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Artículo: AMZ-B0FQ4BGKBJ
Actuarial Finance - Mastering Derivatives and Risk Management With Python: From No-Arbitrage to XVA: Pricing, Hedging, and Capital with Python (Quantitative Risk and Actuarial Modeling Collection)
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1.07 kg
1.07 kg
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Amazon
Amazon
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USA
USA
Sobre este producto
- Master derivatives and risk the actuarial way. Rigorous, practical, Python-powered.If you’re an actuary, quant, or risk professional who needs to bridge market derivatives, insurance liabilities, and capital, this is your desk reference. Built from 33 dense, zero-fluff chapters, it takes you from no-arbitrage foundations to XVA, ALM, and optimization—then makes it real with fully worked Python implementations.Every chapter follows the same high-impact sequence:Theory that’s exam-grade and desk-readyMultiple-choice questions that test understanding and applicationFull Python code demonstrations you can run, extend, and reuseNo hand-waving. No toy examples. Just the models, methods, and code used to price, hedge, and manage risk across equity, rates, credit, FX, inflation, and insurance products.What you’ll masterNo-arbitrage pricing, state prices, SDFs, and the Fundamental Theorem of Asset PricingRisk-neutral valuation, change of numeraire, and measure transformsYield curves, collateralized discounting, and multi-curve frameworksShort-rate models, HJM, and LIBOR Market Models with practical calibrationLocal and stochastic volatility, SVI/eSSVI surfaces, and smile dynamicsPDE, Monte Carlo, variance reduction, and AAD for efficient GreeksEarly-exercise methods for American/Bermudan options and callable structuresExotics, volatility products, and static/dynamic replicationCredit risk, CDS bootstrapping, tranches, and XVA (CVA/DVA/FVA/MVA/KVA)Collateral, clearing, and documentation mechanics that drive pricingRisk measures (VaR/ES), dependence modeling, and capital aggregationStress testing, liquidity and funding, and treasury integrationALM, market-consistent valuation (IFRS 17, Solvency II), and nested simulationHedging embedded options in insurance products and dynamic policyholder behaviorLongevity risk, insurance-linked securities, securitization, and prepaymentModel risk management, governance, and robust optimization under constraintsHow you’ll learnConcise theory: precise definitions, assumptions, and implications for pricing and hedgingExam-style MCQs: from conceptual traps to calculation-level problems, with explanationsFull-code labs: end-to-end Python pricing engines, calibration routines, and risk reportsReproducible workflows: NumPy, pandas, SciPy, matplotlib, and common quant stacksImplementation depth: Monte Carlo, PDE/FD, AAD, calibration, and sensitivity analyticsWho it’s forActuaries moving into derivatives, capital markets, and market-consistent valuationQuants and risk managers who need insurance-linked optionality and ALMFRM/CFA/SOA/CAS candidates who want rigorous theory backed by working codePractitioners building pricing libraries, risk systems, or ESG/nested simulationsYou’ll be able toBuild and validate pricing models that pass audit and model risk scrutinyConstruct arbitrage-free curves and volatility surfaces that hedge well, not just fitQuantify, hedge, and attribute P&L across market, credit, and liquidity risksLink trading, collateral, funding, and accounting through consistent analyticsDeploy Python-based tools that scale from single trades to enterprise portfoliosReady to upgrade from formulas to full-stack pricing, hedging, and capital? Bold, rigorous, and relentlessly practical—this is the quant-actuary playbook you’ll use every day.Add to Cart and start building your edge now.
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