Efnisyfirlit

• Cover
• Introduction
  • About This Book
  • Foolish Assumptions
  • Icons Used in This Book
  • Where to Go from Here
• Part 1: Getting Started with Quantitative Finance
  • Chapter 1: Quantitative Finance Unveiled
    • Defining Quantitative Finance
    • Wielding Financial Weapons of Mass Destruction
    • Analysing and Describing Market Behaviour
    • Managing Risk
    • Computing, Algorithms and Markets
  • Chapter 2: Understanding Probability and Statistics
    • Figuring Probability by Flipping a Coin
    • Defining Random Variables
    • Introducing Some Important Distributions
  • Chapter 3: Taking a Look at Random Behaviours
    • Setting Up a Random Walk
    • Averaging with the Central Limit Theorem
    • Moving Like the Stock Market
    • Generating Random Numbers on a Computer
    • Simulating Random Walks
    • Moving Up a Gear
    • Reverting to the Mean
• Part 2: Tackling Financial Instruments
  • Chapter 4: Sizing Up Interest Rates, Shares and Bonds
    • Explaining Interest
    • Sharing in Profits and Growth
    • Taking the Pulse of World Markets
    • Defining Bonds and Bond Jargon
    • Swapping between Fixed and Floating Rates
  • Chapter 5: Exploring Options
    • Examining a Variety of Options
    • Reading Financial Data
    • Getting Paid when Your Option Expires
    • Using Options in Practice
    • Trading Options On and Off Exchanges
    • Relating the Price of Puts and Calls
  • Chapter 6: Trading Risk with Futures
    • Surveying Future Contracts
    • Seeing into the Future
    • Rolling a Position
    • Converging Futures to the Spot Price
    • Using Futures Creatively
    • Seasonality in Futures Prices
• Part 3: Investigating and Describing Market Behaviour
  • Chapter 7: Reading the Market’s Mood: Volatility
    • Defining Volatility
    • Using Historical Data
    • Shrinking Time Using a Square Root
    • Comparing Volatility Calculations
    • Estimating Volatility by Statistical Means
    • Going Beyond Simple Volatility Models
    • Estimating Future Volatility with Term Structures
  • Chapter 8: Analysing All the Data
    • Data Smoothing
    • Estimating More Distributions
    • Modelling Non-Normal Returns
  • Chapter 9: Analysing Data Matrices: Principal Components
    • Reducing the Amount of Data
    • Applying PCA to Yield Curves
    • Using PCA to Build Models
• Part 4: Option Pricing
  • Chapter 10: Examining the Binomial and Black-Scholes Pricing Models
    • Looking at a Simple Portfolio with No Arbitrage
    • Pricing in a Single Step
    • Branching Out in Pricing an Option
    • Making Assumptions about Option Pricing
    • Introducing Black-Scholes – The Most Famous Equation in Quantitative Finance
    • Solving the Black-Scholes Equation
    • Properties of the Black-Scholes Solutions
    • Generalising to Dividend-Paying Stocks
    • Defining other Options
    • Valuing Options Using Simulations
  • Chapter 11: Using the Greeks in the Black-Scholes Model
    • Using the Black-Scholes Formulae
    • Hedging Class
    • That’s Greek to Me: Explaining the Greek Maths Symbols
    • Rebalancing a Portfolio
    • Troubleshooting Model Risk
  • Chapter 12: Gauging Interest-Rate Derivatives
    • Looking at the Yield Curve and Forward Rates
    • Modelling the Interest-Rate
• Part 5: Risk and Portfolio Management
  • Chapter 13: Managing Market Risk
    • Investing in Risky Assets
    • Stopping Losses and other Good Ideas
    • Hedging Schemes
    • Betting without Losing Your Shirt
    • Evaluating Outcomes with Utility Functions
    • Using the Covariance Matrix to Measure Market Risk
  • Chapter 14: Comprehending Portfolio Theory
    • Diversifying Portfolios
    • Minimising Portfolio Variance
    • Capital Asset Pricing Model
    • Assessing Portfolio Performance
  • Chapter 15: Measuring Potential Losses: Value at Risk (VaR)
    • Controlling Risk in Your Portfolio
    • Defining Volatility and the VaR Measure
    • Constructing VaR using the Covariance Matrix
    • Estimating Volatilities and Correlations
    • Simulating the VaR
    • Validating Your Model
    • Including the Average VaR
    • Estimating Tail Risk with Extreme Value Theory
• Part 6: Market Trading and Strategy
  • Chapter 16: Forecasting Markets
    • Measuring with Technical Analysis
    • Making Predictions Using Market Variables
    • Predicting from Past Values
  • Chapter 17: Fitting Models to Data
    • Maximising the Likelihood
    • Fitting and Overfitting
    • Applying Occam’s Razor
    • Detecting Outliers
    • The Curse of Dimensionality
    • Seeing into the Future
  • Chapter 18: Markets in Practice
    • Auctioning Assets
    • Looking at the Price Impact of a Trade
    • Being a Market Maker and Coping with Bid-Ask Spreads
    • Trading Factors and Distributions
• Part 7: The Part of Tens
  • Chapter 19: Ten Key Ideas of Quantitative Finance
    • If Markets Were Truly Efficient Nobody Would Research Them
    • The Gaussian Distribution is Very Helpful but Doesn’t Always Apply
    • Don’t Ignore Trading Costs
    • Know Your Contract
    • Understanding Volatility is Key
    • You Can Price Options by Building Them from Cash and Stock
    • Finance Isn’t Like Physics
    • Diversification is the One True Free Lunch
    • Find Tools to Help Manage All the Data
    • Don’t Get Fooled by Complex Models
  • Chapter 20: Ten Ways to Ace Your Career in Quantitative Finance
    • Follow Financial Markets
    • Read Some Classic Technical Textbooks
    • Read Some Non-technical Books
    • Take a Professional Course
    • Attend Networking Meetings and Conferences
    • Participate in Online Communities
    • Study a Programming Language
    • Go Back to School
    • Apply for that Hedge Fund or Bank Job
    • Take Time to Rest Up and Give Back
• Glossary
• About the Author
• Advertisement Page
• Connect with Dummies
• End User License Agreement

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