دانلود کتاب Quantitative Finance With Python: A Practical Guide to Investment Management, Trading, and Financial Engineering

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Cover Half Title Series Page Title Page Copyright Page Dedication Contents Foreword Author Contributors Acknowledgments SECTION I: Foundations of Quant Modeling CHAPTER 1: Setting the Stage: Quant Landscape 1.1. INTRODUCTION 1.2. QUANT FINANCE INSTITUTIONS 1.2.1. Sell-Side: Dealers & Market Makers 1.2.2. Buy-Side: Asset Managers & Hedge Funds 1.2.3. Financial Technology Firms 1.3. MOST COMMON QUANT CAREER PATHS 1.3.1. Buy Side 1.3.2. Sell Side 1.3.3. Financial Technology 1.3.4. What’s Common between Roles? 1.4. TYPES OF FINANCIAL INSTRUMENTS 1.4.1. Equity Instruments 1.4.2. Debt Instruments 1.4.3. Forwards & Futures 1.4.4. Options 1.4.5. Option Straddles in Practice 1.4.6. Put-Call Parity 1.4.7. Swaps 1.4.8. Equity Index Total Return Swaps in Practice 1.4.9. Over-the-Counter vs. Exchange Traded Products 1.5. STAGES OF A QUANT PROJECT 1.5.1. Data Collection 1.5.2. Data Cleaning 1.5.3. Model Implementation 1.5.4. Model Validation 1.6. TRENDS: WHERE IS QUANT FINANCE GOING? 1.6.1. Automation 1.6.2. Rapid Increase of Available Data 1.6.3. Commoditization of Factor Premias 1.6.4. Movement toward Incorporating Machine Learning/Artificial Intelligence 1.6.5. Increasing Prevalence of Required Quant/Technical Skills CHAPTER 2: Theoretical Underpinnings of Quant Modeling: Modeling the Risk Neutral Measure 2.1. INTRODUCTION 2.2. RISK NEUTRAL PRICING & NO ARBITRAGE 2.2.1. Risk Neutral vs. Actual Probabilities 2.2.2. Theory of No Arbitrage 2.2.3. Complete Markets 2.2.4. Risk Neutral Valuation Equation 2.2.5. Risk Neutral Discounting, Risk Premia & Stochastic Discount Factors 2.3. BINOMIAL TREES 2.3.1. Discrete vs. Continuous Time Models 2.3.2. Scaled Random Walk 2.3.3. Discrete Binomial Tree Model 2.3.4. Limiting Distribution of Binomial Tree Model 2.4. BUILDING BLOCKS OF STOCHASTIC CALCULUS 2.4.1. Deterministic vs. Stochastic Calculus 2.4.2. Stochastic Processes 2.4.3. Martingales 2.4.4. Brownian Motion 2.4.5. Properties of Brownian Motion 2.5. STOCHASTIC DIFFERENTIAL EQUATIONS 2.5.1. Generic SDE Formulation 2.5.2. Bachelier SDE 2.5.3. Black-Scholes SDE 2.5.4. Stochastic Models in Practice 2.6. ITO’S LEMMA 2.6.1. General Formulation & Theory 2.6.2. Ito in Practice: Risk-Free Bond 2.6.3. Ito in Practice: Black-Scholes Dynamics 2.7. CONNECTION BETWEEN SDEs AND PDEs 2.7.1. PDEs & Stochastic Processes 2.7.2. Deriving the Black-Scholes PDE 2.7.3. General Formulation: Feynman-Kac Formula 2.7.4. Working with PDEs in Practice 2.8. GIRSANOV’S THEOREM 2.8.1. Change of Measure via Girsanov’s Theorem 2.8.2. Applications of Girsanov’s Theorem CHAPTER 3: Theoretical Underpinnings of Quant Modeling: Modeling the Physical Measure 3.1. INTRODUCTION: FORECASTING VS. REPLICATION 3.2. MARKET EFFICIENCY AND RISK PREMIA 3.2.1. Efficient Market Hypothesis 3.2.2. Market Anomalies, Behavioral Finance & Risk Premia 3.2.3. Risk Premia Example: Selling Insurance 3.3. LINEAR REGRESSION MODELS 3.3.1. Introduction & Terminology 3.3.2. Univariate Linear Regression 3.3.3. Multivariate Linear Regression 3.3.4. Standard Errors & Significance Tests 3.3.5. Assumptions of Linear Regression 3.3.6. How are Regression Models used in Practice? 3.3.7. Regression Models in Practice: Calculating High-Yield Betas to Stocks and 3.4. TIME SERIES MODELS 3.4.1. Time Series Data 3.4.2. Stationary vs. Non-Stationary Series & Differencing 3.4.3. White Noise & Random Walks 3.4.4. Autoregressive Processes & Unit Root Tests 3.4.5. Moving Average Models 3.4.6. ARMA Models 3.4.7. State Space Models 3.4.8. How are Time Series Models used in practice? 3.5. PANEL REGRESSION MODELS 3.6. CORE PORTFOLIO AND INVESTMENT CONCEPTS 3.6.1. Time Value of Money 3.6.2. Compounding Returns 3.6.3. Portfolio Calculations 3.6.4. Portfolio Concepts in Practice: Benefit of Diversification 3.7. BOOTSTRAPPING 3.7.1. Overview 3.8. PRINCIPAL COMPONENT ANALYSIS 3.9. CONCLUSIONS: COMPARISON TO RISK NEUTRAL MEASURE CHAPTER 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 4.4.1. Language Syntax 4.4.2. Data Types in Python 4.4.3. Working with Built-in Functions 4.4.4. Conditional Statements 4.4.5. Operator Precedence 4.4.6. Loops 4.4.7. Working with Strings 4.4.8. User-Defined Functions 4.4.9. Variable Scope 4.4.10. Importing Modules 4.4.11. Exception Handling 4.4.12. Recursive Functions 4.4.13. Plotting/Visualizations CHAPTER 5: Programming Concepts in Python 5.1. INTRODUCTION 5.2. NUMPY LIBRARY 5.3. PANDAS LIBRARY 5.4. DATA STRUCTURES IN PYTHON 5.4.1. Tuples 5.4.2. Lists 5.4.3. Array 5.4.4. Differences between Lists and NumPy Arrays 5.4.5. Covariance Matrices in Practice 5.4.6. Covariance Matrices in Practice: Are Correlations Stationary? 5.4.7. Series 5.4.8. DataFrame 5.4.9. Dictionary 5.5. IMPLEMENTATION OF QUANT TECHNIQUES IN PYTHON 5.5.1. Random Number Generation 5.5.2. Linear Regression 5.5.3. Linear Regression in Practice: Equity Return Decomposition by Fama-French Factors 5.5.4. Autocorrelation Tests 5.5.5. ARMA Models in Practice: Testing for Mean-Reversion in Equity Index Returns 5.5.6. Matrix Decompositions 5.6. OBJECT-ORIENTED PROGRAMMING IN PYTHON 5.6.1. Principles of Object-Oriented Programming 5.6.2. Classes in Python 5.6.3. Constructors 5.6.4. Destructors 5.6.5. Class Attributes 5.6.6. Class Methods 5.6.7. Class Methods vs. Global Functions 5.6.8. Operator Overloading 5.6.9. Inheritance in Python 5.6.10. Polymorphism in Python 5.7. DESIGN PATTERNS 5.7.1. Types of Design Patterns 5.7.2. Abstract Base Classes 5.7.3. Factory Pattern 5.7.4. Singleton Pattern 5.7.5. Template Method 5.8. SEARCH ALGORITHMS 5.8.1. Binary Search Algorithm 5.9. SORT ALGORITHMS 5.9.1. Selection Sort 5.9.2. Insertion Sort 5.9.3. Bubble Sort 5.9.4. Merge Sort CHAPTER 6: Working with Financial Datasets 6.1. INTRODUCTION 6.2. DATA COLLECTION 6.2.1. Overview 6.2.2. Reading & Writing Files in Python 6.2.3. Parsing Data from a Website 6.2.4. Interacting with Databases in Python 6.3. COMMON FINANCIAL DATASETS 6.3.1. Stock Data 6.3.2. Currency Data 6.3.3. Futures Data 6.3.4. Options Data 6.3.5. Fixed Income Data 6.4. COMMON FINANCIAL DATA SOURCES 6.5. CLEANING DIFFERENT TYPES OF FINANCIAL DATA 6.5.1. Proper Handling of Corporate Actions 6.5.2. Avoiding Survivorship Bias 6.5.3. Detecting Arbitrage in the Data 6.6. HANDLING MISSING DATA 6.6.1. Interpolation & Filling Forward 6.6.2. Filling via Regression 6.6.3. Filling via Bootstrapping 6.6.4. Filling via K-Nearest Neighbor 6.7. OUTLIER DETECTION 6.7.1. Single vs. Multi-Variate Outlier Detection 6.7.2. Plotting 6.7.3. Standard Deviation 6.7.4. Density Analysis 6.7.5. Distance from K-Nearest Neighbor 6.7.6. Outlier Detection in Practice: Identifying Anomalies in ETF Returns CHAPTER 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 7.3.1. Model Documentation 7.3.2. Code Review 7.3.3. Unit Tests 7.3.4. Production Model Change Process 7.4. GOALS OF MODEL VALIDATION 7.4.1. Validating Model Implementation 7.4.2. Understanding Model Strengths and Weaknesses 7.4.3. Identifying Model Assumptions 7.5. TRADEOFF BETWEEN REALISTIC ASSUMPTIONS AND PARSIMONY IN MODELS SECTION II: Options Modeling CHAPTER 8: Stochastic Models 8.1. SIMPLE MODELS 8.1.1. Black-Scholes Model 8.1.2. Black-Scholes Model in Practice: Are Equity Returns Log-Normally Distributed? 8.1.3. Implied Volatility Surfaces in Practice: Equity Options 8.1.4. Bachelier Model 8.1.5. CEV Model 8.1.6. CEV Model in Practice: Impact of Beta 8.1.7. Ornstein-Uhlenbeck Process 8.1.8. Cox-Ingersol-Ross Model 8.1.9. Conclusions 8.2. STOCHASTIC VOLATILITY MODELS 8.2.1. Introduction 8.2.2. Heston Model 8.2.3. SABR Model 8.2.4. SABR Model in Practice: Relationship between Model Parameters and Volatility Surface 8.2.5. Stochastic Volatility Models: Comments 8.3. JUMP DIFFUSION MODELS 8.3.1. Introduction 8.3.2. Merton’s Jump Diffusion Model 8.3.3. SVJ Model 8.3.4. Variance Gamma Model 8.3.5. VGSA Model 8.3.6. Comments on Jump Processes 8.4. LOCAL VOLATILITY MODELS 8.4.1. Dupire’s Formula 8.4.2. Local Volatility Model in Practice: S&P Option Local Volatility Surface 8.5. STOCHASTIC LOCAL VOLATILITY MODELS 8.6. PRACTICALITIES OF USING THESE MODELS 8.6.1. Comparison of Stochastic Models 8.6.2. Leveraging Stochastic Models in Practice CHAPTER 9: Options Pricing Techniques for European Options 9.1. MODELS WITH CLOSED FORM SOLUTIONS OR ASYMPTOTIC APPROXIMATIONS 9.2. OPTION PRICING VIA QUADRATURE 9.2.1. Overview 9.2.2. Quadrature Approximations 9.2.3. Approximating a Pricing Integral via Quadrature 9.2.4. Quadrature Methods in Practice: Digital Options Prices in Black-Scholes vs. Bachelier Model 9.3. OPTION PRICING VIA FFT 9.3.1. Fourier Transforms & Characteristic Functions 9.3.2. European Option Pricing via Transform 9.3.3. Digital Option Pricing via Transform 9.3.4. Calculating Outer Pricing Integral via Quadrature 9.3.5. Summary of FFT Algorithm 9.3.6. Calculating Outer Pricing Integral via FFT 9.3.7. Summary: Option Pricing via FFT 9.3.8. Strike Spacing Functions 9.3.9. Interpolation of Option Prices 9.3.10. Technique Parameters 9.3.11. Dependence on Technique Parameters 9.3.12. Strengths and Weaknesses 9.3.13. Variants of FFT Pricing Technique 9.3.14. FFT Pricing in Practice: Sensitivity to Technique Parameters 9.4. ROOT FINDING 9.4.1. Setup 9.4.2. Newton’s Method 9.4.3. First Calibration: Implied Volatility 9.4.4. Implied Volatility in Practice: Volatility Skew for VIX Options 9.5. OPTIMIZATION TECHNIQUES 9.5.1. Background & Terminology 9.5.2. Global vs. Local Minima & Maxima 9.5.3. First& Second-Order Conditions 9.5.4. Unconstrained Optimization 9.5.5. Lagrange Multipliers 9.5.6. Optimization with Equality Constraints 9.5.7. Minimum Variance Portfolios in Practice: Stock & Bond Minimum Variance Portfolio Weights 9.5.8. Convex Functions 9.5.9. Optimization Methods in Practice 9.6. CALIBRATION OF VOLATILITY SURFACES 9.6.1. Optimization Formulation 9.6.2. Objective Functions 9.6.3. Constraints 9.6.4. Regularization 9.6.5. Gradient-Based vs. Gradient-Free Optimizers 9.6.6. Gradient-Based Methods with Linear Constraints 9.6.7. Practicalities of Calibrating Volatility Surfaces 9.6.8. Calibration in Practice: BRLJPY Currency Options CHAPTER 10: Options Pricing Techniques for Exotic Options 10.1. INTRODUCTION 10.2. SIMULATION 10.2.1. Overview 10.2.2. Central Limit Theorem & Law of Large Numbers 10.2.3. Random Number Generators 10.2.4. Generating Random Variables 10.2.5. Transforming Random Numbers 10.2.6. Transforming Random Numbers: Inverse Transform Technique 10.2.7. Transforming Random Numbers: Acceptance Rejection Method 10.2.8. Generating Normal Random Variables 10.2.9. Quasi Random Numbers 10.2.10. Euler Discretization of SDEs 10.2.11. Simulating from Geometric Brownian Motion 10.2.12. Simulating from the Heston Model 10.2.13. Simulating from the Variance Gamma Model 10.2.14. Variance Reduction Techniques 10.2.15. Strengths and Weaknesses 10.2.16. Simulation in Practice: Impact of Skew on Lookback Options Values in the Heston Model 10.3. NUMERICAL SOLUTIONS TO PDEs 10.3.1. Overview 10.3.2. PDE Representations of Stochastic Processes 10.3.3. Finite Differences 10.3.4. Time & Space Grid 10.3.5. Boundary Conditions 10.3.6. Explicit Scheme 10.3.7. Implicit Scheme 10.3.8. Crank-Nicolson 10.3.9. Stability 10.3.10. Multi-Dimension PDEs 10.3.11. Partial Integro Differential Equations 10.3.12. Strengths & Weaknesses 10.3.13. American vs. European Digital Options in Practice 10.4. MODELING EXOTIC OPTIONS IN PRACTICE CHAPTER 11: Greeks and Options Trading 11.1. INTRODUCTION 11.2. BLACK-SCHOLES GREEKS 11.2.1. Delta 11.2.2. Gamma 11.2.3. Delta and Gamma in Practice: Delta and Gamma by Strike 11.2.4. Theta 11.2.5. Theta in Practice: How Does Theta Change by Option Expiry? 11.2.6. Vega 11.2.7. Practical Uses of 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 11.7.1. Smile Adjusted Greeks in Practice: USDBRL Options 11.8. HEDGING IN PRACTICE 11.8.1. Re-Balancing Strategies 11.8.2. Delta Hedging in Practice 11.8.3. Vega Hedging in Practice 11.8.4. Validation of Greeks Out-of-Sample 11.9. COMMON OPTIONS TRADING STRUCTURES 11.9.1. Benefits of Trading Options 11.9.2. Covered Calls 11.9.3. Call & Put Spreads 11.9.4. Straddles & Strangles 11.9.5. Butterflies 11.9.6. Condors 11.9.7. Calendar Spreads 11.9.8. Risk Reversals 11.9.9. 1x2s 11.10. VOLATILITY AS AN ASSET CLASS 11.11. RISK PREMIA IN THE OPTIONS MARKET: IMPLIED VS. REALIZED VOLATILITY 11.11.1. Delta-Hedged Straddles 11.11.2. Implied vs. Realized Volatility 11.11.3. Implied Volatility Premium in Practice: S&P 500 11.12. CASE STUDY: GAMESTOP REDDIT MANIA CHAPTER 12: Extraction of Risk Neutral Densities 12.1. MOTIVATION 12.2. BREDEN-LITZENBERGER 12.2.1. Derivation 12.2.2. Breeden-Litzenberger in the Presence of Imprecise Data 12.2.3. Strengths and Weaknesses 12.2.4. Applying Breden-Litzenberger in Practice 12.3. CONNECTION BETWEEN RISK NEUTRAL DISTRIBUTIONS AND MARKET INSTRUMENTS 12.3.1. Butterflies 12.3.2. Digital Options 12.4. OPTIMIZATION FRAMEWORK FOR NON-PARAMETRIC DENSITY EXTRACTION 12.5. WEIGTHED MONTE CARLO 12.5.1. Optimization Directly on Terminal Probabilities 12.5.2. Inclusion of a Prior Distribution 12.5.3. Weighting Simulated Paths Instead of Probabilities 12.5.4. Strengths and Weaknesses 12.5.5. Implementation of Weighted Monte Carlo in Practice: S&P Options 12.6. RELATIONSHIP BETWEEN VOLATILITY SKEW AND RISK NEUTRAL DENSITIES 12.7. RISK PREMIA IN THE OPTIONS MARKET: COMPARISON OF RISK NEUTRAL VS. PHYSICAL MEASURES 12.7.1. Comparison of Risk Neutral vs. Physical Measure: Example 12.7.2. Connection to Market Implied Risk Premia 12.7.3. Taking Advantage of Deviations between the Risk Neutral & Physical Measure 12.8. CONCLUSIONS & ASSESSMENT OF PARAMETRIC VS. NON-PARAMETRIC METHODS SECTION III: Quant Modeling in Different Markets CHAPTER 13: Interest Rate Markets 13.1. MARKET SETTING 13.2. BOND PRICING CONCEPTS 13.2.1. Present Value & Discounting Cashflows 13.2.2. Pricing a Zero Coupon Bond 13.2.3. Pricing a Coupon Bond 13.2.4. Daycount Conventions 13.2.5. Yield to Maturity 13.2.6. Duration & Convexity 13.2.7. Bond Pricing in Practice: Duration and Convexity vs. Maturity 13.2.8. From Yield to Maturity to a Yield Curve 13.3. MAIN COMPONENTS OF A YIELD CURVE 13.3.1. Overview 13.3.2. FRA’s & Eurodollar Futures 13.3.3. Swaps 13.4. MARKET RATES 13.5. YIELD CURVE CONSTRUCTION 13.5.1. Motivation 13.5.2. Libor vs. OIS 13.5.3. Bootstrapping 13.5.4. Optimization 13.5.5. Comparison of Methodologies 13.5.6. Bootstrapping in Practice: US Swap Rates 13.5.7. Empirical Observations of the Yield Curve 13.5.8. Fed Policy and the Yield Curve 13.6. MODELING INTEREST RATE DERIVATIVES 13.6.1. Linear vs. Non-Linear Payoffs 13.6.2. Vanilla vs. Exotic Options 13.6.3. Most Common Interest Rate Derivatives 13.6.4. Modeling the Curve vs. Modeling a Single Rate 13.7. MODELING VOLATILITY FOR A SINGLE RATE: CAPS/FLOORS 13.7.1. T-Forward Numeraire 13.7.2. Caplets/Floorlets via Black’s Model 13.7.3. Stripping Cap/Floor Volatilities 13.7.4. Fitting the Volatility Skew 13.8. MODELING VOLATILITY FOR A SINGLE RATE: SWAPTIONS 13.8.1. Annuity Function & Numeraire 13.8.2. Pricing via the Bachelier Model 13.8.3. Fitting the Volatility Skew with the SABR Model 13.8.4. Swaption Volatility Cube 13.9. MODELING THE TERM STRUCTURE: SHORT RATE MODELS 13.9.1. Short Rate Models: Overview 13.9.2. Ho-Lee 13.9.3. Vasicek 13.9.4. Cox Ingersol Ross 13.9.5. Hull-White 13.9.6. Multi-Factor Short Rate Models 13.9.7. Two Factor Gaussian Short Rate Model 13.9.8. Two Factor Hull-White Model 13.9.9. Short Rate Models: Conclusions 13.10. MODELING THE TERM STRUCTURE: FORWARD RATE MODELS 13.10.1. Libor Market Models: Introduction 13.10.2. Log-Normal Libor Market Model 13.10.3. SABR Libor Market Model 13.10.4. Valuation of Swaptions in an LMM Framework 13.11. EXOTIC OPTIONS 13.11.1. Spread Options 13.11.2. Bermudan Swaptions 13.12. INVESTMENT PERSPECTIVE: TRADED STRUCTURES 13.12.1. Hedging Interest Rate Risk in Practice 13.12.2. Harvesting Carry in Rates Markets: Swaps 13.12.3. Swaps vs. Treasuries Basis Trade 13.12.4. Conditional Flattener/Steepeners 13.12.5. Triangles: Swaptions vs. Mid-Curves 13.12.6. Wedges: Caps vs. Swaptions 13.12.7. Berm vs. Most Expensive European 13.13. CASE STUDY: INTRODUCTION OF NEGATIVE RATES CHAPTER 14: Credit Markets 14.1. MARKET SETTING 14.2. MODELING DEFAULT RISK: HAZARD RATE MODELS 14.3. RISKY BOND 14.3.1. Modeling Risky Bonds 14.3.2. Bonds in Practice: Comparison of Risky & Risk-Free Bond Duration 14.4. CREDIT DEFAULT SWAPS 14.4.1. Overview 14.4.2. Valuation of CDS 14.4.3. Risk Annuity vs. IR Annuity 14.4.4. Credit Triangle 14.4.5. Mark to Market of a CDS 14.4.6. Market Risks of CDS 14.5. CDS VS. CORPORATE BONDS 14.5.1. CDS Bond Basis 14.5.2. What Drives the CDS-Bond Basis? 14.6. BOOTSTRAPPING A SURVIVAL CURVE 14.6.1. Term Structure of Hazard Rates 14.6.2. CDS Curve: Bootstrapping Procedure 14.6.3. Alternate Approach: Optimization 14.7. INDICES OF CREDIT DEFAULT SWAPS 14.7.1. Credit Indices 14.7.2. Valuing Credit Indices 14.7.3. Index vs. Single Name Basis 14.7.4. Credit Indices in Practice: Extracting IG & HY Index Hazard Rates 14.8. MARKET IMPLIED VS EMPIRICAL DEFAULT PROBABILITIES 14.9. OPTIONS ON CDS & CDX INDICES 14.9.1. Options on CDS 14.9.2. Options on Indices 14.10. MODELING CORRELATION: CDOS 14.10.1. CDO Subordination Structure 14.10.2. Mechanics of CDOs 14.10.3. Default Correlation & the Tranche Loss Distribution 14.10.4. A Simple Model for CDOs: One Factor Large Pool Homogeneous Model 14.10.5. Correlation Skew 14.10.6. CDO Correlation in Practice: Impact of Correlation on Tranche Valuation 14.10.7. Alternative Models for CDOs 14.11. MODELS CONNECTING EQUITY AND CREDIT 14.11.1. Merton’s Model 14.11.2. Hirsa-Madan Approach 14.12. MORTGAGE BACKED SECURITIES 14.13. INVESTMENT PERSPECTIVE: TRADED STRUCTURES 14.13.1. Hedging Credit Risk 14.13.2. Harvesting Carry in Credit Markets 14.13.3. CDS Bond Basis 14.13.4. Trading Credit Index Calendar Spreads 14.13.5. Correlation Trade: Mezzanine vs. Equity Tranches CHAPTER 15: Foreign Exchange Markets 15.1. MARKET SETTING 15.1.1. Overview 15.1.2. G10 Major Currencies 15.1.3. EM Currencies 15.1.4. Major Players 15.1.5. Derivatives Market Structure 15.2. MODELING IN A CURRENCY SETTING 15.2.1. FX Quotations 15.2.2. FX Forward Valuations 15.2.3. Carry in FX Markets: Do FX forward Realize? 15.2.4. Deliverable vs. Non-Deliverable Forwards 15.2.5. FX Triangles 15.2.6. Black-Scholes Model in an FX Setting 15.2.7. Quoting Conventions in FX Vol. Surfaces 15.3. VOLATILITY SMILES IN FOREIGN EXCHANGE MARKETS 15.3.1. Persistent Characteristics of FX Volatility Surfaces 15.3.2. FX Volatility Surfaces in Practice: Comparison across Currency Pairs 15.4. EXOTIC OPTIONS IN FOREIGN EXCHANGE MARKETS 15.4.1. Digital Options 15.4.2. One Touch Options 15.4.3. One-Touches vs. Digis in Practice: Ratio of Prices in EURJPY 15.4.4. Asian Options 15.4.5. Barrier Options 15.4.6. Volatility & Variance Swaps 15.4.7. Dual Digitals 15.5. INVESTMENT PERSPECTIVE: TRADED STRUCTURES 15.5.1. Hedging Currency Risk 15.5.2. Harvesting Carry in FX Markets 15.5.3. Trading Dispersion: Currency Triangles 15.5.4. Trading Skewness: Digital Options vs. One Touches 15.6. CASE STUDY: CHF PEG BREAK IN 2015 CHAPTER 16: Equity & Commodity Markets 16.1. MARKET SETTING 16.2. FUTURES CURVES IN EQUITY & COMMODITY MARKETS 16.2.1. Determinants of Futures Valuations 16.2.2. Futures Curves of Hard to Store Assets 16.2.3. Why Are VIX & Commodity Curves Generally in Contango? 16.2.4. Futures Curves In Practice: Excess Contango in Natural Gas & VIX 16.3. VOLATILITY SURFACES IN EQUITY & COMMODITY MARKETS 16.3.1. Persistent Characteristics of Equity & Commodity Volatility Surfaces 16.4. EXOTIC OPTIONS IN EQUITY & COMMODITY MARKETS 16.4.1. Lookback Options 16.4.2. Basket Options 16.5. INVESTMENT PERSPECTIVE: TRADED STRUCTURES 16.5.1. Hedging Equity Risk 16.5.2. Momentum in Single Stocks 16.5.3. Harvesting Roll Yield via Commodity Futures Curves 16.5.4. Lookback vs. European 16.5.5. Dispersion Trading: Index vs. Single Names 16.5.6. Leveraged ETF Decay 16.6. CASE STUDY: NAT. GAS SHORT SQUEEZE 16.7. CASE STUDY: VOLATILITY ETP APOCALYPSE OF 2018 SECTION IV: Portfolio Construction & Risk Management CHAPTER 17: Portfolio Construction & Optimization Techniques 17.1. THEORETICAL BACKGROUND 17.1.1. Physical vs. Risk-Neutral Measure 17.1.2. First-& Second-Order Conditions, Lagrange Multipliers 17.1.3. Interpretation of Lagrange Multipliers 17.2. MEAN-VARIANCE OPTIMIZATION 17.2.1. Investor Utility 17.2.2. Unconstrained Mean-Variance Optimization 17.2.3. Mean-Variance Efficient Frontier 17.2.4. Mean-Variance Fully Invested Efficient Frontier 17.2.5. Mean-Variance Optimization in Practice: Efficient Frontier 17.2.6. Fully Invested Minimum Variance Portfolio 17.2.7. Mean-Variance Optimization with Inequality Constraints 17.2.8. Most Common Constraints 17.2.9. Mean-Variance Optimization: Market or Factor Exposure Constraints 17.2.10. Mean-Variance Optimization: Turnover Constraint 17.2.11. Minimizing Tracking Error to a Benchmark 17.2.12. Estimation of Portfolio Optimization Inputs 17.3. CHALLENGES ASSOCIATED WITH MEAN-VARIANCE OPTIMIZATION 17.3.1. Estimation Error in Expected Returns 17.3.2. Mean-Variance Optimization in Practice: Impact of Estimation Error 17.3.3. Estimation Error of Variance Estimates 17.3.4. Singularity of Covariance Matrices 17.3.5. Mean-Variance Optimization in Practice: Analysis of Covariance Matrices 17.3.6. Non-Stationarity of Asset Correlations 17.4. CAPITAL ASSET PRICING MODEL 17.4.1. Leverage & the Tangency Portfolio 17.4.2. CAPM 17.4.3. Systemic vs. Idiosyncratic Risk 17.4.4. CAPM in Practice: Efficient Frontier, Tangency Portfolio and Leverage 17.4.5. Multi-Factor Models 17.4.6. Fama-French Factors 17.5. BLACK-LITTERMAN 17.5.1. Market Implied Equilibrium Expected Returns 17.5.2. Bayes’ Rule 17.5.3. Incorporating Subjective Views 17.5.4. The Black-Litterman Model 17.6. RESAMPLING 17.6.1. Resampling the Efficient Frontier 17.6.2. Resampling in Practice: Comparison to a Mean-Variance Efficient Frontier 17.7. DOWNSIDE RISK BASED OPTIMIZATION 17.7.1. Value at Risk (VaR) 17.7.2. Conditional Value at Risk (CVaR) 17.7.3. Mean-VaR Optimal Portfolio 17.7.4. Mean-CVaR Optimal Portfolio 17.8. RISK PARITY 17.8.1. Introduction 17.8.2. Inverse Volatility Weighting 17.8.3. Marginal Risk Contributions 17.8.4. Risk Parity Optimization Formulation 17.8.5. Strengths and Weaknesses of Risk Parity 17.8.6. Asset Class Risk Parity Portfolio in Practice 17.9. COMPARISON OF METHODOLOGIES CHAPTER 18: Modeling Expected Returns and Covariance Matrices 18.1. SINGLE & MULTI-FACTOR MODELS FOR EXPECTED RETURNS 18.1.1. Building Expected Return Models 18.1.2. Employing Regularization Techniques 18.1.3. Regularization Techniques in Practice: Impact on Expected Return Model 18.1.4. Correcting for Serial Correlation 18.1.5. Isolating Signal from Noise 18.1.6. Information Coefficient 18.1.7. Information Coefficient in Practice: Rolling IC of a Short Term FX Reversal 18.1.8. The Fundamental Law of Active Management: Relationship between Information Ratio & Information Coefficient 18.2. MODELING VOLATILITY 18.2.1. Estimating Volatility 18.2.2. Rolling & Expanding Windows Volatility Estimates 18.2.3. Exponentially Weighted Moving Average Estimates 18.2.4. High Frequency & Range Based Volatility Estimators 18.2.5. Mean-Reverting Volatility Models: GARCH 18.2.6. GARCH in Practice: Estimation of GARCH(1,1) Parameters to Equity Index Returns 18.2.7. Estimation of Covariance Matrices 18.2.8. Correcting for Negative Eigenvalues 18.2.9. Shrinkage Methods for Covariance Matrices 18.2.10. Shrinkage in Practice: Impact on Structure of Principal Components 18.2.11. Random Matrix Theory CHAPTER 19: Risk Management 19.1. MOTIVATION & SETTING 19.1.1. Risk Management in Practice 19.1.2. Defined vs. Undefined Risks 19.1.3. Types of Risk 19.2. COMMON RISK MEASURES 19.2.1. Portfolio Value at Risk 19.2.2. Marginal VaR Contribution 19.2.3. Portfolio Conditional Value at Risk 19.2.4. Marginal CVaR Contribution 19.2.5. Extreme Loss, Stress Tests & Scenario Analysis 19.3. CALCULATION OF PORTFOLIO VaR AND CVaR 19.3.1. Overview 19.3.2. Historical Simulation 19.3.3. Monte Carlo Simulation 19.3.4. Strengths and Weaknesses of Each Approach 19.3.5. Validating Our Risk Calculations Out-of-Sample 19.3.6. VaR in Practice: Out of Sample Test of Rolling VaR 19.4. RISK MANAGEMENT OF NON-LINEAR INSTRUMENTS 19.4.1. Non-Linear Risk 19.4.2. Hedging Portfolios via Scenarios 19.5. RISK MANAGEMENT IN RATES & CREDIT MARKETS 19.5.1. Introduction 19.5.2. Converting from Change in Yield to Change in Price 19.5.3. DV01 and Credit Spread 01: Risk Management via Parallel Shifts 19.5.4. Partial DV01’s: Risk Management via Key Rate Shifts 19.5.5. Jump to Default Risk 19.5.6. Principal Component Based Shifts CHAPTER 20: Quantitative Trading Models 20.1. INTRODUCTION TO QUANT TRADING MODELS 20.1.1. Quant Strategies 20.1.2. What is Alpha Research? 20.1.3. Types of Quant Strategies 20.2. BACK-TESTING 20.2.1. Parameter Estimation 20.2.2. Modeling Transactions Costs 20.2.3. Evaluating Back-Test Performance 20.2.4. Most Common Quant Traps 20.2.5. Common Performance Metrics 20.2.6. Back-Tested Sharpe Ratios 20.2.7. In-Sample and Out-of-Sample Analysis 20.2.8. Out-of-Sample Performance & Slippage 20.3. COMMON STAT-ARB STRATEGIES 20.3.1. Single Asset Momentum & Mean-Reversion Strategies 20.3.2. Cross Asset Autocorrelation Strategies 20.3.3. Pairs Trading 20.3.4. Pairs Trading in Practice: Gold vs. Gold Miners 20.3.5. Factor Models 20.3.6. PCA-Based Strategies 20.3.7. PCA Decomposition in Practice: How Many Principal Components Explain the S&P 500? 20.3.8. Risk Premia Strategies 20.3.9. Momentum in Practice: Country ETFs 20.3.10. Translating Raw Signals to Positions 20.4. SYSTEMATIC OPTIONS BASED STRATEGIES 20.4.1. Back-Testing Strategies Using Options 20.4.2. Common Options Trading Strategies 20.4.3. Options Strategy in Practice: Covered Calls on NASDAQ 20.5. COMBINING QUANT STRATEGIES 20.6. PRINCIPLES OF DISCRETIONARY VS. SYSTEMATIC INVESTING CHAPTER 21: Incorporating Machine Learning Techniques 21.1. MACHINE LEARNING FRAMEWORK 21.1.1. Machine Learning vs. Econometrics 21.1.2. Stages of a Machine Learning Project 21.1.3. Parameter Tuning & Cross Validation 21.1.4. Classes of Machine Learning Algorithms 21.1.5. Applications of Machine Learning in Asset Management & Trading 21.1.6. Challenges of Using Machine Learning in Finance 21.2. SUPERVISED VS. UNSUPERVISED LEARNING METHODS 21.2.1. Supervised vs. Unsupervised Learning 21.2.2. Supervised Learning Methods 21.2.3. Regression vs. Classification Techniques 21.2.4. Unsupervised Learning Methods 21.3. CLUSTERING 21.3.1. What is Clustering? 21.3.2. K-Means Clustering 21.3.3. Hierarchical Clustering 21.3.4. Distance Metrics 21.3.5. Optimal Number of Clusters 21.3.6. Clustering in Finance 21.3.7. Clustering in Practice: Asset Class & Risk-on Risk-off Clusters 21.4. CLASSIFICATION TECHNIQUES 21.4.1. What is Classification? 21.4.2. K-Nearest Neighbor 21.4.3. Probit Regression 21.4.4. Logistic Regression 21.4.5. Support Vector Machines 21.4.6. Confusion Matrices 21.4.7. Classification Problems in Finance 21.4.8. Classification in Practice: Using Classification Techniques in an Alpha Signal 21.5. FEATURE IMPORTANCE & INTERPRETABILITY 21.5.1. Feature Importance & Interpretability 21.6. OTHER APPLICATIONS OF MACHINE LEARNING 21.6.1. Delta Hedging Schemes & Optimal Execution via Reinforcement Learning 21.6.2. Credit Risk Modeling via Classification Techniques 21.6.3. Incorporating Alternative Data via Natural Language Processing (NLP) Algorithms and Other Machine Learning Techniques 21.6.4. Volatility Surface Calibration via Deep Learning Bibliography Index