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Deep Credit Risk
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Book Synopsis Deep Credit Risk by : Harald Scheule
Download or read book Deep Credit Risk written by Harald Scheule and published by . This book was released on 2020-06-24 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Credit Risk - Machine Learning in Python aims at starters and pros alike to enable you to: - Understand the role of liquidity, equity and many other key banking features- Engineer and select features- Predict defaults, payoffs, loss rates and exposures- Predict downturn and crisis outcomes using pre-crisis features- Understand the implications of COVID-19- Apply innovative sampling techniques for model training and validation- Deep-learn from Logit Classifiers to Random Forests and Neural Networks- Do unsupervised Clustering, Principal Components and Bayesian Techniques- Build multi-period models for CECL, IFRS 9 and CCAR- Build credit portfolio correlation models for VaR and Expected Shortfall- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code- Access real credit data and much more ...
Book Synopsis Deep Credit Risk (Chinese) by : Harald Scheule
Download or read book Deep Credit Risk (Chinese) written by Harald Scheule and published by Deep Credit Risk. This book was released on 2021-07-22 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: - 了解流动性，房屋净值和许多其他关键银行业特征变量的作用； - 选择并处理变量； - 预测违约、偿付、损失率和风险敞口； - 利用危机前特征预测经济衰退和危机后果； - 理解COVID-19对信用风险带来的影响； - 将创新的抽样技术应用于模型训练和验证； - 从Logit分类器到随机森林和神经网络的深入学习; - 进行无监督聚类、主成分和贝叶斯技术的应用； - 为CECL、IFRS 9和CCAR建立多周期模型； - 建立用于在险价值和期望损失的信贷组合相关模型； - 使用更多真实的信用风险数据并运行超过1500行的代码... - Understand the role of liquidity, equity and many other key banking features - Engineer and select features - Predict defaults, payoffs, loss rates and exposures - Predict downturn and crisis outcomes using pre-crisis features - Understand the implications of COVID-19 - Apply innovative sampling techniques for model training and validation - Deep-learn from Logit Classifiers to Random Forests and Neural Networks - Do unsupervised Clustering, Principal Components and Bayesian Techniques - Build multi-period models for CECL, IFRS 9 and CCAR - Build credit portfolio correlation models for VaR and Expected Shortfal - Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code - Access real credit data and much more ...
Book Synopsis Credit-Risk Modelling by : David Jamieson Bolder
Download or read book Credit-Risk Modelling written by David Jamieson Bolder and published by Springer. This book was released on 2018-10-31 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.
Book Synopsis Deep Credit Risk by : Harald Scheule
Download or read book Deep Credit Risk written by Harald Scheule and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Credit Risk Analytics by : Bart Baesens
Download or read book Credit Risk Analytics written by Bart Baesens and published by John Wiley & Sons. This book was released on 2016-10-03 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. Understand the general concepts of credit risk management Validate and stress-test existing models Access working examples based on both real and simulated data Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
Book Synopsis Managing Portfolio Credit Risk in Banks: An Indian Perspective by : Arindam Bandyopadhyay
Download or read book Managing Portfolio Credit Risk in Banks: An Indian Perspective written by Arindam Bandyopadhyay and published by Cambridge University Press. This book was released on 2016-05-09 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains how a proper credit risk management framework enables banks to identify, assess and manage the risk proactively.
Book Synopsis IFRS 9 and CECL Credit Risk Modelling and Validation by : Tiziano Bellini
Download or read book IFRS 9 and CECL Credit Risk Modelling and Validation written by Tiziano Bellini and published by Academic Press. This book was released on 2019-01-31 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses. The book explores a wide range of models and corresponding validation procedures. The most traditional regression analyses pave the way to more innovative methods like machine learning, survival analysis, and competing risk modelling. Special attention is then devoted to scarce data and low default portfolios. A practical approach inspires the learning journey. In each section the theoretical dissertation is accompanied by Examples and Case Studies worked in R and SAS, the most widely used software packages used by practitioners in Credit Risk Management.
Book Synopsis Introduction to Credit Risk Modeling by : Christian Bluhm
Download or read book Introduction to Credit Risk Modeling written by Christian Bluhm and published by CRC Press. This book was released on 2016-04-19 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains Nearly 100 Pages of New MaterialThe recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modelin
Book Synopsis Intelligent Credit Scoring by : Naeem Siddiqi
Download or read book Intelligent Credit Scoring written by Naeem Siddiqi and published by John Wiley & Sons. This book was released on 2017-01-10 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: A better development and implementation framework for credit risk scorecards Intelligent Credit Scoring presents a business-oriented process for the development and implementation of risk prediction scorecards. The credit scorecard is a powerful tool for measuring the risk of individual borrowers, gauging overall risk exposure and developing analytically driven, risk-adjusted strategies for existing customers. In the past 10 years, hundreds of banks worldwide have brought the process of developing credit scoring models in-house, while ‘credit scores' have become a frequent topic of conversation in many countries where bureau scores are used broadly. In the United States, the ‘FICO' and ‘Vantage' scores continue to be discussed by borrowers hoping to get a better deal from the banks. While knowledge of the statistical processes around building credit scorecards is common, the business context and intelligence that allows you to build better, more robust, and ultimately more intelligent, scorecards is not. As the follow-up to Credit Risk Scorecards, this updated second edition includes new detailed examples, new real-world stories, new diagrams, deeper discussion on topics including WOE curves, the latest trends that expand scorecard functionality and new in-depth analyses in every chapter. Expanded coverage includes new chapters on defining infrastructure for in-house credit scoring, validation, governance, and Big Data. Black box scorecard development by isolated teams has resulted in statistically valid, but operationally unacceptable models at times. This book shows you how various personas in a financial institution can work together to create more intelligent scorecards, to avoid disasters, and facilitate better decision making. Key items discussed include: Following a clear step by step framework for development, implementation, and beyond Lots of real life tips and hints on how to detect and fix data issues How to realise bigger ROI from credit scoring using internal resources Explore new trends and advances to get more out of the scorecard Credit scoring is now a very common tool used by banks, Telcos, and others around the world for loan origination, decisioning, credit limit management, collections management, cross selling, and many other decisions. Intelligent Credit Scoring helps you organise resources, streamline processes, and build more intelligent scorecards that will help achieve better results.
Book Synopsis Counterparty Credit Risk by : Jon Gregory
Download or read book Counterparty Credit Risk written by Jon Gregory and published by John Wiley & Sons. This book was released on 2011-09-07 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first decade of the 21st Century has been disastrous for financial institutions, derivatives and risk management. Counterparty credit risk has become the key element of financial risk management, highlighted by the bankruptcy of the investment bank Lehman Brothers and failure of other high profile institutions such as Bear Sterns, AIG, Fannie Mae and Freddie Mac. The sudden realisation of extensive counterparty risks has severely compromised the health of global financial markets. Counterparty risk is now a key problem for all financial institutions. This book explains the emergence of counterparty risk during the recent credit crisis. The quantification of firm-wide credit exposure for trading desks and businesses is discussed alongside risk mitigation methods such as netting and collateral management (margining). Banks and other financial institutions have been recently developing their capabilities for pricing counterparty risk and these elements are considered in detail via a characterisation of credit value adjustment (CVA). The implications of an institution valuing their own default via debt value adjustment (DVA) are also considered at length. Hedging aspects, together with the associated instruments such as credit defaults swaps (CDSs) and contingent CDS (CCDS) are described in full. A key feature of the credit crisis has been the realisation of wrong-way risks illustrated by the failure of monoline insurance companies. Wrong-way counterparty risks are addressed in detail in relation to interest rate, foreign exchange, commodity and, in particular, credit derivative products. Portfolio counterparty risk is covered, together with the regulatory aspects as defined by the Basel II capital requirements. The management of counterparty risk within an institution is also discussed in detail. Finally, the design and benefits of central clearing, a recent development to attempt to control the rapid growth of counterparty risk, is considered. This book is unique in being practically focused but also covering the more technical aspects. It is an invaluable complete reference guide for any market practitioner with any responsibility or interest within the area of counterparty credit risk.
Book Synopsis Credit Risk Management by : Hong Kong Institute of Bankers (HKIB)
Download or read book Credit Risk Management written by Hong Kong Institute of Bankers (HKIB) and published by John Wiley & Sons. This book was released on 2012-09-04 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: The importance of managing credit and credit risks carefully and appropriately cannot be overestimated. The very success or failure of a bank and the banking industry in general may well depend on how credit risk is handled. Banking professionals must be fully versed in the risks associated with credit operations and how to manage those risks. This up-to-date volume is an invaluable reference and study tool that delves deep into issues associated with credit risk management. Credit Risk Management from the Hong Kong Institute of Bankers (HKIB)discusses the various ways through which banks manage risks. Essential for candidates studying for the HKIB Associateship Examination, it can also help those who want to acquire a deeper understanding of how and why banks make decisions and set up processes that lower their risk. Topics covered in this book include: Active credit portfolio management Risk management, pricing, and capital adequacy Capital requirements for banks Approaches to credit risk management Structural models and probability of default Techniques to determine loss given default Derivatives and structured products
Book Synopsis Machine Learning for Financial Risk Management with Python by : Abdullah Karasan
Download or read book Machine Learning for Financial Risk Management with Python written by Abdullah Karasan and published by "O'Reilly Media, Inc.". This book was released on 2021-12-07 with total page 334 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, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: 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 Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Deep Risk written by William J. Bernstein and published by . This book was released on 2013-08 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This booklet takes portfolio design beyond the familiar "black box" mean-variance framework. Most importantly, the short-term volatility of financial assets, commonly measured as standard deviation, is a highly imperfect measure of the actual long-horizon perils faced by real-world investors subject to the vagaries of financial and military history. These risks have names--inflation, deflation, confiscation, and devastation--and any useful discussion of portfolio design of necessity incorporates their probabilities, consequences, and costs of mitigation ... This booklet contains ... with luck, a framework within income and all-equity portfolios. This booklet contains ... with luck, a framework within which to think more clearly about risk. Note: the entire Investing for Adults series is not for beginners.
Book Synopsis Credit Risk Management In and Out of the Financial Crisis by : Anthony Saunders
Download or read book Credit Risk Management In and Out of the Financial Crisis written by Anthony Saunders and published by John Wiley & Sons. This book was released on 2010-04-16 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: A classic book on credit risk management is updated to reflect the current economic crisis Credit Risk Management In and Out of the Financial Crisis dissects the 2007-2008 credit crisis and provides solutions for professionals looking to better manage risk through modeling and new technology. This book is a complete update to Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms, reflecting events stemming from the recent credit crisis. Authors Anthony Saunders and Linda Allen address everything from the implications of new regulations to how the new rules will change everyday activity in the finance industry. They also provide techniques for modeling-credit scoring, structural, and reduced form models-while offering sound advice for stress testing credit risk models and when to accept or reject loans. Breaks down the latest credit risk measurement and modeling techniques and simplifies many of the technical and analytical details surrounding them Concentrates on the underlying economics to objectively evaluate new models Includes new chapters on how to prevent another crisis from occurring Understanding credit risk measurement is now more important than ever. Credit Risk Management In and Out of the Financial Crisis will solidify your knowledge of this dynamic discipline.
Book Synopsis Credit Risk Management by : Tony Van Gestel
Download or read book Credit Risk Management written by Tony Van Gestel and published by Oxford University Press. This book was released on 2009 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first of three volumes on credit risk management, providing a thorough introduction to financial risk management and modelling.
Book Synopsis Advances in Credit Risk Modeling and Management by : Frédéric Vrins
Download or read book Advances in Credit Risk Modeling and Management written by Frédéric Vrins and published by MDPI. This book was released on 2020-07-01 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Credit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored.
