Stochastic Finance with Python

Design Financial Models from Probabilistic Perspective

Häftad, Engelska, 2024

632 kr

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Beskrivning

Journey through the world of stochastic finance from learning theory, underlying models, and derivations of financial models (stocks, options, portfolios) to the almost production-ready Python components under cover of stochastic finance.

Produktinformation

Utgivningsdatum:2024-12-14
Mått:178 x 254 x 23 mm
Vikt:771 g
Format:Häftad
Språk:Engelska
Antal sidor:396
Förlag:APress
ISBN:9798868810510

Utforska kategorier

Tillämpad matematik inom Naturvetenskap och teknik
Finansiering inom Ekonomi och Ledarskap
Programspråk inom Data och IT

Mer om författaren

Avishek Nag has been an analytics practitioner for several years now, specializing in statistical methods, machine learning, NLP & Quantitative Finance. He has experience designing end-to-end Machine Learning systems and driving Data Science/ML initiatives from inception to production in multiple organizations (Cisco, VMware, Mobile Iron, etc.). A few years of experience in the commodity trading domain inspired him to write this book. He has also authored other books on machine learning & survival analysis, respectively. His Data science & ML-related blogs can be found on Medium (@avisheknag17).Besides his work, he is also a passionate artist who loves to explore architectural drawings through pencil and ink. Samples of his artwork can be found on Instagram(/avisheknag17), Artquid.com(artquid.com/avishekarts), and many other art platforms.

Innehållsförteckning

Part I - Foundations & Pre-requisites.- Chapter 1 - Introduction.- Chapter 2 – Finance Basics & Data Sources.- Chapter 3 - Probability.- Chapter 4 - Simulation.- Chapter 5 – Stochastic Process.- Part II – Basic Asset Price Modelling.- Chapter 6 – Diffusion Model.- Chapter 7 – Jump Models.- Part III – Financial Options Modelling.- Chapter 8 – Options & Black-Scholes Model.- Chapter 9 – PDE, Finite-Difference & Black-Scholes Model.- Part IV - Portfolios.- Chapter 10 – Portfolio Optimization.