{ "item_title" : "Actuarial Modeling and Pension Risk Management with Python", "item_author" : [" Danny Munrow", "Hayden Van Der Post "], "item_description" : "Reactive PublishingActuarial Modeling and Pension Risk Management with Python provides a practical, code-focused guide to modern pension risk management techniques using Python.This book bridges actuarial theory and real-world implementation by demonstrating how to build and apply key models for Asset Liability Management (ALM), Liability-Driven Investment (LDI), and Longevity Risk. Readers will learn how to construct stochastic models, run simulations, analyze liabilities, and manage the financial risks inherent in pension plans-all using Python and its powerful data science ecosystem.What You'll Find Inside: Practical implementations of core actuarial and risk management modelsPython code examples for ALM frameworks and cash flow projectionsLiability-Driven Investment strategies and optimization techniquesLongevity risk modeling, including mortality projection and scenario analysisTechniques for stress testing, sensitivity analysis, and risk measurementIntegration of actuarial science with data science toolsWritten for actuaries, pension analysts, risk managers, and quantitative professionals, this book assumes basic familiarity with actuarial concepts and Python programming. It focuses on clear, reproducible code and practical application rather than heavy theoretical derivation.Whether you work with defined benefit plans, pension funds, or insurance portfolios, this resource offers concrete tools to strengthen your modeling capabilities and improve risk management outcomes in today's complex financial environment.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/819/863/9798198633728_b.jpg", "price_data" : { "retail_price" : "39.99", "online_price" : "39.99", "our_price" : "39.99", "club_price" : "39.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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