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Asset-liability management

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This book relates the literatures of finance, industrial economics and investment to the theoretical framework of the 'credit view'. Firstly it is assumed that banks' decisions concerning their assets are seen as at least as relevant as their decisions concerning their liabilities. Secondly, securities and bank credit are considered to be highly imperfect substitutes. In this regard it is important to investigate the way industrial and financial sectors interact. In particular, how is the macroeconomy affected by the phenomenon of 'securitization' and by exogenous changes in the industrial structure of the credit market. The interactions between real and financial sectors are also analysed from the point of view of the industrial firm, in a model where the investment and financial decisions of the firm are taken simultaneously.

Credit --- Asset-liability management --- Macroeconomics --- 339 --- Economics --- Asset-liability management (Banking) --- Funds management --- Financial institutions --- Borrowing --- Finance --- Money --- Loans --- Management --- Investments --- Asset-liability management. --- Credit. --- Macroeconomics. --- Business, Economy and Management

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Reflecting the author's wealth of experience in this field, Handbook of Solvency for Actuaries and Risk Managers: Theory and Practice focuses on the valuation of assets and liabilities, the calculation of capital requirement, and the calculation of the standard formula for the European Solvency II project. The first three sections of the book examine the solvency concept, historical development, and the role of solvency in an enterprise risk management approach. The text provides a general discussion on valuation, investment, and capital, along with modeling and measuring. It also covers depen

Risk (Insurance) --- Asset-liability management --- Risk management --- Asset-liability management. --- Risk management. --- 368 --- Verzekeringswezen --- Risk (Insurance). --- 368 Verzekeringswezen --- Insurance --- Management --- Asset-liability management (Banking) --- Funds management --- Financial institutions --- Risk --- Investments --- Risk (Insurance) - European Union countries --- Asset-liability management - European Union countries --- Risk management - European Union countries

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In reaction to the financial market crisis that started in 2007, the Basel Committee on Banking Supervision substantially revised its existing framework for regulation, supervision and risk management in the banking sector. This revision was introduced with the so-called Basel III framework in December 2010. It essentially comprises a strengthening of the quality of a banks' own funds, as well as new requirements with regard to the amount of required capital. Furthermore, new ratios were introduced in order to limit the leverage employed by banks as well as new liquidity ratios. Since 2012 the Basel Committee has increasingly pursued a revision of the calculation methods for risk-weighted assets. In addition, a large number of new requirements have been developed. This package of new standards from the Basel Committee, which is unofficially called "Basel IV", is now the most comprehensive package of modifications in the history of banking supervision. It is only a matter of time until the innovations of the Basel IV package are transferred into binding EU law. The banking industry will face major challenges in implementing these new rules. In the editor's volume "Basel IV - The Next Generation of Risk Weighted Asset" Martin Neisen and Stefan Roth present the current edition of the Basel reform proposals. The aim is to convince the reader that we are facing a new framework called "Basel IV" and not just a fine adjustment of the existing Basel III regulations. Moreover, the innovations of the Basel IV package are explained in a clear, comprehensible and practical manner. With the aid of a high-profile team of experts, the complexity of the topic is reduced and important support is offered.

Banks and banking --- Bank management --- Asset-liability management --- Credit --- Risk management --- Commercial credit

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Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Quantitative methods (economics) --- Private finance --- Artificial intelligence. Robotics. Simulation. Graphics --- Asset-liability management --- Machine Learning --- Data processing. --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Asset-liability management (Banking) --- Funds management --- Financial institutions --- Data processing --- Management --- Investments --- Machine learning. --- Asset-liability management - Data processing.