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Nonparametric Finance

Book Description

An Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R

Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function

estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end.

Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance

is emphasized over statistical significance throughout, and R code is provided to help readers reproduce the research, computations, and figures being discussed. Strong graphical content clarifies the methods and demonstrates essential visualization techniques, while deep mathematical and statistical insight backs up practical applications.

Written for the leading edge of finance, Nonparametric Finance:

• Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction

• Provides risk management guidance through volatility prediction, quantiles, and value-at-risk

• Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more

• Discusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles

• Provides supplementary R code and numerous graphics to reinforce complex content

Nonparametric function estimation has received little attention in the context of risk management and option pricing, despite its useful applications and benefits. This book provides the essential background and practical knowledge needed to take full advantage of these little-used methods, and turn them into real-world advantage.

Jussi Klemelä, PhD, is Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of Smoothing of Multivariate Data: Density Estimation and Visualization and Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance.

Table of Contents

  1. Cover
  2. Title Page
    1. Copyright
  3. Preface
    1. Chapter 1: Introduction
      1. 1.1 Statistical Finance
      2. 1.2 Risk Management
      3. 1.3 Portfolio Management
      4. 1.4 Pricing of Securities
  4. Part I: Statistical Finance
    1. Chapter 2: Financial Instruments
      1. 2.1 Stocks
      2. 2.2 Fixed Income Instruments
      3. 2.3 Derivatives
      4. 2.4 Data Sets
    2. Chapter 3: Univariate Data Analysis
      1. 3.1 Univariate Statistics
      2. 3.2 Univariate Graphical Tools
      3. 3.3 Univariate Parametric Models
      4. 3.4 Tail Modeling
      5. 3.5 Asymptotic Distributions
      6. 3.6 Univariate Stylized Facts
    3. Chapter 4: Multivariate Data Analysis
      1. 4.1 Measures of Dependence
      2. 4.2 Multivariate Graphical Tools
      3. 4.3 Multivariate Parametric Models
      4. 4.4 Copulas
    4. Chapter 5: Time Series Analysis
      1. 5.1 Stationarity and Autocorrelation
      2. 5.2 Model Free Estimation
      3. 5.3 Univariate Time Series Models
      4. 5.4 Multivariate Time Series Models
      5. 5.5 Time Series Stylized Facts
    5. Chapter 6: Prediction
      1. 6.1 Methods of Prediction
      2. 6.2 Forecast Evaluation
      3. 6.3 Predictive Variables
      4. 6.4 Asset Return Prediction
  5. Part II: Risk Management
    1. Chapter 7: Volatility Prediction
      1. 7.1 Applications of Volatility Prediction
      2. 7.2 Performance Measures for Volatility Predictors
      3. 7.3 Conditional Heteroskedasticity Models
      4. 7.4 Moving Average Methods
      5. 7.5 State Space Predictors
    2. Chapter 8: Quantiles and Value-at-Risk
      1. 8.1 Definitions of Quantiles
      2. 8.2 Applications of Quantiles
      3. 8.3 Performance Measures for Quantile Estimators
      4. 8.4 Nonparametric Estimators of Quantiles
      5. 8.5 Volatility Based Quantile Estimation
      6. 8.6 Excess Distributions in Quantile Estimation
      7. 8.7 Extreme Value Theory in Quantile Estimation
      8. 8.8 Expected Shortfall
  6. Part III: Portfolio Management
    1. Chapter 9: Some Basic Concepts of Portfolio Theory
      1. 9.1 Portfolios and Their Returns
      2. 9.2 Comparison of Return and Wealth Distributions
      3. 9.3 Multiperiod Portfolio Selection
    2. Chapter 10: Performance Measurement
      1. 10.1 The Sharpe Ratio
      2. 10.2 Certainty Equivalent
      3. 10.3 Drawdown
      4. 10.4 Alpha and Conditional Alpha
      5. 10.5 Graphical Tools of Performance Measurement
    3. Chapter 11: Markowitz Portfolios
      1. 11.1 Variance Penalized Expected Return
      2. 11.2 Minimizing Variance under a Sufficient Expected Return
      3. 11.3 Markowitz Bullets
      4. 11.4 Further Topics in Markowitz Portfolio Selection
      5. 11.5 Examples of Markowitz Portfolio Selection
    4. Chapter 12: Dynamic Portfolio Selection
      1. 12.1 Prediction in Dynamic Portfolio Selection
      2. 12.2 Backtesting Trading Strategies
      3. 12.3 One Risky Asset
      4. 12.4 Two Risky Assets
  7. Part IV: Pricing of Securities
    1. Chapter 13: Principles of Asset Pricing
      1. 13.1 Introduction to Asset Pricing
      2. 13.2 Fundamental Theorems of Asset Pricing
      3. 13.3 Evaluation of Pricing and Hedging Methods
    2. Chapter 14: Pricing by Arbitrage
      1. 14.1 Futures and the Put–Call Parity
      2. 14.2 Pricing in Binary Models
      3. 14.3 Black–Scholes Pricing
      4. 14.4 Black–Scholes Hedging
      5. 14.5 Black–Scholes Hedging and Volatility Estimation
    3. Chapter 15: Pricing in Incomplete Models
      1. 15.1 Quadratic Hedging and Pricing
      2. 15.2 Utility Maximization
      3. 15.3 Absolutely Continuous Changes of Measures
      4. 15.4 GARCH Market Models
      5. 15.5 Nonparametric Pricing Using Historical Simulation
      6. 15.6 Estimation of the Risk-Neutral Density
      7. 15.7 Quantile Hedging
    4. Chapter 16: Quadratic and Local Quadratic Hedging
      1. 16.1 Quadratic Hedging
      2. 16.2 Local Quadratic Hedging
      3. 16.3 Implementations of Local Quadratic Hedging
    5. Chapter 17: Option Strategies
      1. 17.1 Option Strategies
      2. 17.2 Profitability of Option Strategies
    6. Chapter 18: Interest Rate Derivatives
      1. 18.1 Basic Concepts of Interest Rate Derivatives
      2. 18.2 Interest Rate Forwards
      3. 18.3 Interest Rate Options
      4. 18.4 Modeling Interest Rate Markets
  8. References
    1. Index
  9. End User License Agreement