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Provisional Agenda for the Thirty-Ninth Meeting of the International Monetary and Financial Committee.
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Provisional Agenda for the Thirty-Ninth Meeting of the International Monetary and Financial Committee.
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Developing economies can strengthen their financial systems by implementing the main elements of global regulatory reform. But to build an effective prudential framework, they may need to adapt international standards taking into account the sophistication and size of their financial institutions, the relevance of different financial operations in their market, the granularity of information available and the capacity of their supervisors. Under a proportionate application of the Basel standards, smaller institutions with less complex business models would be subject to a simpler regulatory framework that enhances the resilience of the financial sector without generating disproportionate compliance costs. This paper provides guidance on how non-Basel Committee member countries could incorporate banks’ capital and liquidity standards into their framework. It builds on the experience gained by the authors in the course of their work in providing technical assistance on—and assessing compliance with—international standards in banking supervision.
Banks and Banking --- Finance: General --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financial Institutions and Services: Government Policy and Regulation --- General Financial Markets: Government Policy and Regulation --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Financial services law & regulation --- Banking --- Liquidity risk --- Liquidity requirements --- Basel III --- Basel Core Principles --- Banks and banking --- State supervision --- Financial risk management
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At the request of the National Statistics and Census Institute (INDEC), a technical assistance mission on external sector statistics (ESS) visited Buenos Aires during April 17–28, 2017. This was a follow up to the November 2016 mission that evaluated the ESS methodology, information sources, and dissemination policy and made recommendations to improve quality, adapt the production of ESS to the methodology provided by the Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6), and support the quarterly compilation and dissemination of the International Investment Position (IIP) in accordance with the Special Data Dissemination Standards (SDDS). The mission reviewed the implementation status of the tasks identified in the action plan prepared by the November 2016 mission; assisted compilers in preparing quarterly ESS in accordance with BPM6 guidelines for the next quarterly publication; and provided practical advice on the methodology to be used.
Argentina --- Economic conditions. --- Exports and Imports --- Macroeconomics --- Statistics --- Data Collection and Data Estimation Methodology --- Computer Programs: Other --- International Investment --- Long-term Capital Movements --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- International Lending and Debt Problems --- International economics --- Econometrics & economic statistics --- External sector statistics --- Foreign assets --- International investment position --- Private debt --- External debt --- Economic and financial statistics --- External position --- National accounts --- Economic statistics --- Investments, Foreign --- Debt --- Debts, External
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Financial Sector Assessment Program; Technical Note-Stress Testing the Banking Sector.
Monetary policy --- Banks and Banking --- Finance: General --- Industries: Financial Services --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Financial Institutions and Services: Government Policy and Regulation --- Banking --- Finance --- Financial services law & regulation --- Stress testing --- Credit risk --- Market risk --- Financial institutions --- Financial regulation and supervision --- Financial sector policy and analysis --- Loans --- Banks and banking --- Financial risk management --- Switzerland
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How to prevent runs on open-end mutual funds? In recent years, markets have observed an innovation that changed the way open-end funds are priced. Alternative pricing rules (known as swing pricing) adjust funds’ net asset values to pass on funds’ trading costs to transacting shareholders. Using unique data on investor transactions in U.K. corporate bond funds, we show that swing pricing eliminates the first-mover advantage arising from the traditional pricing rule and significantly reduces redemptions during stress periods. The positive impact of alternative pricing rules on fund flows reverses in calm periods when costs associated with higher tracking error dominate the pricing effect.
Banks and Banking --- Investments: Bonds --- Macroeconomics --- Industries: Financial Services --- Pension Funds --- Non-bank Financial Institutions --- Financial Instruments --- Institutional Investors --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- General Aggregative Models: General --- General Financial Markets: General (includes Measurement and Data) --- Price Level --- Inflation --- Deflation --- Financial services law & regulation --- Finance --- Investment & securities --- Liquidity risk --- Flow of funds --- Mutual funds --- Corporate bonds --- Price structures --- Financial regulation and supervision --- National accounts --- Financial institutions --- Prices --- Financial risk management --- Bonds --- United States
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This Detailed Assessment of Observance report specifies Base Core Principles (BCP) for effective banking supervision in Australia. An assessment of the effectiveness of banking supervision requires a review of the legal framework, and a detailed examination of the policies and practices of the institution(s) responsible for banking regulation and supervision. In line with the BCP methodology, the assessment focused on banking supervision and regulation in Australia and did not cover the specificities of regulation and supervision of other financial institutions. The assessment has made use of five categories to determine compliance: compliant; largely compliant, materially noncompliant, noncompliant, and non-applicable. The report insists that Australian Prudential Regulation Authority (APRA) should put more focus on assessing the various components of firms’ Internal Capital Adequacy Assessment Process and other firm-wide stress testing practices. A periodic more comprehensive assessment of banks’ risk management and governance frameworks will further enhance APRA’s supervisory approach.
Banks and Banking --- Finance: General --- Public Finance --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Financial Institutions and Services: Government Policy and Regulation --- Public Administration --- Public Sector Accounting and Audits --- Banking --- Financial services law & regulation --- Finance --- Management accounting & bookkeeping --- Credit risk --- Operational risk --- Market risk --- Stress testing --- Financial regulation and supervision --- Liquidity risk --- Internal audit --- Public financial management (PFM) --- Banks and banking --- Financial risk management --- Auditing, Internal --- Australia
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We explore empirically how the time-varying allocation of credit across firms with heterogeneous credit quality matters for financial stability outcomes. Using firm-level data for 55 countries over 1991-2016, we show that the riskiness of credit allocation, captured by Greenwood and Hanson (2013)’s ISS indicator, helps predict downside risks to GDP growth and systemic banking crises, two to three years ahead. Our analysis indicates that the riskiness of credit allocation is both a measure of corporate vulnerability and of investor sentiment. Economic forecasters wrongly predict a positive association between the riskiness of credit allocation and future growth, suggesting a flawed expectations process.
Financial risk. --- Business risk (Finance) --- Money risk (Finance) --- Risk --- Financial Risk Management --- Macroeconomics --- Money and Monetary Policy --- Financial Markets and the Macroeconomy --- Money and Interest Rates: Forecasting and Simulation --- Financial Crises --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Pension Funds --- Non-bank Financial Institutions --- Financial Instruments --- Institutional Investors --- Financial Institutions and Services: Government Policy and Regulation --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Business Fluctuations --- Cycles --- Monetary economics --- Economic & financial crises & disasters --- Credit --- Financial conditions index --- Credit booms --- Bank credit --- Financial crises --- Money --- Financial sector policy and analysis --- Business cycles --- United States
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Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Finance. --- Funding --- Funds --- Economics --- Currency question --- Banks and Banking --- Money and Monetary Policy --- Industries: Financial Services --- Intelligence (AI) & Semantics --- Model Evaluation and Selection --- Forecasting and Other Model Applications --- Large Data Sets: Modeling and Analysis --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Pension Funds --- Non-bank Financial Institutions --- Financial Instruments --- Institutional Investors --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Technological Change: Choices and Consequences --- Diffusion Processes --- Monetary economics --- Financial services law & regulation --- Finance --- Machine learning --- Credit risk --- Credit --- Credit ratings --- Loans --- Financial regulation and supervision --- Money --- Financial institutions --- Technology --- Financial risk management --- United States
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The paper presents a framework to integrate liquidity and solvency stress tests. An empirical study based on European bond trading data finds that asset sales haircuts depend on the total amount of assets sold and general liquidity conditions in the market. To account for variations in market liquidity, the study uses Markov regime-switching models and links haircuts with market volatility and the amount of securities sold by banks. The framework is accompanied by a Matlab program and an Excel-based tool, which allow the calculations to be replicated for any type of traded security and to be used for liquidity and solvency stress testing.
Economics--Mathematical models. --- Markov processes. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Banks and Banking --- Finance: General --- Financial Risk Management --- Investments: Bonds --- Banks --- Depository Institutions --- Micro Finance Institutions --- Mortgages --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Portfolio Choice --- Investment Decisions --- International Financial Markets --- General Financial Markets: General (includes Measurement and Data) --- Finance --- Financial services law & regulation --- Banking --- Investment & securities --- Liquidity --- Asset liquidity --- Liquidity risk --- Asset management --- Asset and liability management --- Financial regulation and supervision --- Sovereign bonds --- Financial institutions --- Economics --- Financial risk management --- Banks and banking --- Asset-liability management --- Bonds --- United States --- Mathematical models.
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