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

Length: 664 pages
Edition: 1
Language: English
Publisher: Chapman & Hall
Publication Date: 2022-05-20
ISBN-10: 1032014431
ISBN-13: 9781032014432
Sales Rank: #4513892 (See Top 100 Books)

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.

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