Financial Data Transparency, International Institutions, and Sovereign Borrowing Costs

Recent events in international finance illustrate the close connection between the viability of a country's major private financial institutions and the sustainability of its sovereign debt. We explore the precise nature of this connection and the ways in which it shapes investors’ expectations of sovereign creditworthiness. We consider how investors use the overall level of information available about the private financial sector—and the potential risks it poses to government finances—when making decisions about investing in sovereign debt. We expect that governments providing more information about the private financial sector will have lower, and less volatile, borrowing costs. In order to test this argument, we create a new Financial Data Transparency (FDT) Index measuring governments’ willingness to release credible financial system data. Using the FDT and a sample of high-income OECD countries, we find that such transparency reduces sovereign borrowing costs. The effects are conditional on the level of public indebtedness. Transparent countries with low debt enjoy lower and less volatile borrowing costs

Bank risk of failure and the too-big-to-fail policy

Banks and banking

Acquisition and production of skilled behavior in dynamic decision-making tasks: Modeling strategic behavior in human-automation interaction: Why and aid can (and should) go unused

Advances in computer and control technology offer the opportunity for task-offload aiding in human-machine systems. A task-offload aid (e.g., an autopilot, an intelligent assistant) can be selectively engaged by the human operator to dynamically delegate tasks to an automated system. Successful design and performance prediction in such systems requires knowledge of the factors influencing the strategy the operator develops and uses for managing interaction with the task-offload aid. A model is presented that shows how such strategies can be predicted as a function of three task context properties (frequency and duration of secondary tasks and costs of delaying secondary tasks) and three aid design properties (aid engagement and disengagement times, aid performance relative to human performance). Sensitivity analysis indicates how each of these contextual and design factors affect the optimal aid aid usage strategy and attainable system performance. The model is applied to understanding human-automation interaction in laboratory experiments on human supervisory control behavior. The laboratory task allowed subjects freedom to determine strategies for using an autopilot in a dynamic, multi-task environment. Modeling results suggested that many subjects may indeed have been acting appropriately by not using the autopilot in the way its designers intended. Although autopilot function was technically sound, this aid was not designed with due regard to the overall task context in which it was placed. These results demonstrate the need for additional research on how people may strategically manage their own resources, as well as those provided by automation, in an effort to keep workload and performance at acceptable levels

VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues.

Current trends in international banking supervision following the 1996 Amendment to the Basel Accord emphasise market risk control based upon internal Value-at-risk (VaR) models. This paper discusses the merits and drawbacks of VaR models in the light of their impact on market liquidity. After a preliminary review of basic concepts and measures regarding market risk, market friction and liquidity risk, the arguments supporting the internal models approach to supervision on market risk are discussed, in the light of the debate on the limitations and possible enhancements of VaR models. In particular, adverse systemic effects of widespread risk management practices are considered. Risk measurement models dealing with liquidity risk are then examined in detail, in order to verify their potential for application in the field. We conclude that VaR models are still far from effectively treating market and liquidity risk in their multi-faceted aspects. Regulatory guidelines are right in recognising the importance of internal risk control systems. Implementation of those guidelines might inadvertently encourage mechanic application of VaR models, with adverse systemic effects.

Dundee Discussion Papers in Economics 179:Activism, separation of powers and development

We consider a model of constitutional (mechanism) design with separation of powers where different institutions are assigned different tasks. In this context, we define activism as an institution extending its mechanism of decision-making into the domain of other institution’s tasks. When members of the institutions are likely to be benevolent as well as non-benevolent, such activism in a limited form reduces the cost of achieving collusion-proofness and raises welfare. Hence the value of such activism can be potentially very high in the context of developing economies. But as the fraction of non-benevolent member increases, such activism turns excessive and reduces welfare. It is argued that developing economies are likely to get caught in the excessive activism trap because of the high levels of corruption and bribery

Hyperplane Separation Technique for Multidimensional Mean-Payoff Games

We consider both finite-state game graphs and recursive game graphs (or pushdown game graphs), that can model the control flow of sequential programs with recursion, with multi-dimensional mean-payoff objectives. In pushdown games two types of strategies are relevant: global strategies, that depend on the entire global history; and modular strategies, that have only local memory and thus do not depend on the context of invocation. We present solutions to several fundamental algorithmic questions and our main contributions are as follows: (1) We show that finite-state multi-dimensional mean-payoff games can be solved in polynomial time if the number of dimensions and the maximal absolute value of the weight is fixed; whereas if the number of dimensions is arbitrary, then problem is already known to be coNP-complete. (2) We show that pushdown graphs with multi-dimensional mean-payoff objectives can be solved in polynomial time. (3) For pushdown games under global strategies both single and multi-dimensional mean-payoff objectives problems are known to be undecidable, and we show that under modular strategies the multi-dimensional problem is also undecidable (whereas under modular strategies the single dimensional problem is NP-complete). We show that if the number of modules, the number of exits, and the maximal absolute value of the weight is fixed, then pushdown games under modular strategies with single dimensional mean-payoff objectives can be solved in polynomial time, and if either of the number of exits or the number of modules is not bounded, then the problem is NP-hard. (4) Finally we show that a fixed parameter tractable algorithm for finite-state multi-dimensional mean-payoff games or pushdown games under modular strategies with single-dimensional mean-payoff objectives would imply the solution of the long-standing open problem of fixed parameter tractability of parity games.Comment: arXiv admin note: text overlap with arXiv:1201.282

From Security Enforcement to Supervisory Control in Discrete Event Systems: Qualitative and Quantitative Analyses

Cyber-physical systems are technological systems that involve physical components that are monitored and controlled by multiple computational units that exchange information through a communication network. Examples of cyber-physical systems arise in transportation, power, smart manufacturing, and other classes of systems that have a large degree of automation. Analysis and control of cyber-physical systems is an active area of research. The increasing demands for safety, security and performance improvement of cyber-physical systems put stringent constraints on their design and necessitate the use of formal model-based methods to synthesize control strategies that provably enforce required properties. This dissertation focuses on the higher level control logic in cyber-physical systems using the framework of discrete event systems. It tackles two classes of problems for discrete event systems. The first class of problems is related to system security. This problem is formulated in terms of the information flow property of opacity. In this part of the dissertation, an interface-based approach called insertion/edit function is developed to enforce opacity under the potential inference of malicious intruders that may or may not know the implementation of the insertion/edit function. The focus is the synthesis of insertion/edit functions that solve the opacity enforcement problem in the framework of qualitative and quantitative games on finite graphs. The second problem treated in the dissertation is that of performance optimization in the context of supervisory control under partial observation. This problem is transformed to a two-player quantitative game and an information structure where the game is played is constructed. A novel approach to synthesize supervisors by solving the game is developed. The main contributions of this dissertation are grouped into the following five categories. (i) The transformation of the formulated opacity enforcement and supervisory control problems to games on finite graphs provides a systematic way of performing worst case analysis in design of discrete event systems. (ii) These games have state spaces that are as compact as possible using the notion of information states in each corresponding problem. (iii) A formal model-based approach is employed in the entire dissertation, which results in provably correct solutions. (iv) The approaches developed in this dissertation reveal the interconnection between control theory and formal methods. (v) The results in this dissertation are applicable to many types of cyber-physical systems with security-critical and performance-aware requirements.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150002/1/jiyiding_1.pd

Has minority foreign investment in China�s banks improved their cost efficiency?

Since 2001, foreign investors have been permitted to acquire minority ownership stakes in China�s banks. This paper assesses whether there is any evidence of a cost efficiency payoff in those banks that have taken on foreign investment. Data Envelopment Analysis is first used to generate measures of cost efficiency for China�s banks over the period 2001-2006. A second stage regression is then performed to determine whether foreign investment has an impact on cost efficiency. The results indicate a positive impact, although one that is only marginally significant. Policy implications are discussed.

The Responsibility Quantification (ResQu) Model of Human Interaction with Automation

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation