Bøger i The Wiley Finance Series serien

Manufacturing and Managing Customer-Driven Derivatives Manufacturing and Managing Customer-Driven Derivatives sheds light on customer-driven derivative products and their manufacturing process, which can prove a complicated topic for even experienced financial practitioners.

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.

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 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.

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.

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.

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.

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.

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.

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.

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

Covers commodity option pricing for quantitative analysts, traders or structures in banks, hedge funds and commodity trading companies. Based on the author's industry experience with commodity derivatives, this book provides a thorough and mathematical introduction to the various market conventions and models used in commodity option pricing.

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.

In the wake of the recent financial crisis, many will agree that it is time for a fresh approach to portfolio management. The Complete Guide to Portfolio Construction and Management provides practical investment advice for building a robust, diversified portfolio.

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.