Bøger i The Wiley Finance Series serien

The comprehensive, broadly-applicable, real-world guide to financial modelling Financial Modelling in Practice, Second Edition covers the full spectrum of financial modelling tools and techniques to provide practical skills grounded in real-world scenarios.

This comprehensive reference delivers a toolkit for harvesting market rewards from a wide range of investments. Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters.

Modelling Single-name and Multi-name Credit Derivatives presents an up-to-date, comprehensive, accessible and practical guide to the pricing and risk-management of credit derivatives. It is both a detailed introduction to credit derivative modelling and a reference for those who are already practitioners.

Assess the likelihood, timing and scope of climate risksIn Climate Risks: An Investor's Field Guide to Identification and Assessment, financial analyst Bob Buhr delivers a risk-based framework for classifying and measuring potential climate risks at the firm level, and their potential financial impacts. The author presents a "climate risk taxonomy" that encompasses a broad range of physical, transition and natural capital risks that may impact a firm's financial profile.The taxonomy presented in the book will be of interest to investors and lenders involved in:* The identification and assessment of the potential scope and impact of a wide range of risks that might normally remain outside of more traditional risk or credit analysis, usually for horizon issues;* The determination of the points at which climate risks may crystallize into real and significant financial exposure* The assessment of the relative aggregate riskiness of portfolios exposed to climate and natural capital risks at the firm levelA rigorous and practical toolkit for the assessment and measurement of a broad range of potential climate risks, this book offers fund managers, portfolio analysts, risk experts, and other finance professionals a clear blueprint for assessing potential financial impacts at firms arising from climate change.

A comprehensive introduction to various numerical methods used in computational finance today Quantitative skills are a prerequisite for anyone working in finance or beginning a career in the field, as well as risk managers.

A practitioner's guide to ex-post performance measurement techniques Risk within asset management firms has an undeserved reputation for being an overly complex, mathematical subject. This book simplifies the subject and demonstrates with practical examples that risk is perfectly straightforward and not as complicated as it might seem.

An essential introduction to data analytics and Machine Learning techniques in the business sectorIn 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) & 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 purposeThe 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.

An introduction to the subject of performance measurement aimed at performance analysts, portfolio managers and senior management within asset management firms and pension fund trustees.

Transform your financial organisation's formula for value creation with this insightful and strategic approachIn Transforming Financial Institutions through Technology Innovation and Operational Change, visionary turnaround leader Joerg Ruetschi delivers a practical and globally relevant methodology and framework for value creation at financial institutions. The author demonstrates how financial organisations can combine finance strategy with asset-liability and technology management to differentiate their services and gain competitive advantage in a ferocious industry.In addition to exploring the four critical areas of strategic and competitive transformation -- financial analysis, valuation, modeling, and stress -- the book includes:* Explanations of how to apply the managerial fundamentals discussed in the book in the real world, with descriptions of the principles for reorganization, wind-down and overall value creation* An analysis of the four key emerging technologies in the financial industry: AI, blockchain, software, and infrastructure solutions, and their transformational impact* Real-world case studies and examples on how financial institutions can be repositioned and rebuilt on a path of profitabilityPerfect for managers and decision makers in the financial services industry, Transforming Financial Institutions through Technology Innovation and Operational Change is also required reading for regulators, tech firms, and private equity and venture capital funds.

State-of-the-art algorithmic deep learning and tensoring techniques for financial institutionsThe computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner's View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions.This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You'll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you'll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used.* Review the fundamentals of deep learning and Chebyshev tensors* Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation* Learn how to apply the solutions to a wide range of real-life risk calculations.* Download sample code used in the book, so you can follow along and experiment with your own calculations* Realize improved risk management whilst overcoming the burden of limited computational powerQuants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.

Discover the future of the financial services industry with this insightful new resource on Contextual and Conscious BankingIn Banks and Fintech on Platform Economies: Contextual and Conscious Banking, accomplished fintech professional and author Paolo Sironi delivers an insightful examination of how platform theory, born outside of financial services, will make its way inside banking and financial markets to radically transform the way firms do business.You'll learn why the financial services industry must master the necessary shift of focus from selling business outputs to selling client outcomes. You'll also discover how to steer the industry towards new forms of digital transformation underpinned by Contextual Banking and Conscious Banking platform strategies that will benefit stakeholders of all kinds.This important book:* Describes the shift in mindset necessary to help banks strengthen and extend the reach of their Banking-as-a-Service and Banking-as-a-Platform operations.* Shows how a renewed interpretation of fundamental uncertainty inspires the usage of exponential technologies to achieve architectural resilience, and open the reference theory to spring new business models centered on clients' and ecosystems' antifragility.* Financial services industry can break-out from a narrow space of value-generation to reclaim top spot against bigtech contenders, enjoying greater flexibility and adaptability at lower digital costsPerfect for CEOs, business leaders, regulators, fintech entrepreneurs, wealth managers, behavioral finance researchers and professionals working at financial technology companies, Banks and Fintech on Platform Economieswill also earn a place in the libraries of bankers seeking a firm grasp of the rapidly evolving outcome economy and a view about the future of the industry.

The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts. Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance. Inside, you'll find over ten chapters which apply deep learning to multiple use cases across quantitative finance. You'll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text. Readers will be able to work through these examples directly. This book is a complete resource on how deep learning is used in quantitative finance applications. It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics. You'll also learn about the most important software frameworks. The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging. The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts. Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniques Offers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learning Demonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion website Introduces the most important software frameworks for applying deep learning within finance This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.

A unique, authoritative, and comprehensive treatment of fixed income marketsFixed Income Trading and Risk Management: The Complete Guide delivers a comprehensive and innovative exposition of fixed income markets. Written by European Central Bank portfolio manager Alexander During, this book takes a practical view of how several different national fixed income markets operate in detail.The book presents common theoretical models but adds a lot of information on the actually observed behavior of real markets. You'll benefit from the book's:* Fulsome overview of money, credit, and monetary policy* Description of cash instruments, inflation-linked debt, and credit claims* Analysis of derivative instruments, standard trading strategies, and data analysis* In-depth focus on risk management in fixed income marketsPerfect for new and junior staff in financial institutions working in sales and trading, risk management, back office operations, and portfolio management positions, Fixed Income Trading and Risk Management also belongs on the bookshelves of research analysts and postgraduate students in finance, economics, or MBA programs.

There has been an explosive growth in the number of corporates, investors and financial institutions turning to structured products to achieve cost savings, risk controls and yield enhancements.

A comprehensive reference guide to key commodity derivative markets Commodity Derivatives provides detailed coverage of a variety of commodity markets and their derivatives for those invested on both the buy and sell sides.

Looks at the theory and practice of modern banking, and at the prospects for the future. This text is devoted to micro issues of banking, including competition, structure, performance, risk, and regulation.

A one-of-a-kind reference guide covering the behavioral and statistical explanations for market momentum and the implementation of momentum trading strategiesMarket Momentum: Theory and Practice is a thorough, how-to reference guide for a full range of financial professionals and students. It examines the behavioral and statistical causes of market momentum while also exploring the practical side of implementing related strategies.The phenomenon of momentum in finance occurs when past high returns are followed by subsequent high returns, and past low returns are followed by subsequent low returns. Market Momentum provides a detailed introduction to the financial topic, while examining existing literature. Recent academic and practitioner research is included, offering a more up-to-date perspective.What type of book is Market Momentum and how does it serve a range of readers' interests and needs?* A holistic market momentum guide for industry professionals, asset managers, risk managers, firm managers, plus hedge fund and commodity trading advisors* Advanced text to help graduate students in finance, economics, and mathematics further develop their funds management skills* Useful resource for financial practitioners who want to implement momentum trading strategies* Reference book providing behavioral and statistical explanations for market momentumDue to claims that the phenomenon of momentum goes against the Efficient Markets Hypothesis, behavioral economists have studied the topic in-depth. However, many books published on the subject are written to provide advice on how to make money. In contrast, Market Momentum offers a comprehensive approach to the topic, which makes it a valuable resource for both investment professionals and higher-level finance students. The contributors address momentum theory and practice, while also offering trading strategies that practitioners can study.

A practioner's guide to quantitative reverse stress testing, featuring advanced solutions for better management of financial risksWhether you manage a portfolio, a business unit or a bank, you must ensure that a sudden shock in the markets won't drive you out of business because of a large loss, a jump in capital utilisation or an increase in funding consumption . In financial risk management, reverse stress testing is the attempt to identify worst case scenarios causing maximum financial loss. Traditional stress testing methods have failed to avert the last financial crisis or highlight financial distress. . Every so often some regulators and risk managers specify stress scenarios with simplifications that create a false sense of safety. To avoid a combinatorial explosion, a number of arbitrary choices are usually made in relation to the level of each shock, their combination and the time horizon. These assumptions, although necessary, limit the effectiveness of this technique.Quantitative Reverse Stress Testing: Hunting for the Black Swan offers a more natural approach to identifying the realistic scenario provoking the largest losses. It also provides a means to understand the impact of extreme events and how to mitigate them while introducing technological advances and the adapted mathematical tools that have made it possible to solve this type of quantitative finance problems. You'll see how mitigation strategies can be more accurate and better targeted, reducing risk, cost of funding and regulatory capital.With this book, you'll have access to a comprehensive guide to crucial aspects of reverse stress testing.* Get an introduction to a rigorous mathematical framework to quantitative reverse stress testing* Consider different heuristics, one of them based on annealing, to solve theoptimisation problem* Review solution implementation, system infrastructure design and integration into the financial risk management of a firm* Read related case studies from a variety of industriesIf it's time to move your approach to reverse stress testing forward, this guide offers real solutions tooptimisation and cost issues. Get a guide that supports a more informed approach to testing and financial risk management as it helps to protect vital business interests.

Detect accounting fraud before it's too lateAccounting fraud is the deliberate manipulation of accounting records in order to make a company's financial performance seem better or worse than it actually is. Accounting scandals often have catastrophic consequences for shareholders and employees. Thus, analysts and auditors must be equipped to detect accounting fraud.This book is a comprehensive guide to detecting accounting fraud for auditors investigating accounting fraud and analysts/managers seeking to prevent it. A wide variety of warning signs are described, as are several techniques for detecting and addressing fraud.* Understand the motivations and warning signs behind accounting fraud* Get to know how accounting fraud is done and how to detect it* Avoid the losses that often come from accounting fraud* Benefit from case studies throughout to that help illustrate the author's pointsIt's unfortunate that managers, auditors, and analysts must be wary of accounting fraud--but this book equips you with the know-how to detect it before it's too late.

Operational Risk Management offers a comprehensive guide that contains a review of the most up-to-date and effective operational risk management practices in the financial services industry. The book provides an essential overview of the current methods and best practices applied in financial companies and also contains advanced tools and techniques developed by the most mature firms in the field. The author explores the range of operational risks such as information security, fraud or reputation damage and details how to put in place an effective program based on the four main risk management activities: risk identification, risk assessment, risk mitigation and risk monitoring. The book also examines some specific types of operational risks that rank high on many firms' risk registers. Drawing on the author's extensive experience working with and advising financial companies, Operational Risk Management is written both for those new to the discipline and for experienced operational risk managers who want to strengthen and consolidate their knowledge.

Wealth owners are responsible for more than just assets The Destructive Power of Family Wealth offers thoughtful, holistic planning to ensure that your wealth remains a positive force for your family.

The derivative practitioner s expert guide to IFRS 9 application Accounting for Derivatives explains the likely accounting implications of a proposed transaction on derivatives strategy, in alignment with the IFRS 9 standards.

Use powerful C++ algorithms and Object Oriented Programming (OOP) to aid in hedge fund decision making Low interest rates, overcrowded markets and greater regulatory oversight are just some of the many reasons it is close to impossible for hedge funds to draw competitive returns.

Delve into ETFs for smarter investing and a weatherproof portfolio Beyond Smart Beta is the investor's complete guide to index investing, with deep analysis, expert clarification and smart strategies for active portfolio management.

Clear, comprehensive guidance toward the global infrastructure investment market Infrastructure As An Asset Class is the leading infrastructure investment guide, with comprehensive coverage and in-depth expert insight.

A survival guide for the FinTech era of banking FinTech Innovation examines the rise of financial technology and its growing impact on the global banking industry. Wealth managers are standing at the epicenter of a tectonic shift, as the balance of power between offering and demand undergoes a dramatic upheaval.

The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers.

The time for financial technology innovation is now Marketplace Lending, Financial Analysis, and the Future of Credit clearly explains why financial credit institutions need to further innovate within the financial technology arena. Through this text, you access a framework for applying innovative strategies in credit services.

Cut risk and generate profit even after the market drops The Second Leg Down offers practical approaches to profiting after a market event. Written by a specialist in global macro, volatility and hedging overlay strategies, this book provides in-depth insight into surviving in a volatile environment.