9798312129328 - Optimization & Numerical Methods in Quant Finance: A Practical Guide to Portfolio Optimization, Derivatives Pricing, and Risk Management: 3 di Bisette, Vincent; Pub... (10 risultati)

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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. Reactive PublishingMaster Optimization & Numerical Methods for Smarter Financial Decision-MakingFinancial markets demand precision, and optimization & numerical methods are the backbone of portfolio management, option pricing, and risk assessment. From hedge funds to trading desks, mastering these techniques allows quants, traders, and financial engineers to build faster, more efficient models that drive profitability and minimize risk.This comprehensive guide provides a step-by-step approach to applying optimization techniques and numerical algorithms to real-world financial problems, with a strong emphasis on practical implementation using Python.What You'll Learn: Linear & Nonlinear Optimization in Finance - Lagrange multipliers, convex optimization, and portfolio allocation strategiesNumerical Solutions for Option Pricing - Finite difference methods, binomial trees, and Monte Carlo simulationsGradient Descent & Machine Learning Applications - Optimizing financial models using stochastic gradient descent (SGD)Constrained Optimization for Risk Management - Value at Risk (VaR) and efficient frontier calculationsGlobal vs. Local Optimization - Genetic algorithms, simulated annealing, and evolutionary strategies in financeNumerical Linear Algebra for Quantitative Finance - Eigenvalue decomposition, PCA, and factor modelingPython Implementations & Real-World Case Studies - Hands-on coding with SciPy, NumPy, and PandasWho This Book is For: Traders & Portfolio Managers - Optimize asset allocation and risk-return profilesQuantitative Analysts & Financial Engineers - Build more efficient pricing and risk modelsStudents & Researchers in Finance & Data Science - Strengthen your foundation in applied mathematics and computationWith clear explanations, real-world case studies, and Python implementations, this book transforms optimization and numerical methods into powerful tools for financial decision-making.Enhance your financial models-get your copy today! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Reactive PublishingMaster Optimization & Numerical Methods for Smarter Financial Decision-MakingFinancial markets demand precision, and optimization & numerical methods are the backbone of portfolio management, option pricing, and risk assessment. From hedge funds to trading desks, mastering these techniques allows quants, traders, and financial engineers to build faster, more efficient models that drive profitability and minimize risk.This comprehensive guide provides a step-by-step approach to applying optimization techniques and numerical algorithms to real-world financial problems, with a strong emphasis on practical implementation using Python.What You'll Learn: Linear & Nonlinear Optimization in Finance - Lagrange multipliers, convex optimization, and portfolio allocation strategiesNumerical Solutions for Option Pricing - Finite difference methods, binomial trees, and Monte Carlo simulationsGradient Descent & Machine Learning Applications - Optimizing financial models using stochastic gradient descent (SGD)Constrained Optimization for Risk Management - Value at Risk (VaR) and efficient frontier calculationsGlobal vs. Local Optimization - Genetic algorithms, simulated annealing, and evolutionary strategies in financeNumerical Linear Algebra for Quantitative Finance - Eigenvalue decomposition, PCA, and factor modelingPython Implementations & Real-World Case Studies - Hands-on coding with SciPy, NumPy, and PandasWho This Book is For: Traders & Portfolio Managers - Optimize asset allocation and risk-return profilesQuantitative Analysts & Financial Engineers - Build more efficient pricing and risk modelsStudents & Researchers in Finance & Data Science - Strengthen your foundation in applied mathematics and computationWith clear explanations, real-world case studies, and Python implementations, this book transforms optimization and numerical methods into powerful tools for financial decision-making.Enhance your financial models-get your copy today! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.