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.
Diogo Gobira is a skilled finance professional and entrepreneur with a strong background in quantitative risk management and mathematical finance. He holds a Master of Science degree in Mathematical Finance from the Institute for Pure and Applied Mathematics (IMPA), and has worked as a Market Risk and Quantitative Modelling Manager at BNDES (Brazilian National Development Bank). Diogo is proficient in a range of technical areas, including programming, databases, derivatives pricing, portfolio optimization, integrated risk management, IRRBB, FTP, stress testing, and balance sheet optimization. Diogo is also a co-founder of Financial Risk Academy, a company specializing in the development of balance sheet optimization models and advanced training and consulting in quantitative finance.
Lucas Processi is an engineer and financial expert with a passion for market risk management and pricing of financial instruments. With a Bachelor's degree in Production Engineering from the Federal Fluminense University (UFF) and a Master's degree in Economics and Finance from the Getulio Vargas Foundation (FGV), Lucas is a market risk manager at the Brazilian National Development Bank (BNDES) and one of the founders of the Financial Risk Academy, where he shares his expertise in quantitative finance and programming with students and professionals alike. Additionally, his experience in the banking industry has enabled him to be a consultant in robo-advisors development, mathematical programming, ALM, and balance sheet optimization.
Motivation Core ALM Techniques ALM Perspectives: Accounting, Economic and Regulatory Accounting Principles Financial Contracts Modeling Contract Aggregation Scenario Generation Introduction to Mathematical Programming Applied to ALM Preparing The Model Coefficients Contract Sets Core Decision Variables Making The Model Legible With Auxiliary Variables Typical Objective Functions Accounting Constraints Market and Liquidity Constraints Regulatory Constraints Non-Arbitrage Constraints Implementing The Model Using Julia and JuMP 19. Conclusions
