Stochastic Finance With Python
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Stochastic Finance With Python
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Author : Avishek Nag
language : en
Publisher: Springer Nature
Release Date : 2024-12-13
Stochastic Finance With Python written by Avishek Nag and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-13 with Computers categories.
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. You’ll 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. You’ll 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
Foundations Of Quantitative Finance Book Iii The Integrals Of Riemann Lebesgue And Riemann Stieltjes
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Author : Robert R. Reitano
language : en
Publisher: CRC Press
Release Date : 2023-05-23
Foundations Of Quantitative Finance Book Iii The Integrals Of Riemann Lebesgue And Riemann Stieltjes written by Robert R. Reitano and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-23 with Mathematics categories.
Every financial professional wants and needs an advantage. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not—and that is the advantage these books offer the astute reader. Published under the collective title of Foundations of Quantitative Finance, this set of ten books presents the advanced mathematics finance professionals need to advance their careers. These books develop the theory most do not learn in Graduate Finance programs, or in most Financial Mathematics undergraduate and graduate courses. As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial industry and two decades in education where he taught in highly respected graduate programs. Readers should be quantitatively literate and familiar with the developments in the first book in the set. While the set offers a continuous progression through these topics, each title can also be studied independently. Features Extensively referenced to utilize materials from earlier books Presents the theory needed to support advanced applications Supplements previous training in mathematics, with more detailed developments Built from the author's five decades of experience in industry, research, and teaching Published and forthcoming titles in the Robert R. Reitano Quantitative Finance Series: Book I: Measure Spaces and Measurable Functions Book II: Probability Spaces and Random Variables Book III: The Integrals of Lebesgue and (Riemann-)Stieltjes Book IV: Distribution Functions and Expectations Book V: General Measure and Integration Theory Book VI: Densities, Transformed Distributions, and Limit Theorems Book VII: Brownian Motion and Other Stochastic Processes Book VIII: Itô Integration and Stochastic Calculus 1 Book IX: Stochastic Calculus 2 and Stochastic Differential Equations Book X: Classical Models and Applications in Finance
Computational Methods In Finance
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Author : Ali Hirsa
language : en
Publisher: CRC Press
Release Date : 2024-08-30
Computational Methods In Finance written by Ali Hirsa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-30 with Business & Economics categories.
Computational Methods in Finance is a book developed from the author’s courses at Columbia University and the Courant Institute of New York University. This self-contained text is designed for graduate students in financial engineering and mathematical finance, as well as practitioners in the financial industry. It will help readers accurately price a vast array of derivatives. This new edition has been thoroughly revised throughout to bring it up to date with recent developments. It features numerous new exercises and examples, as well as two entirely new chapters on machine learning. Features Explains how to solve complex functional equations through numerical methods Includes dozens of challenging exercises Suitable as a graduate-level textbook for financial engineering and financial mathematics or as a professional resource for working quants.
Foundations Of Quantitative Finance Book Iv Distribution Functions And Expectations
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Author : Robert R. Reitano
language : en
Publisher: CRC Press
Release Date : 2023-09-12
Foundations Of Quantitative Finance Book Iv Distribution Functions And Expectations written by Robert R. Reitano and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-12 with Mathematics categories.
Every finance professional wants and needs a competitive edge. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not—and that is the competitive edge these books offer the astute reader. Published under the collective title of Foundations of Quantitative Finance, this set of ten books develops the advanced topics in mathematics that finance professionals need to advance their careers. These books expand the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses. As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial services industry and two decades in academia where he taught in highly respected graduate programs. Readers should be quantitatively literate and familiar with the developments in the earlier books in the set. While the set offers a continuous progression through these topics, each title can be studied independently. Features Extensively referenced to materials from earlier books Presents the theory needed to support advanced applications Supplements previous training in mathematics, with more detailed developments Built from the author's five decades of experience in industry, research, and teaching Published and forthcoming titles in the Robert R. Reitano Quantitative Finance Series: Book I: Measure Spaces and Measurable Functions Book II: Probability Spaces and Random Variables Book III: The Integrals of Lebesgue and (Riemann-)Stieltjes Book IV: Distribution Functions and Expectations Book V: General Measure and Integration Theory Book VI: Densities, Transformed Distributions, and Limit Theorems Book VII: Brownian Motion and Other Stochastic Processes Book VIII: Itô Integration and Stochastic Calculus 1 Book IX: Stochastic Calculus 2 and Stochastic Differential Equations Book X: Classical Models and Applications in Finance
Foundations Of Quantitative Finance Book V General Measure And Integration Theory
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Author : Robert R. Reitano
language : en
Publisher: CRC Press
Release Date : 2024-02-27
Foundations Of Quantitative Finance Book V General Measure And Integration Theory written by Robert R. Reitano and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-27 with Mathematics categories.
Every finance professional wants and needs a competitive edge. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not—and that is the competitive edge these books offer the astute reader. Published under the collective title of Foundations of Quantitative Finance, this set of ten books develops the advanced topics in mathematics that finance professionals need to advance their careers. These books expand the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses. As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial services industry and two decades in academia where he taught in highly respected graduate programs. Readers should be quantitatively literate and familiar with the developments in the earlier books in the set. While the set offers a continuous progression through these topics, each title can be studied independently. Features Extensively referenced to materials from earlier books Presents the theory needed to support advanced applications Supplements previous training in mathematics, with more detailed developments Built from the author's five decades of experience in industry, research, and teaching Published and forthcoming titles in the Robert R. Reitano Quantitative Finance Series: Book I: Measure Spaces and Measurable Functions Book II: Probability Spaces and Random Variables Book III: The Integrals of Lebesgue and (Riemann-)Stieltjes Book IV: Distribution Functions and Expectations Book V: General Measure and Integration Theory Book VI: Densities, Transformed Distributions, and Limit Theorems Book VII: Brownian Motion and Other Stochastic Processes Book VIII: Itô Integration and Stochastic Calculus 1 Book IX: Stochastic Calculus 2 and Stochastic Differential Equations Book X: Classical Models and Applications in Finance
Financial Data Analytics With Machine Learning Optimization And Statistics
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Author : Sam Chen
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-21
Financial Data Analytics With Machine Learning Optimization And Statistics written by Sam Chen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-21 with Business & Economics categories.
An essential introduction to data analytics and Machine Learning techniques in the business sector In Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. The book begins with a brief introduction to basic sampling theory and the fundamentals of simulation techniques, followed by a comparison between R and Python. It then discusses statistical diagnosis for financial security data and introduces some common tools in financial forensics such as Benford's Law, Zipf's Law, and anomaly detection. The statistical estimation and Expectation-Maximization (EM) & Majorization-Minimization (MM) algorithms are also covered. The book next focuses on univariate and multivariate dynamic volatility and correlation forecasting, and emphasis is placed on the celebrated Kelly's formula, followed by a brief introduction to quantitative risk management and dependence modelling for extremal events. A practical topic on numerical finance for traditional option pricing and Greek computations immediately follows as well as other important topics in financial data-driven aspects, such as Principal Component Analysis (PCA) and recommender systems with their applications, as well as advanced regression learners such as kernel regression and logistic regression, with discussions on model assessment methods such as simple Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) for typical classification problems. The book then moves on to other commonly used machine learning tools like linear classifiers such as perceptrons and their generalization, the multilayered counterpart (MLP), Support Vector Machines (SVM), as well as Classification and Regression Trees (CART) and Random Forests. Subsequent chapters focus on linear Bayesian learning, including well-received credibility theory in actuarial science and functional kernel regression, and non-linear Bayesian learning, such as the Naïve Bayes classifier and the Comonotone-Independence Bayesian Classifier (CIBer) recently independently developed by the authors and used successfully in InsurTech. After an in-depth discussion on cluster analyses such as K-means clustering and its inversion, the K-nearest neighbor (KNN) method, the book concludes by introducing some useful deep neural networks for FinTech, like the potential use of the Long-Short Term Memory model (LSTM) for stock price prediction. This book can help readers become well-equipped with the following skills: To evaluate financial and insurance data quality, and use the distilled knowledge obtained from the data after applying data analytic tools to make timely financial decisions To apply effective data dimension reduction tools to enhance supervised learning To describe and select suitable data analytic tools as introduced above for a given dataset depending upon classification or regression prediction purpose The book covers the competencies tested by several professional examinations, such as the Predictive Analytics Exam offered by the Society of Actuaries, and the Institute and Faculty of Actuaries' Actuarial Statistics Exam. Besides being an indispensable resource for senior undergraduate and graduate students taking courses in financial engineering, statistics, quantitative finance, risk management, actuarial science, data science, and mathematics for AI, Financial Data Analytics with Machine Learning, Optimization and Statistics also belongs in the libraries of aspiring and practicing quantitative analysts working in commercial and investment banking.
Handbook Of Price Impact Modeling
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Author : Kevin T Webster
language : en
Publisher: CRC Press
Release Date : 2023-05-05
Handbook Of Price Impact Modeling written by Kevin T Webster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-05 with Mathematics categories.
Handbook of Price Impact Modeling provides practitioners and students with a mathematical framework grounded in academic references to apply price impact models to quantitative trading and portfolio management. Automated trading is now the dominant form of trading across all frequencies. Furthermore, trading algorithm rise introduces new questions professionals must answer, for instance: How do stock prices react to a trading strategy? How to scale a portfolio considering its trading costs and liquidity risk? How to measure and improve trading algorithms while avoiding biases? Price impact models answer these novel questions at the forefront of quantitative finance. Hence, practitioners and students can use this Handbook as a comprehensive, modern view of systematic trading. For financial institutions, the Handbook’s framework aims to minimize the firm’s price impact, measure market liquidity risk, and provide a unified, succinct view of the firm’s trading activity to the C-suite via analytics and tactical research. The Handbook’s focus on applications and everyday skillsets makes it an ideal textbook for a master’s in finance class and students joining quantitative trading desks. Using price impact models, the reader learns how to: Build a market simulator to back test trading algorithms Implement closed-form strategies that optimize trading signals Measure liquidity risk and stress test portfolios for fire sales Analyze algorithm performance controlling for common trading biases Estimate price impact models using public trading tape Finally, the reader finds a primer on the database kdb+ and its programming language q, which are standard tools for analyzing high-frequency trading data at banks and hedge funds. Authored by a finance professional, this book is a valuable resource for quantitative researchers and traders.
Brownian Motion And Stochastic Calculus
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Author : Jamie Flux
language : en
Publisher: Independently Published
Release Date : 2024-12-10
Brownian Motion And Stochastic Calculus written by Jamie Flux and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-10 with Mathematics categories.
Unlock the profound depths of Brownian Motion and Stochastic Calculus with this comprehensive and authoritative text. Designed for researchers, practitioners, and advanced students, this book provides an in-depth exploration of theoretical concepts and practical applications, seamlessly bridging the gap between abstract mathematics and real-world problem-solving. Key Features: Comprehensive Coverage: Spanning 66 meticulously crafted chapters, this work delves into essential topics such as probability theory, measure theory, stochastic differential equations, martingales, and more. Each chapter focuses on a specific concept, allowing for a modular and thorough understanding of the subject matter. Python Implementation: Enhance your learning experience with robust Python code examples integrated throughout the text. Implement and simulate complex stochastic models, facilitating a hands-on approach to mastering the material. Theoretical and Applied Balance: Gain a solid foundation in stochastic calculus theory while exploring practical applications across various fields like finance, physics, biology, and machine learning. This dual focus ensures a well-rounded grasp of both the abstract and the tangible aspects of the discipline. Why This Book? In an era where uncertainty and complex systems are the norms, a profound understanding of stochastic processes is indispensable. This text stands as an essential resource for those aiming to: Advance Academic Research: Provide a solid theoretical foundation for advanced studies and contribute to scholarly work in mathematics and related fields. Enhance Professional Practice: Apply sophisticated stochastic models to solve real-world problems in finance, engineering, data science, and beyond. Develop Technical Skills: Leverage the power of Python to implement and experiment with stochastic calculus concepts, enhancing computational proficiency alongside mathematical understanding. Elevate your mastery of stochastic calculus and unlock new horizons in both theory and application. This is more than a textbook; it's a gateway to advancing your expertise and making significant contributions to the scientific and technological communities.
Advanced Topics In Computational Partial Differential Equations
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Author : Hans Petter Langtangen
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-10-29
Advanced Topics In Computational Partial Differential Equations written by Hans Petter Langtangen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-10-29 with Mathematics categories.
A gentle introduction to advanced topics such as parallel computing, multigrid methods, and special methods for systems of PDEs. The goal of all chapters is to ‘compute’ solutions to problems, hence algorithmic and software issues play a central role. All software examples use the Diffpack programming environment - some experience with Diffpack is required. There are also some chapters covering complete applications, i.e., the way from a model, expressed as systems of PDEs, through to discretization methods, algorithms, software design, verification, and computational examples. Suitable for readers with a background in basic finite element and finite difference methods for partial differential equations.
Mathematical Modeling And Computation In Finance With Exercises And Python And Matlab Computer Codes
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Author : Cornelis W Oosterlee
language : en
Publisher: World Scientific
Release Date : 2019-10-29
Mathematical Modeling And Computation In Finance With Exercises And Python And Matlab Computer Codes written by Cornelis W Oosterlee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-29 with Business & Economics categories.
This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.