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This book is an introduction to the modelling of cash collateralised debt obligations (“CDOs”).  It is intended that the reader have a basic understanding of CDOs and a basic working knowledge of Microsoft Office Excel.  There will be written explanations of concepts along with understandable mathematical explanations and examples provided in Excel. 

Darren Smith

Pamela Winchie

Cash CDO Modelling in Excel

A Step by Step Approach

Foreword.  &lt;p&gt;Acknowledgments.&lt;/p&gt; &lt;p&gt;&lt;b&gt;1 Introduction.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;1.1 To Excel or Not to Excel?&lt;/p&gt; &lt;p&gt;1.2 Existing Tools and Software.&lt;/p&gt; &lt;p&gt;&lt;b&gt;2 What is a Cash CDOs?&lt;/b&gt;&lt;/p&gt; &lt;p&gt;2.1 Types of CDOs.&lt;/p&gt; &lt;p&gt;2.2 Description of a Cash Flow CDO.&lt;/p&gt; &lt;p&gt;2.3 Life Cycle of a Cash CDO.&lt;/p&gt; &lt;p&gt;2.4 Contribution to the “Credit Crunch”.&lt;/p&gt; &lt;p&gt;&lt;b&gt;3 Introduction to Modelling.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;3.1 Goals in Modelling.&lt;/p&gt; &lt;p&gt;3.2 Modelling Philosophies and Trade-Offs.&lt;/p&gt; &lt;p&gt;3.3 Flexibility.&lt;/p&gt; &lt;p&gt;3.4 Organization and Layout of a Model.&lt;/p&gt; &lt;p&gt;3.5 Life-Cycle Issues: Building an Adaptable Model.&lt;/p&gt; &lt;p&gt;&lt;b&gt;4 Prerequisites to Cash Flow Modelling.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;4.1 Modelling Dates.&lt;/p&gt; &lt;p&gt;4.2 Interest Rate Curve Modelling.&lt;/p&gt; &lt;p&gt;4.3 Present Value Modelling.&lt;/p&gt; &lt;p&gt;&lt;b&gt;5 Getting Started.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;5.1 Create the Input Sheet.&lt;/p&gt; &lt;p&gt;5.2 The Value of Labelling.&lt;/p&gt; &lt;p&gt;&lt;b&gt;6 Modelling Assets.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;6.1 Initial Asset Pool: Rep Line Modelling vs. Actual Assets.&lt;/p&gt; &lt;p&gt;6.2 The Collateral Sheet in the Cash Flow Model.&lt;/p&gt; &lt;p&gt;6.3 Modelling Defaults and Recoveries.&lt;/p&gt; &lt;p&gt;6.4 Amortization.&lt;/p&gt; &lt;p&gt;6.5 Modelling Reinvestment.&lt;/p&gt; &lt;p&gt;6.6 Reinvestment Cohorts.&lt;/p&gt; &lt;p&gt;6.7 Accounts.&lt;/p&gt; &lt;p&gt;6.8 Timing Models vs. Actual Timing.&lt;/p&gt; &lt;p&gt;6.9 Simple Warehouse Modelling.&lt;/p&gt; &lt;p&gt;&lt;b&gt;7 Basic Waterfall Modelling.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;7.1 Basic Waterfalls.&lt;/p&gt; &lt;p&gt;7.2 Layout and Design.&lt;/p&gt; &lt;p&gt;7.3 Avoiding Negative Values.&lt;/p&gt; &lt;p&gt;7.4 Timing Modelled vs. Actual Timing.&lt;/p&gt; &lt;p&gt;7.5 Liabilities Cash Flows.&lt;/p&gt; &lt;p&gt;7.6 Fees and Expenses Cash Flows.&lt;/p&gt; &lt;p&gt;7.7 Interest Waterfall.&lt;/p&gt; &lt;p&gt;7.8 Interest Waterfall (Available Funds after Payment).&lt;/p&gt; &lt;p&gt;7.9 Interest Waterfall Calculations.&lt;/p&gt; &lt;p&gt;7.10 Principal Waterfall.&lt;/p&gt; &lt;p&gt;7.11 Principle Waterfall (Available Funds after Payment).&lt;/p&gt; &lt;p&gt;7.12 Principal Waterfall Calculations.&lt;/p&gt; &lt;p&gt;7.13 Adding Over-Collaterization Tests.&lt;/p&gt; &lt;p&gt;7.14 Adding Interest Coverage Tests.&lt;/p&gt; &lt;p&gt;7.15 Technical Issues with Coverage Tests.&lt;/p&gt; &lt;p&gt;&lt;b&gt;8 Outputs Sheet.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;8.1 Purpose of the Outputs Sheet.&lt;/p&gt; &lt;p&gt;8.2 Collating Waterfall Outputs.&lt;/p&gt; &lt;p&gt;8.3 Present Value.&lt;/p&gt; &lt;p&gt;8.4 Duration.&lt;/p&gt; &lt;p&gt;8.5 Weighted Average Life and Internal Rate of Return.&lt;/p&gt; &lt;p&gt;8.6 Equity Analysis.&lt;/p&gt; &lt;p&gt;8.7 Basic Auditing.&lt;/p&gt; &lt;p&gt;&lt;b&gt;9 Moody’s Rating Agency Methodology.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;9.1 Introduction to Agency Methodologies.&lt;/p&gt; &lt;p&gt;9.2 The Bet Approach.&lt;/p&gt; &lt;p&gt;9.3 Evaluating the Collateral.&lt;/p&gt; &lt;p&gt;9.4 Creating the Moody’s Sheet and Related References in the Cash Flow Model.&lt;/p&gt; &lt;p&gt;9.5 Default Profiles.&lt;/p&gt; &lt;p&gt;9.6 Interest Rate Profiles.&lt;/p&gt; &lt;p&gt;9.7 Running the Analysis.&lt;/p&gt; &lt;p&gt;9.8 Variations on the BET.&lt;/p&gt; &lt;p&gt;9.9 2009 Methodology Update.&lt;/p&gt; &lt;p&gt;&lt;b&gt;10 Standard &amp; Poor’s Rating Methodology.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;10.1 The S&amp;P Approach.&lt;/p&gt; &lt;p&gt;10.2 Evaluating the Collateral.&lt;/p&gt; &lt;p&gt;10.3 Modelling Recovery Rates.&lt;/p&gt; &lt;p&gt;10.4 CDO Evaluator.&lt;/p&gt; &lt;p&gt;10.5 Default Rates.&lt;/p&gt; &lt;p&gt;10.6 Interest Rate Stresses.&lt;/p&gt; &lt;p&gt;10.7 Amortization.&lt;/p&gt; &lt;p&gt;10.8 Additional S&amp;P Modelling Criteria.&lt;/p&gt; &lt;p&gt;10.9 Building the S7P Sheet and Related References.&lt;/p&gt; &lt;p&gt;10.10 Running the Stress Scenarios.&lt;/p&gt; &lt;p&gt;&lt;b&gt;11 Advanced Waterfall Modelling.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;11.1 Hedge Agreements.&lt;/p&gt; &lt;p&gt;11.2 Fixed Notes.&lt;/p&gt; &lt;p&gt;11.3 Variable Funding Notes.&lt;/p&gt; &lt;p&gt;11.4 Liquidity Facilities.&lt;/p&gt; &lt;p&gt;11.5 Interest Reserve Accounts.&lt;/p&gt; &lt;p&gt;11.6 Other Structural Features.&lt;/p&gt; &lt;p&gt;11.7 Combination Notes.&lt;/p&gt; &lt;p&gt;11.8 Collateral Manager Equity Analysis.&lt;/p&gt; &lt;p&gt;&lt;b&gt;12 Maintaining the Cash Flow Model.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;12.1 Adapting Your Model for Different Capital Structures.&lt;/p&gt; &lt;p&gt;12.2 Audit Sheet.&lt;/p&gt; &lt;p&gt;12.3 Debugging.&lt;/p&gt; &lt;p&gt;&lt;b&gt;13 Advanced Structuring Issues.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;13.1 Projecting Accrued Interest.&lt;/p&gt; &lt;p&gt;13.2 Collating Collateral Cash Flows.&lt;/p&gt; &lt;p&gt;&lt;b&gt;14 Sourcing and Integrating Data From External Systems.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;14.1 Data Requirements.&lt;/p&gt; &lt;p&gt;14.2 Trustee Reports.&lt;/p&gt; &lt;p&gt;14.3 Bloomberg.&lt;/p&gt; &lt;p&gt;14.4 Loan Level Information Sources.&lt;/p&gt; &lt;p&gt;&lt;b&gt;15 Regulatory Applications of CDO Technology.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;15.1 The Basel Accords.&lt;/p&gt; &lt;p&gt;15.2 Regulatory Capital Requirements for CDO Notes.&lt;/p&gt; &lt;p&gt;15.3 The Standardized Approach for CDOs.&lt;/p&gt; &lt;p&gt;15.4 The Internal Ratings-Based Approach for CDOs.&lt;/p&gt; &lt;p&gt;15.5 The Internal Ratings-Based Approach for CDOs: The Ratings-Based Approach.&lt;/p&gt; &lt;p&gt;15.6 The Internal Ratings-Based Approach for CDOs: The Supervisory Formula Approach.&lt;/p&gt; &lt;p&gt;15.7 The Internal Ratings-Based Approach: Liquidity Facilities, Overlapping Exposures, Credit Risk Mitigation and Early Amortization Features.&lt;/p&gt; &lt;p&gt;15.8 Supervisory Provisions.&lt;/p&gt; &lt;p&gt;15.9 Updates to Basel II.&lt;/p&gt; &lt;p&gt;&lt;b&gt;16 CDO Valuation.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;16.1 Introduction.&lt;/p&gt; &lt;p&gt;16.2 Basic Valuation Approaches.&lt;/p&gt; &lt;p&gt;16.3 Traditional Underwriter Analysis.&lt;/p&gt; &lt;p&gt;16.4 Fundamental Cash Flow Analysis.&lt;/p&gt; &lt;p&gt;16.5 Using Rating Agency Models.&lt;/p&gt; &lt;p&gt;16.6 Transition Matrices.&lt;/p&gt; &lt;p&gt;16.7 Conclusion.&lt;/p&gt; &lt;p&gt;&lt;b&gt;17 In Conclusion.&lt;/b&gt;&lt;/p&gt; &lt;p&gt;Index.&lt;/p&gt;

The English Edition

In England, at the turn of the 18th century, the young and beautiful Elizabeth Bennet is faced with a major life decision: the choice of a suitable husband. The smug Mr. Darcy is hardly an option, but the charming Mr. Wickham might be. Elizabeth's overly proud nature obscures her vision again and again. Will she still be able to find happiness in love?Filled with witty dialogue and sharp social observations, Jane Austen's famous love story is one of the best-loved novels of all time. A hardcover edition embossed with silver foil. <ul><li>English Edition for the anniversary year 2025: Jane Austen's 250th birthday</li><li>Jane Austen was born on December 16th, 1775</li><li>»Do anything rather than marry without affection.« Jane Bennet</li><li>»I have not the pleasure of understanding you.« Mr Bennet in »Pride and Prejudice«</li><li>»I could easily forgive his pride, if he had not mortified mine.« Elizabeth Bennet in »Pride and Prejudice«</li><li>»I declare after all there is no enjoyment like reading! How much sooner one tires of anything than of a book!« Miss Caroline Bingley in »Pride and Prejudice«</li></ul>

A special edition hardcover embossed with silver foil

Austen - Pride and Prejudice. English Edition

Cryptocurrencies and digital assets have continued to gain widespread acceptance from both retail and institutional investors. Alongside this rapid growth, the digital ecosystem remains plagued by bad actors who prey on the space. These range from state-sponsored North Korean hacker groups, Russian ransomware gangs, multinational organized criminal enterprises to lower-level Chinese gangsters and run-of-the-mill romance scammers. These criminals indiscriminately target victims across all demographics. Furthermore, cryptocurrency-related scams continue to present increasingly significant threats to institutions and national security. Since the publication of&nbsp;The Cryptocurrency and Digital Asset Fraud Casebook, volumes I and II, scammers have only intensified the sophistication and scope of their attacks. This evolving threat landscape, coupled with a rapidly changing regulatory environment, underscores the ongoing need for up-to-date information and analysis. The Cryptocurrency and Digital Asset Fraud Casebook: Exchange Hacks, Deepfakes, Social Media, and Artificial Intelligence Scams, volume III, is essential reading for anyone seeking the latest insights into fraudulent activity within the cryptocurrency and digital asset space. Jason Scharfman is the Managing Partner of Corgentum Consulting, a specialist consulting firm that performs compliance consulting, due diligence reviews, and investigations of cryptocurrency projects, digital assets, and fund managers of all types, including cryptocurrency hedge funds, private equity, and venture capital. Mr. Scharfman has previous experience advising regulators and government institutions, as well as working on behalf of institutional investors and venture capital firms. He is recognized as one of the leading experts in the fields of digital asset and cryptocurrency compliance, fund operations, and due diligence.&nbsp;

Cryptocurrencies and digital assets have continued to gain widespread acceptance from both retail and institutional investors. Alongside this rapid growth, the digital ecosystem remains plagued by bad actors who prey on the space. These range from state-sponsored North Korean hacker groups, Russian ransomware gangs, multinational organized criminal enterprises to lower-level Chinese gangsters and run of the mill romance scammers. These criminals indiscriminately target victims across all demographics. Furthermore, cryptocurrency-related scams continue to present increasingly significant threats to institutions and national security.Since the publication of The Cryptocurrency and Digital Asset Fraud Casebook volumes I and II, scammers have only intensified the sophistication and scope of their attacks. This evolving threat landscape, coupled with a rapidly changing regulatory environment, underscores the ongoing need for up-to-date information and analysis. The Cryptocurrency and Digital Asset Fraud Casebook, Volume III: Exchange Hacks, Deepfakes, Social Media, and Artificial Intelligence Scams is essential reading for anyone seeking the latest insights into fraudulent activity within the cryptocurrency and digital asset space.

Exchange Hacks, Deepfakes, Social Media, and Artificial Intelligence Scams

The Cryptocurrency and Digital Asset Fraud Casebook, Volume III

Austen - Sense and Sensibility

Wiley Finance

PRAISE FOR FINANCIAL DATA ANALYTICS "Really interesting, and an impressive masterpiece! Financial Data Analytics contains a rich amount of material, with original research findings in almost every chapter; many parts of the book will even be directly helpful for my own teaching in business school. In view of its dedication towards data-driven analytical tools genuinely needed in financial problems, I believe that it is the very book that defines the scope of financial data analytics." -Alain Bensoussan, Fellow of AMS, IEEE, and SIAM; President of INRIA (1984-1996); President of CNES (Centre National d'Etudes Spatiales) (1996-2003); Chairman of ESA Council (European Space Agency) (1999-2002); Former Member of Advisory Board, Mathematical Finance; Lars Magnus Ericsson Chair Professor of Management, Naveen Jindal School of Management, University of Texas at Dallas "Financial Data Analytics is an exceptional book that integrates mathematics, practical examples, and real-life scenarios. With its focus on real datasets and practical programming codes in Python and R, the book offers a comprehensive exploration of various topics. It presents novel research findings and provides valuable insights for researchers, practitioners, and actuarial students. The book strikes a balance between foundational concepts and advanced techniques, making it an invaluable reference for professionals in the field ... By redefining the landscape of financial data analytics in FinTech and InsurTech, this book establishes itself as a trusted guide in the industry." - Simon Lam, Fellow of SOA, CFA, FRM; President of The Actuarial Society of Hong Kong (2018, 2023); Deputy CEO &amp; General Manager, Munich Re (Hong Kong) "The book will certainly play an impactful role in the advancement of financial analytics and should be on the bookshelf of every serious student of the topic." - Wai Keung Li, Fellow of Am. Stat. Assoc. and Inst. Math. Stat.; Emeritus Professor, The University of Hong Kong; Dean, Faculty of Liberal Arts and Social Sciences, The Education University of Hong Kong "... The dual focus on theory and applications, together with the discussion on recent advancements of the fields, makes the book one of a kind, even field-defining, among books on similar topics, and an ideal resource for anyone interested in understanding and implementing statistical models in this era of big data, as well as for students preparing for professional examinations on data analytics, such as the SRM, PA and ATPA exams of the Society of Actuaries." - Ambrose Lo, Fellow of SOA, Chartered Enterprise Risk Analyst; Author of ACTEX Study Manual for SOA Exam SRM, ACTEX Study Manual for SOA Exam PA, and ACTEX Study Manual for SOA Exam ATPA "... Financial Data Analytics is one comprehensive biblical handbook for academic researchers, financial practitioners, and graduate students for both methodologies and applications. The book also lays a systematic framework for future extension and enrichment for financial data analytics." - Nai-pan Tang, Former Chief Risk Officer and Member of Executive Committee, Hang Seng Bank; Former Deputy CEO and Chief Risk Officer, Shanghai Commercial Bank Ltd.; Former Director of the Board, Deputy CEO, Alternative CEO, Chief Risk Officer, and Vice Chairman of Asset Management, China CITIC Bank International; Director, The Hong Kong Institute of Bankers (2019-2021); Professor of Practice, Department of Finance, Chinese University of Hong Kong

An essential introduction to data analytics and Machine Learning techniques in the business sector

In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs--especially of key results--and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves.

The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) &amp; Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems.

The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech.

After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction.

This book can help readers become well-equipped with the following skills:
* To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions
* To apply effective data dimension reduction tools to enhance supervised learning
* To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose

The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam.

Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.

Financial Data Analytics with Machine Learning, Optimization and Statistics

Reclam 100 Seiten

Was ist Geld überhaupt? – Die kompakte Einführung zu allen Fragen rund um Währung, Inflation, Schulden & Co

Geld. 100 Seiten

Ein Irland-Liebesroman zum Wohlfühlen

Große Träume in der kleinen Bücherei der Herzen

“A must-read for serious investors, this book brings scientific rigor to trend following. The authors cut through the noise with a systematic, research-backed approach to moving averages. An invaluable resource for practitioners and academics alike.” —Wesley R. Gray, Ph.D., CEO of Alpha Architect&nbsp;“In The Ultimate Moving Average Handbook, Zakamulin and Giner employ academic rigor to transform the art of trend following into a science. By challenging conventional thought and exposing the myths surrounding so-called superior moving averages, they provide traders and researchers with an objective, data-driven framework for evaluating and optimizing trend-based trading rules. This book should serve as a guidepost for making more informed investment decisions. It is an essential read for anyone serious about systematic investing and technical analysis.”&nbsp;—Gary Antonacci, author of Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk&nbsp;“While claiming to be academic, this book provides a fountain of information for trend followers with techniques that have been overlooked. I was particularly interested in the zero-lag models and the calculation frequency.&nbsp; It is remarkably readable and a great achievement.”—Perry Kaufman, author of the classic Trading Systems and Methods book&nbsp;“This book is a tour de force that extends and accelerates the important trend in technical analysis toward scientific rigor and empirical evidence. Moving averages—one of the field’s key tools—are covered in all their forms and from every angle, making this required reading for every serious technical analyst. Most importantly, moving averages are evaluated within a rules-based framework—an important and highly pragmatic approach to the subject.”—David Aronson, author of Evidence-Based Technical Analysis book&nbsp;“In an era dominated by AI, writing a book about the humble moving average is nothing short of audacious. But here’s why it matters: MA-driven investment strategies consistently deliver rock-solid out-of-sample results—frequently outpacing AI-powered approaches. And here’s the secret: many systematic fund managers quietly agree. While MAs may seem simple, this book goes beyond the surface, digging deep and leaving no stone unturned. If systematic trading is your game, consider this your goldmine of insights—packed with fresh ideas to supercharge your strategies.”—Dr Tom Starke, Founder of AAAQuants&nbsp;Valeriy Zakamulin is a Finance Professor at the University of Agder, Norway, whose research focuses on asset pricing, portfolio management, and technical trading strategies.Javier Giner is a Senior Lecturer in Financial Economics at the University of La Laguna, Spain, with research spanning quantitative methods in both acoustics and finance.

Bringing Science into the Art of Trend Following

The Ultimate Moving Average Handbook

Dieses Lehrbuch stellt die wesentlichen Methoden und Anwendungsgebiete der Künstlichen Intelligenz und der Analyse großer Datenmengen bei Finanzintermediären und Industrieunternehmen im Bereich der Finanzwirtschaft dar. Es&nbsp;vermittelt die Grundlagen und fortgeschrittene Methoden der KI und ihrer Anwendungen in der Finanzwirtschaft und&nbsp;unterstützt bei der Vorbereitung auf das Berufsleben im quantitativen Asset- und Risikomanagement. Das Buch kann begleitend zu universitären Lehrveranstaltungen im Bereich Financial Data Analytics, Risiko- und Investmentmanagement benutzt werden.

Daten, Methoden und Anwendungen

Künstliche Intelligenz in der Finanzwirtschaft

Theory and Practical Applications

Bayesian Machine Learning in Quantitative Finance

Springer Texts in Business and Economics

This textbook rethinks derivatives in a structured manner. This is done from the perspective of financial modeling, whereby the central issues from the financial economy are holistically mapped and resolved with the help of Microsoft Excel. The guideline of the book is an integrated case study, which is divided into two parts. The first part deals with the basics and the valuation of options and futures. In the second part, the individual option strategies are examined step by step in separate course units. The analysis ends – figuratively speaking – in a cockpit of sorts, where the reader controls the possible strategies in Excel. The book is aimed at students of business administration with a focus on finance.

Options and Futures

Derivatives

Nicht erst seit Ausbruch der Corona-Pandemie scheinen Bargeldzahlungen die Wurzel allen Übels zu sein. Unter dem Deckmantel der Kriminalitätsbekämpfung drängt die EU-Kommission auf eine europaweite Bargeldobergrenze. Deutsche Politiker überbieten sich mit immer neuen Forderungen bis hin zu Limits von maximal 10.000 Euro. Dass der Zwang zum digitalen Bezahlen am Ende gar nicht zu weniger Kriminalität führt, scheint die Gegner des Bargelds gar nicht zu interessieren, obgleich genau das oftmals ihr einziges Argument bleibt. Steht letztlich sogar die Abschaffung unseres Bargeldes auf dem Plan? Das wäre fatal, denn sicheres, allgemein verfügbares, anonymes, kostenfreies und stabiles Geld ist eine der zentralen Säulen unserer freiheitlichen Wirtschaftsordnung.
 
 
 Schon eine deutliche Begrenzung von Bargeldzahlungen ermöglicht den Kontrollstaat. Jeder kann durch den Staat nach Belieben diszipliniert werden. Ohne Bargeld gibt es keine wirkliche Freiheit und bankrotte westliche Industrienationen können leichter eine Währungsreform durchsetzen und sich auf Kosten der Bürger entschulden. Prof. Dr. Max Otte, profilierter Finanzmarktexperte und mehrfacher Bestsellerautor, erklärt in seiner Streitschrift, warum jeder gegen das drohende Bargeldverbot kämpfen muss und was jeder persönlich tun kann.

Nicht erst seit Ausbruch der Corona-Pandemie scheinen Bargeldzahlungen die Wurzel allen Übels zu sein. Unter dem Deckmantel der Kriminalitätsbekämpfung drängt die EU-Kommission auf eine europaweite Bargeldobergrenze. Deutsche Politiker überbieten sich mit immer neuen Forderungen bis hin zu Limits von maximal 10.000 Euro. Dass der Zwang zum digitalen Bezahlen am Ende gar nicht zu weniger Kriminalität führt, scheint die Gegner des Bargelds gar nicht zu interessieren, obgleich genau das oftmals ihr einziges Argument bleibt. Steht letztlich sogar die Abschaffung unseres Bargeldes auf dem Plan? Das wäre fatal, denn sicheres, allgemein verfügbares, anonymes, kostenfreies und stabiles Geld ist eine der zentralen Säulen unserer freiheitlichen Wirtschaftsordnung.
 
 Schon eine deutliche Begrenzung von Bargeldzahlungen ermöglicht den Kontrollstaat. Jeder kann durch den Staat nach Belieben diszipliniert werden. Ohne Bargeld gibt es keine wirkliche Freiheit und bankrotte westliche Industrienationen können leichter eine Währungsreform durchsetzen und sich auf Kosten der Bürger entschulden. Prof. Dr. Max Otte, profilierter Finanzmarktexperte und mehrfacher Bestsellerautor, erklärt in seiner Streitschrift, warum jeder gegen das drohende Bargeldverbot kämpfen muss und was jeder persönlich tun kann.

Rettet unser Bargeld

Cet ouvrage, paru en 1988 déjà dans sa première édition, a depuis trouvé sa place dans la littérature juridique suisse. Le voici au seuil de sa sixième édition. Au fil du temps, le premier auteur s’est associé à des collègues de la (plus) jeune génération. Ce fut le cas pour la cinquième édition des Professeurs Laurent Bieri (Lausanne) et Blaise Carron (Neuchâtel) ; c’est le cas pour celle-ci du Professeur Blaise Carron qui a repris l’intégralité de la responsabilité, avec la collaboration du Professeur Aurélien Witzig (Neuchâtel et Genève), chargé de quelques contrats de sa spécialité. La matière évolue rapidement et cette mise à jour s’imposait. Elle a permis de compléter le texte, de le corriger et de l’améliorer. Cette édition conserve toutefois toutes les caractéristiques qui ont fait le succès de l’ouvrage : elle couvre l’intégralité des contrats spéciaux, en fait une présentation systématique et générale, avec les références nécessaires à des approfondissements. Pour la première fois en outre, elle intègre le contrat d’assurance. Ce livre est destiné à toutes celles et tous ceux qui pratiquent le droit des contrats, mais également à toutes celles et tous ceux qui se lancent dans la carrière juridique.

Les contrats spéciaux

Cash CDO Modelling in Excel

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