-Agent Financial Network (MAFN) model is well suited to monitor bank activity and to stress test policy for perverse incentives on an ongoing basis.
The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for f... more The Prime Minster Jan-Dhan Yojna (PMJDY), started in 2014, follows in a long line of drives for financial inclusion in India, marked only by a much greater scope and ambition than previous roll outs. This top down approach to close the gap on the unbanked of India relies primarily on public sector banks with targets set for rural outreach. We develop an innovative approach using cross sectional bank level data from 2014 till 2017 to quantify the incentives and costs involved in targeting unbanked households. This gives a monetary estimate of the economic shortfalls or surpluses for participating banks, measured as bank balances relative to outlay costs and subsidies per PMJDY beneficiary. We model the double bind problem faced by banks to achieve economies of scale that arise from spreading the fixed infrastructure costs over the number of below poverty line (BPL) customers when there is a dearth of balances in these accounts. This lack of economic viability of PMJDY accounts is found in most public sector banks, a matter which is problematic in view of their extant financial fragility in India. We provide evidence for cross subsidization of rural bank accounts by urban accounts. We give estimates using fixed effects panel methods as to what cost public sector banks bear and also quantify the extent to which account ineffectiveness is ameliorated with exogenous factors, primarily the tie up of PMJDY accounts with bio-metric Aadhar cards and electronic direct benefit transfer of G2P payments.
Self-referential calculations of oppositional or contrarian structures and the necessity to innov... more Self-referential calculations of oppositional or contrarian structures and the necessity to innovate to outsmart hostile agents in an arms race are ubiquitous in socio-economic systems, immunology and evolutionary biology. However, such phenomena with strategic innovation, which entails novel actions beyond listable sets, are outside the ambit of extant game theory. How can strategic innovation with novel actions be a Nash equilibrium of a game? Based on the only known Godel-Turing-Post (GTP) axiomatic framework on meta-analyses of offline simulations that involve recursive operations on encoded information, we show that mutually mentalising agents capable of such offline simulations can “think outside the box” and embark on an arms race in novelty or surprises. A key logical ingredient of this is the self-referential encoding of a proposition on mutual negation or opposition, often referred to as the Godel sentence. The only recursive best response function of a two-person game with an oppositional structure that can implement strategic innovation in a lock-step formation of an arms race is the productive function of the Emil Post set theoretic proof of the Godel incompleteness result.
ABSTRACT Relational neural networks as a concept offers a unique opportunity for improving classi... more ABSTRACT Relational neural networks as a concept offers a unique opportunity for improving classification accuracy by exploiting relational structure in data. The premise is that a relational classification technique, which uses information implicit in relationships, should classify more accurately than techniques that only examine objects in isolation. In this paper, we study the use of relational neural networks for predicting bank failure. Alongside classical financial ratios normally used as predictor variables, we introduced new relational variables for the network. The relational neural network structure, specified as a combination of feed forward and recurrent neural networks, is determined by bank data through neuro‐evolution. We discuss empirical results comparing performance of the relational approach to standard propositional methods used for bank failure prediction.
The complexity of the financial markets, represents a big challenge to the specialist in the area... more The complexity of the financial markets, represents a big challenge to the specialist in the area. The traditional way of coping with the analysis of such systems is the use of analytical models. However, the analytical models present some difficulties and this has leaded to the development of alternative methods for the analysis of such markets. In this paper we
Social and economic networks are becoming increasingly popular in the last ten years, because of ... more Social and economic networks are becoming increasingly popular in the last ten years, because of both the application of game theory to the network formation processes 4, and the study of stochastic processes that fit the statistical properties of real world social networks. 5 In the very recent years there have also been attempts to combine the contribution of these two streams of research, trying to find strategic models whose equilibria resemble the empirical data. 6 A well known source of debate in the game theoretical ...
Uploads
Papers by Sheri Markose