Muutke küpsiste eelistusi

E-raamat: Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective

  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Dec-2024
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9798868810527
  • Formaat - PDF+DRM
  • Hind: 67,91 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Ilmumisaeg: 13-Dec-2024
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9798868810527

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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. This book will show you the techniques to estimate potential financial outcomes using stochastic processes implemented with Python.





The book starts by reviewing financial concepts, such as analyzing different asset types like stocks, options, and portfolios. It then delves into the crux of stochastic finance, providing a glimpse into the probabilistic nature of financial markets. Youll look closely at probability theory, random variables, Monte Carlo simulation, and stochastic processes to cover the prerequisites from the applied perspective. Then explore random walks and Brownian motion, essential in understanding financial market dynamics. Youll get a glimpse of two vital modelling tools used throughout the book - stochastic calculus and stochastic differential equations (SDE). 





Advanced topics like modeling jump processes and estimating their parameters by Fourier-transform-based density recovery methods can be intriguing to those interested in full-numerical solutions of probability models. Moving forward, the book covers options, including the famous Black-Scholes model, dissecting it from both risk-neutral probability and PDE perspectives. A chapter at the end also covers the discovery of portfolio theory, beginning with mean-variance analysis and advancing to portfolio simulation and the efficient frontier.





What You Will Learn









Understand applied probability and statistics with finance Design forecasting models of the stock price with the stochastic process, Monte-Carlo simulation. Option price estimation with both risk-neutral probabilistic and PDE-driven approach. Use Object-oriented Python to design financial models with reusability.





Who This Book Is For 





Data scientists, quantitative researchers and practitioners, software engineers and AI architects interested in quantitative finance





 





 
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.
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.