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Author: Mr.Jorge A. Chan-Lau
Publisher: International Monetary Fund
ISBN: 1475598629
Category : Business & Economics
Languages : en
Pages : 48
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Book Description
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
Author: Mr.Jorge A. Chan-Lau
Publisher: International Monetary Fund
ISBN: 1475598629
Category : Business & Economics
Languages : en
Pages : 48
Get Book Here
Book Description
Diebold and Yilmaz (2015) recently introduced variance decomposition networks as tools for quantifying and ranking the systemic risk of individual firms. The nature of these networks and their implied rankings depend on the choice decomposition method. The standard choice is the order invariant generalized forecast error variance decomposition of Pesaran and Shin (1998). The shares of the forecast error variation, however, do not add to unity, making difficult to compare risk ratings and risks contributions at two different points in time. As a solution, this paper suggests using the Lanne-Nyberg (2016) decomposition, which shares the order invariance property. To illustrate the differences between both decomposition methods, I analyzed the global financial system during 2001 – 2016. The analysis shows that different decomposition methods yield substantially different systemic risk and vulnerability rankings. This suggests caution is warranted when using rankings and risk contributions for guiding financial regulation and economic policy.
Author: Ms.Isabelle Mejean
Publisher: International Monetary Fund
ISBN: 1451962347
Category : Business & Economics
Languages : en
Pages : 33
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Book Description
Economists interested in location choices usually focus their attention on investments abroad. This neglects the fact that multinational enterprises continue to invest domestically while undertaking foreign expansion. This paper compares investments at home and abroad. Our firm-level dataset shows an important home bias in productive investments. Part of this "excessive" domestic investment is explained by standard determinants of location choices. The interdependence between affiliates of the same industrial group however accounts for the lion's share of the home bias. Moreover, French firms' propensity to invest abroad is positively related to their productivity and the size of their intangible assets.
Author: Michel Denuit
Publisher: Springer Nature
ISBN: 3030258270
Category : Business & Economics
Languages : en
Pages : 258
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Book Description
This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
Author: Yu Hen Hu
Publisher: CRC Press
ISBN: 1351836307
Category : Technology & Engineering
Languages : en
Pages : 417
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Book Description
The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view. The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.
Author: Mrs.Jana Bricco
Publisher: International Monetary Fund
ISBN: 1513516221
Category : Business & Economics
Languages : en
Pages : 49
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Book Description
The analysis of interconnectedness and contagion is an important part of the financial stability and risk assessment of a country’s financial system. This paper offers detailed and practical guidance on how to conduct a comprehensive analysis of interconnectedness and contagion for a country’s financial system under various circumstances. We survey current approaches at the IMF for analyzing interconnectedness within the interbank, cross-sector and cross-border dimensions through an overview and examples of the data and methodologies used in the Financial Sector Assessment Program. Finally, this paper offers practical advice on how to interpret results and discusses potential financial stability policy recommendations that can be drawn from this type of in-depth analysis.
Author: Surajit Borkotokey
Publisher: Springer Nature
ISBN: 9811647372
Category : Mathematics
Languages : en
Pages : 372
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Book Description
This book is a collection of selected papers presented at the consecutively held international conferences on “Game Theory and Networks”, organized by the Department of Mathematics, Dibrugarh University, India, in collaboration with the Economics Department of Queen’s University, Belfast, UK, during September 6–9, 2019 and September, 13–15 2018. The book includes chapters on network measures and network formation, application of network theory to contagion, biological data and finance and macroeconomics as expository articles. The book also contains chapters on fair allocation in the context of queuing, rationing and cooperative games with transferable utilities for engaged researchers. A few survey chapters on non-cooperative game theory, evolutionary game theory, mechanism design and social choice theory are also incorporated to cater to the needs of the beginners in the field. This book discusses the use of game theoretic tools and network models across disciplines: mathematics, statistics, economics, computer science, political science, sociology and psychology. It aims at providing a suitable learning experience to beginners on the basics of cooperative games, networks and mechanism design, as well as recent developments to research scholars having the basic knowledge of these topics.
Author: Jianguo Liu
Publisher: Frontiers Media SA
ISBN: 2832532381
Category : Science
Languages : en
Pages : 115
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Book Description
Author: Francis X. Diebold
Publisher: Oxford University Press
ISBN: 0199338329
Category : Business & Economics
Languages : en
Pages : 285
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Book Description
Connections among different assets, asset classes, portfolios, and the stocks of individual institutions are critical in examining financial markets. Interest in financial markets implies interest in underlying macroeconomic fundamentals. In Financial and Macroeconomic Connectedness, Frank Diebold and Kamil Yilmaz propose a simple framework for defining, measuring, and monitoring connectedness, which is central to finance and macroeconomics. These measures of connectedness are theoretically rigorous yet empirically relevant. The approach to connectedness proposed by the authors is intimately related to the familiar econometric notion of variance decomposition. The full set of variance decompositions from vector auto-regressions produces the core of the 'connectedness table.' The connectedness table makes clear how one can begin with the most disaggregated pair-wise directional connectedness measures and aggregate them in various ways to obtain total connectedness measures. The authors also show that variance decompositions define weighted, directed networks, so that these proposed connectedness measures are intimately related to key measures of connectedness used in the network literature. After describing their methods in the first part of the book, the authors proceed to characterize daily return and volatility connectedness across major asset (stock, bond, foreign exchange and commodity) markets as well as the financial institutions within the U.S. and across countries since late 1990s. These specific measures of volatility connectedness show that stock markets played a critical role in spreading the volatility shocks from the U.S. to other countries. Furthermore, while the return connectedness across stock markets increased gradually over time the volatility connectedness measures were subject to significant jumps during major crisis events. This book examines not only financial connectedness, but also real fundamental connectedness. In particular, the authors show that global business cycle connectedness is economically significant and time-varying, that the U.S. has disproportionately high connectedness to others, and that pairwise country connectedness is inversely related to bilateral trade surpluses.
Author: Allen Kent
Publisher: CRC Press
ISBN: 9780824722951
Category : Computers
Languages : en
Pages : 408
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Book Description
Combining Artificial Neural Networks to Symbolic and Algebraic computation
Author: Ke-Lin Du
Publisher: Springer Nature
ISBN: 1447174526
Category : Mathematics
Languages : en
Pages : 996
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Book Description
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
