Quantitative Finance with Python - Kelliher, Chris

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Résumé :
This book bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance....

Biographie:

Section I. Foundations of Quant Modeling. 1. Setting the Stage: Quant Landscape. 1.1. Introduction. 1.2. Quant Finance Institutions. 1.3. Most Common Quant Career Paths. 1.4. Types of Financial Instruments. 1.5. Stages of a Quant Project. 1.6. Trends: Where is Quant Finance Going? 2. Theoretical Underpinnings of Quant Modeling: Modeling the Risk Neutral Measure. 2.1. Introduction. 2.2. Risk Neutral Pricing & No Arbitrage. 2.3. Binomial Trees. 2.4. Building Blocks of Stochastic Calculus. 2.5. Stochastic Differential Equations. 2.6. It?'s Lemma. 2.7. Connection between SDEs and PDES. 2.8. Girsanov's Theorem. 3. Theoretical Underpinnings of Quant Modeling: Modeling the Physical Measure. 3.1. Introduction: Forecasting vs. Replication. 3.2. Market Efficiency and Risk Premia. 3.3. Linear Regression Models. 3.4. Time Series Models. 3.5. Panel Regression Models. 3.6. Core Portfolio and Investment Concepts. 3.7. Bootstrapping. 3.8. Principal Component Analysis. 3.9. Conclusions: Comparison to Risk Neutral Measure Modelling. 4. Python Programming Environment. 4.1. The Python Programming Language. 4.2. Advantages and Disadvantages of Python. 4.3. Python Development Environments. 4.4. Basic Programming Concepts in Python. 5. Programming Concepts in Python. 5.1. Introduction. 5.2. Numpy Library. 5.3. Pandas Library. 5.4. Data Structures in Python. 5.5. Implementation of Quant Techniques in Python. 5.6. Object-Oriented Programming in Python. 5.7. Design Patterns. 5.8. Search Algorithms. 5.9. Sort Algorithms. 6. Working with Financial Datasets. 6.1. Introduction. 6.2. Data Collection. 6.3. Common Financial Datasets. 6.4. Common Financial Data Sources. 6.5. Cleaning Different Types of Financial Data. 6.6. Handling Missing Data. 6.7. Outlier Detection. 7. Model Validation. 7.1. Why Is Model Validation So Important? 7.2. How Do We Ensure Our Models Are Correct? 7.3. Components of a Model Validation Process. 7.4. Goals of Model Validation. 7.5. Trade-off between Realistic Assumptions and Parsimony in Models. Section II. Options Modeling. 8. Stochastic Models. 8.1. Simple Models. 8.2. Stochastic Volatility Models. 8.3. Jump Diffusion Models. 8.4. Local Volatility Models. 8.5. Stochastic Local Volatility Models. 8.6. Practicalities of using these Models. 9. Options Pricing Techniques for European Options. 9.1. Models with Closed Form Solutions or Asymptotic Approximations. 9.2. Option Pricing via Quadrature. 9.3. Option Pricing via FFT. 9.4. Root Finding. 9.5. Optimization Techniques. 9.6. Calibration of Volatility Surfaces. 10. Options Pricing Techniques for Exotic Options. 10.1. Introduction. 10.2. Simulation. 10.3. Numerical Solutions to PDEs. 10.4. Modeling Exotic Options in Practice. 11. Greeks and Options Trading. 11.1. Introduction. 11.2. Black-Scholes Greeks. 11.3. Theta vs. Gamma. 11.4. Model Dependence of Greeks. 11.5. Greeks for Exotic Options. 11.6. Estimation of Greeks via Finite Differences. 11.7. Smile Adjusted Greeks. 11.8. Hedging in Practice. 11.9. Common Options Trading Structures. 11.10. Volatility as an Asset Class. 11.11. Risk Premia in the Options Market: Implied vs. Realized Volatility. 11.12. Case Study: GameStop Reddit Mania. 12. Extraction of Risk Neutral Densities. 12.1. Motivation. 12.2. Breden-Litzenberger. 12.3. Connection Between Risk Neutral Distributions and Market Instruments. 12.4. Optimization Framework for Non-Parametric Density Extraction. 12.5. Weigthed Monte Carlo. 12.6. Relationship between Volatility skew and Risk Neutral Densities. 12.7. Risk Premia in the Options Market: Comparison OF Risk Neutral vs. Ph...

Sommaire:

Chris Kelliher is a Senior Quantitative Researcher in the Global Asset Allocation group at Fidelity Investments. In addition, Mr. Kelliher is a Lecturer in the Masters in Mathematical Finance and Financial Technology program at Boston University's Questrom School of Business. In this role he teaches multiple graduate level courses including Computational Methods in Finance, Fixed Income & Programming for Quant Finance. Prior to joining Fidelity in 2019, Mr. Kelliher served as a portfolio manager for RDC Capital Partners. Before joining RDC, Mr. Kelliher served as a principal and quantitative portfolio manager at a leading quantitative investment management firm, FDO Partners. Prior to FDO, Mr. Kelliher was a senior quantitative portfolio analyst and trader at Convexity Capital Management and a senior quantitative researcher at Bracebridge Capital. He has been in the financial industry since 2004. Mr. Kelliher earned a BA in Economics from Gordon College, where he graduated Cum Laude with Departmental Honours, and an MS in Mathematical Finance from New York University's Courant Institute.