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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.
Contingencies are unexpected crises or events that cause a major threat to the safety, security and well-being of a certain population. This research effort builds upon the work on contingency logistics reliability models by Miman (2008) who extended the preliminary work conducted by Thomas (2004) that provides the modeling approach which takes a mission success orientation and focuses on the ability to recover from or prevent a contingency logistics failure. Miman (2008) proposes the sustainability model of a contingency logistics network using the concept of selective maintenance. This problem, once formulated, is a non-convex, non-linear, non-separable, multi-dimensional, discrete knapsack problem. These problems are known to be NP hard. Therefore, one needs to explore heuristic solutions in search of robust and effective solution approaches. He developed a memetic algorithm, GAFTS, and proposed this for identifying the best set of maintenance actions to sustain the contingency logistics network. Besides, he used Physical Programming, a multi criteria optimization procedure, to exploit a network manager’s preference toward the numerous criteria (reliability, cost, time, resource utilization etc...) judiciously. This research effort continues the exploration of heuristic techniques for the sustainability model developed by Miman (2008) and develops a hybrid heuristics technique, EDGASA, incooperating simulating annealing (SA) procedure with genetic algorithm (GA). Comparisons of EDGASA with GA and SA reveal that it outperforms in terms of average reliability, best reliability and worst reliability found at an expense of increased solution time. One of the contributions of this study is a multi-objective modeling approach developed based on utopia distance that aims at minimizing the weighted distance between a solution to the ideal point that could be achieved. The study fills some of the voids in the contingency logistics networks’ solution and modeling and highlights potential studies by applying the hybrid heuristic developed as well as multiobjective modeling approach proposed to other problems.
Recent Developments in Asian Economics is a crucial resource of current, cutting-edge research for any scholar of international finance and economics. Chapters cover a wide range of topics, such as social welfare systems, organizational culture, sustainability, the impact of economic policy uncertainty, and more.
Much research into financial contagion and systematic risks has been motivated by the finding that cross-market correlations (resp. coexceedances) between asset returns increase significantly during crisis periods. Is this increase due to an exogenous shock common to all markets (interdependence) or due to certain types of transmission of shocks between markets (contagion)? Darolles and Gourieroux explain that an attempt to convey contagion and causality in a static framework can be flawed due to identification problems; they provide a more precise definition of the notion of shock to strengthen the solution within a dynamic framework. This book covers the standard practice for defining shocks in SVAR models, impulse response functions, identitification issues, static and dynamic models, leading to the challenges of measurement of systematic risk and contagion, with interpretations of hedge fund survival and market liquidity risks - Features the standard practice of defining shocks to models to help you to define impulse response and dynamic consequences - Shows that identification of shocks can be solved in a dynamic framework, even within a linear perspective - Helps you to apply the models to portfolio management, risk monitoring, and the analysis of financial stability
This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.
The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. These proceedings contain all of the papers that were presented.
This monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework. Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the context of additive Gaussian processes. It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.
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.
This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006. The 170 revised full papers presented were carefully selected from 557 submissions. The papers are organized in topical sections on learning and information processing, data mining, retrieval and management, bioinformatics and bio-inspired models, agents and hybrid systems, financial engineering, as well as a special session on nature-inspired date technologies.
This book showcases recent research advances in service science and related fields. Including selected papers from the 2022 INFORMS International Conference on Service Science, held in Shenzhen, China from July 2 to 4, 2022, the book presents new theories and empirical results in the emerging, interdisciplinary field of digital transformation and society. Incorporating research, education and practice alike, the respective chapters highlight a host of ways to approach these challenges in service science.