An advanced method for financial institutions to optimize Asset Liability Management for maximized return and minimized risk Financial institutions today are facing daunting regulatory and economic challenges. As they manage bank regulation and competition, institutions are also optimizing their Asset Liability Management (ALM) operations. The function of the ALM unit today goes beyond risk management related to the banking book into managing regulatory capital and positioning the balance sheet to maximize profit. Asset Liability Management Optimization: A Practitioner's Guide to Balance Sheet Management and Remodelling offers a step-by-step process for modeling and reshaping a bank's balance sheet. Based on the author's extensive research, it describes how to apply a quantifiable optimization method to help maximize asset return and minimize funding cost in the banking book. ALM ranks as a key component of any financial institution's overall operating strategy. Now, financial professionals can use an advanced solution for optimizing ALM. This book takes a closer look at the evolving role of the ALM function and the target position of the banking book. It provides strategies for active management, structuring, and hedging of a bank balance sheet, while also exploring additional topics related to ALM. A description of the Funds Transfer Pricing (FTP) process related to a bank’s target position Detailed examinations of interest rate risk in the banking book (IRRBB) Discussion of Basel III regulatory requirements and maturity gap analysis Overview of customer behavior, along with its impact on interest rate and liquidity risk Practical spreadsheet models (NII sensitivity and EVE volatility IRRBB model, simplified optimization model for minimization of average funding cost for a bank and an example of behavioral model for Non-Maturing Deposits) Explorations of model risk, sensitivity analysis, and case studies The optimization techniques found in Asset Liability Management Optimization can prove vital to financial professionals who are tasked with maximizing asset return and reducing funding costs as a critical part of business objectives.

ALM Modeling and Balance Sheet Optimization is a comprehensive book that combines theoretical exploration with practical guidance and code examples on implementing a balance sheet optimization model. The book emphasizes the use of stochastic dynamic programming to develop a deep and holistic understanding of the banking problem. Encompassing the entire implementation stack – spanning from data layers to the specification of decision variables, business and regulatory constraints, objective functions, modeling strategies, solving techniques, debugging, and reporting – this book serves as a comprehensive guide for constructing highly effective balance optimization models from scratch, enabling the maximization of banking outcomes. Readers will learn how to build a mathematical model capable of generating projections for portfolios; balance sheet, income and cash flow statements; capital, and risk measures in real-world scenarios. This practical approach is particularly valuable for professionals involved in integrated stress testing, capital adequacy assessment, financial planning, and optimization tasks. In essence, the book offers valuable insights into the challenges of balance sheet optimization, providing readers with the necessary tools to build their own dynamic and comprehensive ALM models.

ALM Modeling and Balance Sheet Optimization leads the reader through the vast world of mathematical programming applied to balance sheet optimization. It considers a wide range of business and regulatory constraints and different flavors of financial objectives to show how a mathematical model can be used to orchestrate the available information, market visions, operational constraints and bank objectives, working as a powerful decision support tool. The book argues that, soon, the use of optimization models may no longer be a mere option, becoming a matter of survival for bank institutions as in other sectors. Readers will be able to understand the theory and implement a mathematical model capable of producing scenario projections for portfolios, balance sheets, results, capital and risk indicators in the real world. This book will be great benefit to those involved with integrated stress testing, capital adequacy testing and financial strategic planning and optimization tasks.

In the Handbook of Asset and Liability Management: From Models to Optimal Return Strategies, Alexandre Adam presents a comprehensive guide to Asset and Liability Management. Written from a quantitative perspective with economic explanations, this book will appeal to both mathematicians and non-mathematicians alike as it gives an operational view on the business. Well structured, this book includes essential information on Balance Sheet Items and Products Modeling, Tools for Asset and Liability Managers, as well as Optimal Returns Strategies. Explaining, in detail, all the written and unwritten rules of Asset Liability Management, using up-to-date models and the latest findings, the Handbook of Asset and Liability Management is an essential tool for Asset and Liability Managers both for the present day and the future.

Asset-Liability and Liquidity Management distils the author’s extensive experience in the financial industry, and ALM in particular, into concise and comprehensive lessons. Each of the topics are covered with a focus on real-world applications, based on the author’s own experience in the industry. The author is the Vice President of Treasury Modeling and Analytics at American Express. He is also an adjunct Professor at New York University, teaching a variety of analytical courses. Learn from the best as Dr. Farahvash takes you through basic and advanced topics, including: The fundamentals of analytical finance Detailed explanations of financial valuation models for a variety of products The principle of economic value of equity and value-at-risk The principle of net interest income and earnings-at-risk Liquidity risk Funds transfer pricing A detailed Appendix at the end of the book helps novice users with basic probability and statistics concepts used in financial analytics.

Introduces practical approaches for optimizing management and hedging of Interest Rate Risk in the Banking Book (IRRBB) driven by fast evolving regulatory landscape and market expectations. Interest rate risk in the banking book (IRRBB) gained its importance through the regulatory requirements that have been growing and guiding the banking industry for the last couple of years. The importance of IRRBB is shifting for banks, away from ‘just’ a regulatory requirement to having an impact on the overall profitability of a financial institution. Interest Rate Risk in the Banking Book sheds light on the best practices for managing this importance risk category and provides detailed analysis of the hedging strategies, practical examples, and case studies based on the author’s experience. This handbook is rich in practical insights on methodological approach and contents of ALCO report, IRRBB policy, ICAAP, Risk Appetite Statement (RAS) and model documentation. It is intended for the Treasury, Risk and Finance department and is helpful in improving and optimizing their IRRBB framework and strategy. By the end of this IRRBB journey, the reader will be equipped with all the necessary tools to build a proactive and compliant framework within a financial institution. Gain an updated understanding of the evolving regulatory landscape for IRRBB Learn to apply maturity gap analysis, sensitivity analysis, and the hedging strategy in banking contexts • Understand how customer behavior impacts interest rate risk and how to manage the consequences Examine case studies illustrating key IRRBB exposures and their implications Written by London market risk expert Beata Lubinska, Interest Rate Risk in the Banking Book is the authoritative resource on this evolving topic.

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.