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.

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• 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

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.

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• 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

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.

Select Guide Rating

• 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