Download or read book Risk Measurement Econometrics and Neural Networks written by Georg Bol and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises the articles of the 6th Econometric Workshop in Karlsruhe, Germany. In the first part approaches from traditional econometrics and innovative methods from machine learning such as neural nets are applied to financial issues. Neural Networks are successfully applied to different areas such as debtor analysis, forecasting and corporate finance. In the second part various aspects from Value-at-Risk are discussed. The proceedings describe the legal framework, review the basics and discuss new approaches such as shortfall measures and credit risk.
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
Download or read book PREDICTIVE MODELS TO RISK ANALYSIS WITH NEURAL NETWORKS REGRESSION AND DECISION TREES written by and published by CESAR PEREZ. This book was released on with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential aim of this book is to use predictive models to analyze risk. Models of decision trees, regression and neural networks are used to predict various risk categories. This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models and neural network models to predict a continuous target. Successive chapters present examples that clarify the application of the models in the field of risk. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used. The book begins by introducing the basics of creating a project, manipulating data sources, and navigating through different results windows. Data Mining tools are used to build the main risk models: Decision Tree, Neural Network, and Regression.
Download or read book Risk Management written by Michael Frenkel and published by Springer Science & Business Media. This book was released on 2005-12-06 with total page 842 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with all aspects of risk management that have undergone significant innovation in recent years, this book aims at being a reference work in its field. Different to other books on the topic, it addresses the challenges and opportunities facing the different risk management types in banks, insurance companies, and the corporate sector. Due to the rising volatility in the financial markets as well as political and operational risks affecting the business sector in general, capital adequacy rules are equally important for non-financial companies. For the banking sector, the book emphasizes the modifications implied by the Basel II proposal. The volume has been written for academics as well as practitioners, in particular finance specialists. It is unique in bringing together such a wide array of experts and correspondingly offers a complete coverage of recent developments in risk management.
Download or read book Operations Research Proceedings 2002 written by Ulrike Leopold-Wildburger and published by Springer Science & Business Media. This book was released on 2003-02-24 with total page 572 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings volume contains a selection of papers presented at the International Conference on Operations Research (SOR 2002).The contributions cover the broad interdisciplinary spectrum of Operations Research and present recent advances in theory, development of methods, and applications in practice. Subjects covered are Production, Logistics and Supply Chain Production, Marketing and Data Analysis, Transportation and Traffic, Scheduling and Project Management, Telecommunication and Information Technology, Energy and Environment, Public Economy, Health, Agriculture, Education, Banking, Finance, Insurance, Risk Management, Continuous Optimization, Discrete and Combinatorial Optimization, Stochastic and Dynamic Programming, Simulation, Control Theory, Systems Dynamics, Dynamic Games, Game Theory, Auctioning and Bidding, Experimental Economics, Econometrics, Statistics and Mathematical Economics, Fuzzy Logic, Multicriteria Decision Making, Decision Theory.
Download or read book Scenario Analysis in Risk Management written by Bertrand K. Hassani and published by Springer. This book was released on 2016-10-26 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies.
Download or read book Tail Risk of Hedge Funds written by Gregor Aleksander Gawron and published by Cuvillier Verlag. This book was released on 2007 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Pacific Basin Business Economics and Finance written by Cheng-Few Lee and published by Emerald Group Publishing. This book was released on 2020-09-09 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Pacific Basin Business, Economics, and Finance is an annual publication designed to focus on interdisciplinary research in finance, economics, accounting and management among Pacific Rim countries.
Download or read book Floods in the Ganga Brahmaputra Meghna Delta written by Aznarul Islam and published by Springer Nature. This book was released on 2023-02-14 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume covers the floods of the major rivers of the Ganga-Brahmaputra-Meghna (GBM) Delta, and storm surge related coastal floods in these regions. The book is dedicated to addressing floods from an integrated physical-social perspective to provide students and researchers with a holistic understanding of floods in terms of both human and geomorphological aspects. The systematic coverage of all the major rivers and coastal areas in the GBM delta and surrounding regions will foster a clear comprehension of this dense reservoir of population, where thousands of people are impacted every year due to flood hazards and agricultural destabilization. This comprehensive treatment of flood issues in the region covers flash floods, fluvial floods, fluvio-tidal floods, and coastal floods, and outlines flood management strategies to maintain ecological integrity and environmental stability, and prevent harmful impacts of future floods. The book is intended for students and researchers in earth and environmental sciences, especially geomorphology, hydrology, geography, geology, natural resources management, and regional planning.
Download or read book Principles of Neural Model Identification Selection and Adequacy written by Achilleas Zapranis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly stochastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.
Download or read book Market Risk Quantification Using Neural Networks written by Harutyun Harutyunyan and published by . This book was released on 2002 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Financial Surveillance written by Marianne Frisen and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book-length treatment of statistical surveillance methods used in financial analysis. It contains carefully selected chapters written by specialists from both fields and strikes a balance between the financial and statistical worlds, enhancing future collaborations between the two areas, and enabling more successful prediction of financial market trends. The book discusses, in detail, schemes for different control charts and different linear and nonlinear time series models and applies methods to real data from worldwide markets, as well as including simulation studies.
Download or read book Econometric Modelling of European Money Demand written by Engelbert Plassmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of a single European currency constitutes a remarkable instance of internationalization of monetary policy. Whether a concomitant internationalization can be detected also in the econometric foundations of monetary policy is the topic dealt with in this book. The basic theoretical ingredients comprise a data-driven approach to econometric modelling and a generalized approach to cross-sectional aggregation. The empirical result is a data-consistent structural money demand function isolated within a properly identified, dynamic macroeconomic system for Europe. The book itself evolved from a research project within the former Son derforschungsbereich SFB 178 "Internationalization of the Economy" at the University of Konstanz. Its finalization entails a due amount of gratitude to be extended into several directions: I am personally indebted, first of all, to my academic supervisor, Professor Dr. Nikolaus Laufer, for originally inspiring this work and for meticulously perusing its eventual result. Professor Dr. Win fried Pohlmeier, as a second supervisor, provided valuable confidence bounds around an earlier draft. The comments of both supervisors contributed substantially to the present shape of the book. I am institutionally indebted to the University of Konstanz, notably its Faculty of Economics and Statistics, for continuous provision of an excellent research environment, and to the Deutsche Forschungsgemeinschaft in Bonn for generous sponsorship of the former SFB, whose financial support dur ing that period is gratefully acknowledged. I am also indebted to Dresdner Bank AG Frankfurt, Risk Methodology Trading, for benign tolerance of all distractions associated with the preparation of the final manuscript.
Download or read book Empirical Economic and Financial Research written by Jan Beran and published by Springer. This book was released on 2014-11-07 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to establish a connection between the traditional field of empirical economic research and the emerging area of empirical financial research and to build a bridge between theoretical developments in these areas and their application in practice. Accordingly, it covers broad topics in the theory and application of both empirical economic and financial research, including analysis of time series and the business cycle; different forecasting methods; new models for volatility, correlation and of high-frequency financial data and new approaches to panel regression, as well as a number of case studies. Most of the contributions reflect the state-of-art on the respective subject. The book offers a valuable reference work for researchers, university instructors, practitioners, government officials and graduate and post-graduate students, as well as an important resource for advanced seminars in empirical economic and financial research.
Download or read book Machine Learning in Banking Risk Management written by Mourine Atsien and published by GRIN Verlag. This book was released on 2023-02-20 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essay from the year 2022 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: A, , course: Business and technology, language: English, abstract: Technological applications are playing a more influential role in management in the contemporary business environment. Machine learning, artificial intelligence, and other algorithmic applications are some of the most common influencers in business applications. They present numerous solutions to business management problems, including banking risk management. In the last decade, risk management has gained greater prominence in financial services. In the past, banks focused on the detection, measuring, and reporting of risks. However, they are now leveraging on machine learning for greater accuracy and efficacy in risk management. As such, this paper explored different ways that machine learning applies in banking risk management. To achieve the objective of this study, the researcher conducted a comprehensive literature review on the topic of machine learning in banking risk management. The researcher found considerable industry and academic research focusing on developments in the financial services industry, especially in relation to risk management. It reviewed the literature, analysing and evaluating various risk management machine-learning techniques. It identified risk management problem areas and explored various ways of addressing them. The review showed that machine learning learning in risk management in financial services sector was still under-researched. While there were many studies on credit risks, other risks such as liquidity risks, market risks, and operational risks saw minimal attention. Nevertheless, machine learning applications were found to have the potential to develop more effective risk management models. Machine learning is leveraged on different data types to predict potential events with greater accuracy and estimate losses associated with different risk types. In addition, the machine learning techniques in risk management were found to provide better and more accurate results than traditional statistical models. Though machine learning suggests improving banking risk management, there are some areas that need further study. For instance, the paper suggested in-depth studies on machine learning models for different types of banking risks.
Download or read book Optimal Statistical Inference in Financial Engineering written by Masanobu Taniguchi and published by CRC Press. This book was released on 2007-11-26 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering literature, even though this idea is fundamental to the field. Balancing statistical theory with data analysis, Optimal Statistical Inference in Financial Engineering examines how stochastic models can effectively des
Download or read book Machine Learning for Financial Risk Management with Python written by Abdullah Karasan and published by . This book was released on 2021 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.
