Introduction to Stochastic Calculus Applied to Finance PDF Download
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Author: Damien Lamberton
Publisher: CRC Press
ISBN: 142000994X
Category : Business & Economics
Languages : en
Pages : 253
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Book Description
Since the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, this concise and accessible introduction covers the probabilistic techniques required to understand the most widely used financial models. Along with additional exercises, this edition presents fully updated material on stochastic volatility models and option pricing as well as a new chapter on credit risk modeling. It contains many numerical experiments and real-world examples taken from the authors' own experiences. The book also provides all of the necessary stochastic calculus theory and implements some of the algorithms using SciLab. Key topics covered include martingales, arbitrage, option pricing, and the Black-Scholes model.
Author: Damien Lamberton
Publisher: CRC Press
ISBN: 142000994X
Category : Business & Economics
Languages : en
Pages : 253
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Book Description
Since the publication of the first edition of this book, the area of mathematical finance has grown rapidly, with financial analysts using more sophisticated mathematical concepts, such as stochastic integration, to describe the behavior of markets and to derive computing methods. Maintaining the lucid style of its popular predecessor, this concise and accessible introduction covers the probabilistic techniques required to understand the most widely used financial models. Along with additional exercises, this edition presents fully updated material on stochastic volatility models and option pricing as well as a new chapter on credit risk modeling. It contains many numerical experiments and real-world examples taken from the authors' own experiences. The book also provides all of the necessary stochastic calculus theory and implements some of the algorithms using SciLab. Key topics covered include martingales, arbitrage, option pricing, and the Black-Scholes model.
Author: Paul Glasserman
Publisher: Springer Science & Business Media
ISBN: 0387216170
Category : Mathematics
Languages : en
Pages : 603
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Book Description
From the reviews: "Paul Glasserman has written an astonishingly good book that bridges financial engineering and the Monte Carlo method. The book will appeal to graduate students, researchers, and most of all, practicing financial engineers [...] So often, financial engineering texts are very theoretical. This book is not." --Glyn Holton, Contingency Analysis
Author: John C. Cox
Publisher: Prentice Hall
ISBN:
Category : Business & Economics
Languages : en
Pages : 518
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Book Description
Includes the first published detailed description of option exchange operations, the first published treatment using only elementary mathematics and the first step-by-step procedure for implementing the Black-Scholes formula in actual trading.
Author: Yves Hilpisch
Publisher: John Wiley & Sons
ISBN: 1119037999
Category : Business & Economics
Languages : en
Pages : 390
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Book Description
Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.
Author: Desmond J. Higham
Publisher: Cambridge University Press
ISBN: 1139457896
Category : Mathematics
Languages : en
Pages : 300
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Book Description
This is a lively textbook providing a solid introduction to financial option valuation for undergraduate students armed with a working knowledge of a first year calculus. Written in a series of short chapters, its self-contained treatment gives equal weight to applied mathematics, stochastics and computational algorithms. No prior background in probability, statistics or numerical analysis is required. Detailed derivations of both the basic asset price model and the Black–Scholes equation are provided along with a presentation of appropriate computational techniques including binomial, finite differences and in particular, variance reduction techniques for the Monte Carlo method. Each chapter comes complete with accompanying stand-alone MATLAB code listing to illustrate a key idea. Furthermore, the author has made heavy use of figures and examples, and has included computations based on real stock market data.
Author: Julien Guyon
Publisher: CRC Press
ISBN: 1466570342
Category : Business & Economics
Languages : en
Pages : 480
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Book Description
New Tools to Solve Your Option Pricing ProblemsFor nonlinear PDEs encountered in quantitative finance, advanced probabilistic methods are needed to address dimensionality issues. Written by two leaders in quantitative research-including Risk magazine's 2013 Quant of the Year-Nonlinear Option Pricing compares various numerical methods for solving hi
Author: Stanislav Uryasev
Publisher: Springer Science & Business Media
ISBN: 1475731507
Category : Mathematics
Languages : en
Pages : 319
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Book Description
Probabilistic and percentile/quantile functions play an important role in several applications, such as finance (Value-at-Risk), nuclear safety, and the environment. Recently, significant advances have been made in sensitivity analysis and optimization of probabilistic functions, which is the basis for construction of new efficient approaches. This book presents the state of the art in the theory of optimization of probabilistic functions and several engineering and finance applications, including material flow systems, production planning, Value-at-Risk, asset and liability management, and optimal trading strategies for financial derivatives (options). Audience: The book is a valuable source of information for faculty, students, researchers, and practitioners in financial engineering, operation research, optimization, computer science, and related areas.
Author: Lishang Jiang
Publisher: World Scientific
ISBN: 9812563695
Category : Science
Languages : en
Pages : 344
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Book Description
From the perspective of partial differential equations (PDE), this book introduces the Black-Scholes-Merton's option pricing theory. A unified approach is used to model various types of option pricing as PDE problems, to derive pricing formulas as their solutions, and to design efficient algorithms from the numerical calculation of PDEs.
Author: Huu Tue Huynh
Publisher: John Wiley & Sons
ISBN: 0470722134
Category : Business & Economics
Languages : en
Pages : 354
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Book Description
Stochastic Simulation and Applications in Finance with MATLAB Programs explains the fundamentals of Monte Carlo simulation techniques, their use in the numerical resolution of stochastic differential equations and their current applications in finance. Building on an integrated approach, it provides a pedagogical treatment of the need-to-know materials in risk management and financial engineering. The book takes readers through the basic concepts, covering the most recent research and problems in the area, including: the quadratic re-sampling technique, the Least Squared Method, the dynamic programming and Stratified State Aggregation technique to price American options, the extreme value simulation technique to price exotic options and the retrieval of volatility method to estimate Greeks. The authors also present modern term structure of interest rate models and pricing swaptions with the BGM market model, and give a full explanation of corporate securities valuation and credit risk based on the structural approach of Merton. Case studies on financial guarantees illustrate how to implement the simulation techniques in pricing and hedging. NOTE TO READER: The CD has been converted to URL. Go to the following website www.wiley.com/go/huyhnstochastic which provides MATLAB programs for the practical examples and case studies, which will give the reader confidence in using and adapting specific ways to solve problems involving stochastic processes in finance.
Author: Eryk Lewinson
Publisher: Packt Publishing Ltd
ISBN: 1789617324
Category : Computers
Languages : en
Pages : 426
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Book Description
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
