{"product_id":"nonparametric-finance-isbn-9781119409106","title":"Nonparametric Finance","description":"\u003cp\u003e\u003cb\u003eAn Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. \u003ci\u003eNonparametric Finance \u003c\/i\u003eprovides graduate students and finance professionals with a foundation in nonparametric function\u003c\/p\u003e \u003cp\u003eestimation 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.\u003c\/p\u003e \u003cp\u003eCoverage 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\u003c\/p\u003e \u003cp\u003eis 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.\u003c\/p\u003e \u003cp\u003eWritten for the leading edge of finance, \u003ci\u003eNonparametric Finance:\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e• Introduces basic statistical finance concepts, including univariate and multivariate data analysis, time series analysis, and prediction\u003c\/p\u003e \u003cp\u003e• Provides risk management guidance through volatility prediction, quantiles, and value-at-risk\u003c\/p\u003e \u003cp\u003e• Examines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more\u003c\/p\u003e \u003cp\u003e• 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\u003c\/p\u003e \u003cp\u003e• Provides supplementary R code and numerous graphics to reinforce complex content\u003c\/p\u003e \u003cp\u003eNonparametric 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJussi Klemelä, PhD, \u003c\/b\u003eis Adjunct Professor at the University of Oulu. His research interests include nonparametric function estimation, density estimation, and data visualization. He is the author of \u003ci\u003eSmoothing of Multivariate Data: Density Estimation and Visualization\u003c\/i\u003e and \u003ci\u003eMultivariate Nonparametric Regression and Visualization: With R and Applications\u003c\/i\u003e \u003ci\u003eto Finance.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003ePreface xiii \u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e1.1 Statistical Finance 2 \u003c\/p\u003e \u003cp\u003e1.2 Risk Management 3 \u003c\/p\u003e \u003cp\u003e1.3 Portfolio Management 5 \u003c\/p\u003e \u003cp\u003e1.4 Pricing of Securities 6 \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Statistical Finance 11\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Financial Instruments 13\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e2.1 Stocks 13 \u003c\/p\u003e \u003cp\u003e2.1.1 Stock Indexes 14 \u003c\/p\u003e \u003cp\u003e2.1.2 Stock Prices and Returns 15 \u003c\/p\u003e \u003cp\u003e2.2 Fixed Income Instruments 19 \u003c\/p\u003e \u003cp\u003e2.2.1 Bonds 19 \u003c\/p\u003e \u003cp\u003e2.2.2 Interest Rates 20 \u003c\/p\u003e \u003cp\u003e2.2.3 Bond Prices and Returns 22 \u003c\/p\u003e \u003cp\u003e2.3 Derivatives 23 \u003c\/p\u003e \u003cp\u003e2.3.1 Forwards and Futures 23 \u003c\/p\u003e \u003cp\u003e2.3.2 Options 24 \u003c\/p\u003e \u003cp\u003e2.4 Data Sets 27 \u003c\/p\u003e \u003cp\u003e2.4.1 Daily S\u0026amp;P 500 Data 27 \u003c\/p\u003e \u003cp\u003e2.4.2 Daily S\u0026amp;P 500 and Nasdaq-100 Data 28 \u003c\/p\u003e \u003cp\u003e2.4.3 Monthly S\u0026amp;P 500, Bond, and Bill Data 28 \u003c\/p\u003e \u003cp\u003e2.4.4 Daily US Treasury 10 Year Bond Data 29 \u003c\/p\u003e \u003cp\u003e2.4.5 Daily S\u0026amp;P 500 Components Data 30 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Univariate Data Analysis 33\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e3.1 Univariate Statistics 34 \u003c\/p\u003e \u003cp\u003e3.1.1 The Center of a Distribution 34 \u003c\/p\u003e \u003cp\u003e3.1.2 The Variance and Moments 37 \u003c\/p\u003e \u003cp\u003e3.1.3 The Quantiles and the Expected Shortfalls 40 \u003c\/p\u003e \u003cp\u003e3.2 Univariate Graphical Tools 42 \u003c\/p\u003e \u003cp\u003e3.2.1 Empirical Distribution Function Based Tools 43 \u003c\/p\u003e \u003cp\u003e3.2.2 Density Estimation Based Tools 53 \u003c\/p\u003e \u003cp\u003e3.3 Univariate Parametric Models 55 \u003c\/p\u003e \u003cp\u003e3.3.1 The Normal and Log-normal Models 55 \u003c\/p\u003e \u003cp\u003e3.3.2 The Student Distributions 59 \u003c\/p\u003e \u003cp\u003e3.4 Tail Modeling 61 \u003c\/p\u003e \u003cp\u003e3.4.1 Modeling and Estimating Excess Distributions 62 \u003c\/p\u003e \u003cp\u003e3.4.2 Parametric Families for Excess Distributions 65 \u003c\/p\u003e \u003cp\u003e3.4.3 Fitting the Models to Return Data 74 \u003c\/p\u003e \u003cp\u003e3.5 Asymptotic Distributions 83 \u003c\/p\u003e \u003cp\u003e3.5.1 The Central Limit Theorems 84 \u003c\/p\u003e \u003cp\u003e3.5.2 The Limit Theorems for Maxima 88 \u003c\/p\u003e \u003cp\u003e3.6 Univariate Stylized Facts 91 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multivariate Data Analysis 95\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e4.1 Measures of Dependence 95 \u003c\/p\u003e \u003cp\u003e4.1.1 Correlation Coefficients 97 \u003c\/p\u003e \u003cp\u003e4.1.2 Coefficients of Tail Dependence 101 \u003c\/p\u003e \u003cp\u003e4.2 Multivariate Graphical Tools 103 \u003c\/p\u003e \u003cp\u003e4.2.1 Scatter Plots 103 \u003c\/p\u003e \u003cp\u003e4.2.2 Correlation Matrix: Multidimensional Scaling 104 \u003c\/p\u003e \u003cp\u003e4.3 Multivariate Parametric Models 107 \u003c\/p\u003e \u003cp\u003e4.3.1 Multivariate Gaussian Distributions 107 \u003c\/p\u003e \u003cp\u003e4.3.2 Multivariate Student Distributions 107 \u003c\/p\u003e \u003cp\u003e4.3.3 Normal Variance Mixture Distributions 108 \u003c\/p\u003e \u003cp\u003e4.3.4 Elliptical Distributions 110 \u003c\/p\u003e \u003cp\u003e4.4 Copulas 111 \u003c\/p\u003e \u003cp\u003e4.4.1 Standard Copulas 111 \u003c\/p\u003e \u003cp\u003e4.4.2 Nonstandard Copulas 112 \u003c\/p\u003e \u003cp\u003e4.4.3 Sampling from a Copula 113 \u003c\/p\u003e \u003cp\u003e4.4.4 Examples of Copulas 116 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Time Series Analysis 121\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e5.1 Stationarity and Autocorrelation 122 \u003c\/p\u003e \u003cp\u003e5.1.1 Strict Stationarity 122 \u003c\/p\u003e \u003cp\u003e5.1.2 Covariance Stationarity and Autocorrelation 126 \u003c\/p\u003e \u003cp\u003e5.2 Model Free Estimation 128 \u003c\/p\u003e \u003cp\u003e5.2.1 Descriptive Statistics for Time Series 129 \u003c\/p\u003e \u003cp\u003e5.2.2 Markov Models 129 \u003c\/p\u003e \u003cp\u003e5.2.3 Time Varying Parameter 130 \u003c\/p\u003e \u003cp\u003e5.3 Univariate Time Series Models 135 \u003c\/p\u003e \u003cp\u003e5.3.1 Prediction and Conditional Expectation 135 \u003c\/p\u003e \u003cp\u003e5.3.2 ARMA Processes 136 \u003c\/p\u003e \u003cp\u003e5.3.3 Conditional Heteroskedasticity Models 143 \u003c\/p\u003e \u003cp\u003e5.3.4 Continuous Time Processes 154 \u003c\/p\u003e \u003cp\u003e5.4 Multivariate Time Series Models 157 \u003c\/p\u003e \u003cp\u003e5.4.1 MGARCH Models 157 \u003c\/p\u003e \u003cp\u003e5.4.2 Covariance in MGARCH Models 159  \u003c\/p\u003e \u003cp\u003e5.5 Time Series Stylized Facts 160 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Prediction 163\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e6.1 Methods of Prediction 164 \u003c\/p\u003e \u003cp\u003e6.1.1 Moving Average Predictors 164 \u003c\/p\u003e \u003cp\u003e6.1.2 State Space Predictors 166 \u003c\/p\u003e \u003cp\u003e6.2 Forecast Evaluation 170 \u003c\/p\u003e \u003cp\u003e6.2.1 The Sum of Squared Prediction Errors 170 \u003c\/p\u003e \u003cp\u003e6.2.2 Testing the Prediction Accuracy 172 \u003c\/p\u003e \u003cp\u003e6.3 Predictive Variables 175 \u003c\/p\u003e \u003cp\u003e6.3.1 Risk Indicators 175 \u003c\/p\u003e \u003cp\u003e6.3.2 Interest Rate Variables 177 \u003c\/p\u003e \u003cp\u003e6.3.3 Stock Market Indicators 178 \u003c\/p\u003e \u003cp\u003e6.3.4 Sentiment Indicators 180 \u003c\/p\u003e \u003cp\u003e6.3.5 Technical Indicators 180 \u003c\/p\u003e \u003cp\u003e6.4 Asset Return Prediction 182 \u003c\/p\u003e \u003cp\u003e6.4.1 Prediction of S\u0026amp;P 500 Returns 184 \u003c\/p\u003e \u003cp\u003e6.4.2 Prediction of 10-Year Bond Returns 187 \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Risk Management 193\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Volatility Prediction 195\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e7.1 Applications of Volatility Prediction 197 \u003c\/p\u003e \u003cp\u003e7.1.1 Variance and Volatility Trading 197 \u003c\/p\u003e \u003cp\u003e7.1.2 Covariance Trading 197 \u003c\/p\u003e \u003cp\u003e7.1.3 Quantile Estimation 198 \u003c\/p\u003e \u003cp\u003e7.1.4 Portfolio Selection 199 \u003c\/p\u003e \u003cp\u003e7.1.5 Option Pricing 199 \u003c\/p\u003e \u003cp\u003e7.2 Performance Measures for Volatility Predictors 199 \u003c\/p\u003e \u003cp\u003e7.3 Conditional Heteroskedasticity Models 200 \u003c\/p\u003e \u003cp\u003e7.3.1 GARCH Predictor 200 \u003c\/p\u003e \u003cp\u003e7.3.2 ARCH Predictor 203 \u003c\/p\u003e \u003cp\u003e7.4 Moving Average Methods 205 \u003c\/p\u003e \u003cp\u003e7.4.1 Sequential Sample Variance 205 \u003c\/p\u003e \u003cp\u003e7.4.2 Exponentially Weighted Moving Average 207 \u003c\/p\u003e \u003cp\u003e7.5 State Space Predictors 211 \u003c\/p\u003e \u003cp\u003e7.5.1 Linear Regression Predictor 212 \u003c\/p\u003e \u003cp\u003e7.5.2 Kernel Regression Predictor 214 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Quantiles and Value-at-Risk 219\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e8.1 Definitions of Quantiles 220 \u003c\/p\u003e \u003cp\u003e8.2 Applications of Quantiles 223 \u003c\/p\u003e \u003cp\u003e8.2.1 Reserve Capital 223 \u003c\/p\u003e \u003cp\u003e8.2.2 Margin Requirements 225 \u003c\/p\u003e \u003cp\u003e8.2.3 Quantiles as a Risk Measure 226 \u003c\/p\u003e \u003cp\u003e8.3 Performance Measures for Quantile Estimators 227 \u003c\/p\u003e \u003cp\u003e8.3.1 Measuring the Probability of Exceedances 228 \u003c\/p\u003e \u003cp\u003e8.3.2 A Loss Function for Quantile Estimation 231 \u003c\/p\u003e \u003cp\u003e8.4 Nonparametric Estimators of Quantiles 233 \u003c\/p\u003e \u003cp\u003e8.4.1 Empirical Quantiles 234 \u003c\/p\u003e \u003cp\u003e8.4.2 Conditional Empirical Quantiles 238 \u003c\/p\u003e \u003cp\u003e8.5 Volatility Based Quantile Estimation 240 \u003c\/p\u003e \u003cp\u003e8.5.1 Location–Scale Model 240 \u003c\/p\u003e \u003cp\u003e8.5.2 Conditional Location–Scale Model 245 \u003c\/p\u003e \u003cp\u003e8.6 Excess Distributions in Quantile Estimation 258 \u003c\/p\u003e \u003cp\u003e8.6.1 The Excess Distributions 259 \u003c\/p\u003e \u003cp\u003e8.6.2 Unconditional Quantile Estimation 261 \u003c\/p\u003e \u003cp\u003e8.6.3 Conditional Quantile Estimators 269 \u003c\/p\u003e \u003cp\u003e8.7 Extreme Value Theory in Quantile Estimation 288 \u003c\/p\u003e \u003cp\u003e8.7.1 The Block Maxima Method 288 \u003c\/p\u003e \u003cp\u003e8.7.2 Threshold Exceedances 289 \u003c\/p\u003e \u003cp\u003e8.8 Expected Shortfall 292 \u003c\/p\u003e \u003cp\u003e8.8.1 Performance of Estimators of the Expected Shortfall 292 \u003c\/p\u003e \u003cp\u003e8.8.2 Estimation of the Expected Shortfall 293 \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Portfolio Management 297\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Some Basic Concepts of Portfolio Theory 299\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e9.1 Portfolios and Their Returns 300 \u003c\/p\u003e \u003cp\u003e9.1.1 Trading Strategies 300 \u003c\/p\u003e \u003cp\u003e9.1.2 The Wealth and Return in the One- Period Model 301 \u003c\/p\u003e \u003cp\u003e9.1.3 The Wealth Process in the Multiperiod Model 304 \u003c\/p\u003e \u003cp\u003e9.1.4 Examples of Portfolios 306 \u003c\/p\u003e \u003cp\u003e9.2 Comparison of Return and Wealth Distributions 312 \u003c\/p\u003e \u003cp\u003e9.2.1 Mean–Variance Preferences 313 \u003c\/p\u003e \u003cp\u003e9.2.2 Expected Utility 316 \u003c\/p\u003e \u003cp\u003e9.2.3 Stochastic Dominance 325 \u003c\/p\u003e \u003cp\u003e9.3 Multiperiod Portfolio Selection 326 \u003c\/p\u003e \u003cp\u003e9.3.1 One-Period Optimization 328 \u003c\/p\u003e \u003cp\u003e9.3.2 The Multiperiod Optimization 329 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Performance Measurement 337\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e10.1 The Sharpe Ratio 338 \u003c\/p\u003e \u003cp\u003e10.1.1 Definition of the Sharpe Ratio 338 \u003c\/p\u003e \u003cp\u003e10.1.2 Confidence Intervals for the Sharpe Ratio 340 \u003c\/p\u003e \u003cp\u003e10.1.3 Testing the Sharpe Ratio 343 \u003c\/p\u003e \u003cp\u003e10.1.4 Other Measures of Risk-Adjusted Return 345 \u003c\/p\u003e \u003cp\u003e10.2 Certainty Equivalent 346 \u003c\/p\u003e \u003cp\u003e10.3 Drawdown 347 \u003c\/p\u003e \u003cp\u003e10.4 Alpha and Conditional Alpha 348 \u003c\/p\u003e \u003cp\u003e10.4.1 Alpha 349 \u003c\/p\u003e \u003cp\u003e10.4.2 Conditional Alpha 355 \u003c\/p\u003e \u003cp\u003e10.5 Graphical Tools of Performance Measurement 356 \u003c\/p\u003e \u003cp\u003e10.5.1 Using Wealth in Evaluation 356 \u003c\/p\u003e \u003cp\u003e10.5.2 Using the Sharpe Ratio in Evaluation 359 \u003c\/p\u003e \u003cp\u003e10.5.3 Using the Certainty Equivalent in Evaluation 364 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Markowitz Portfolios 367\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e11.1 Variance Penalized Expected Return 369 \u003c\/p\u003e \u003cp\u003e11.1.1 Variance Penalization with the Risk-Free Rate 369 \u003c\/p\u003e \u003cp\u003e11.1.2 Variance Penalization without the Risk-Free Rate 371 \u003c\/p\u003e \u003cp\u003e11.2 Minimizing Variance under a Sufficient Expected Return 372 \u003c\/p\u003e \u003cp\u003e11.2.1 Minimizing Variance with the Risk-Free Rate 372 \u003c\/p\u003e \u003cp\u003e11.2.2 Minimizing Variance without the Risk-Free Rate 374 \u003c\/p\u003e \u003cp\u003e11.3 Markowitz Bullets 375 \u003c\/p\u003e \u003cp\u003e11.4 Further Topics in Markowitz Portfolio Selection 380 \u003c\/p\u003e \u003cp\u003e11.4.1 Estimation 380 \u003c\/p\u003e \u003cp\u003e11.4.2 Penalizing Techniques 381 \u003c\/p\u003e \u003cp\u003e11.4.3 Principal Components Analysis 382 \u003c\/p\u003e \u003cp\u003e11.5 Examples of Markowitz Portfolio Selection 383 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Dynamic Portfolio Selection 385\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e12.1 Prediction in Dynamic Portfolio Selection 387 \u003c\/p\u003e \u003cp\u003e12.1.1 Expected Returns in Dynamic Portfolio Selection 387 \u003c\/p\u003e \u003cp\u003e12.1.2 Markowitz Criterion in Dynamic Portfolio Selection 390 \u003c\/p\u003e \u003cp\u003e12.1.3 Expected Utility in Dynamic Portfolio Selection 391 \u003c\/p\u003e \u003cp\u003e12.2 Backtesting Trading Strategies 393 \u003c\/p\u003e \u003cp\u003e12.3 One Risky Asset 394 \u003c\/p\u003e \u003cp\u003e12.3.1 Using Expected Returns with One Risky Asset 394 \u003c\/p\u003e \u003cp\u003e12.3.2 Markowitz Portfolios with One Risky Asset 401 \u003c\/p\u003e \u003cp\u003e12.4 Two Risky Assets 405 \u003c\/p\u003e \u003cp\u003e12.4.1 Using Expected Returns with Two Risky Assets 405 \u003c\/p\u003e \u003cp\u003e12.4.2 Markowitz Portfolios with Two Risky Assets 409 \u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Pricing of Securities 419\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Principles of Asset Pricing 421\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e13.1 Introduction to Asset Pricing 422 \u003c\/p\u003e \u003cp\u003e13.1.1 Absolute Pricing 423 \u003c\/p\u003e \u003cp\u003e13.1.2 Relative Pricing Using Arbitrage 424 \u003c\/p\u003e \u003cp\u003e13.1.3 Relative Pricing Using Statistical Arbitrage 428 \u003c\/p\u003e \u003cp\u003e13.2 Fundamental Theorems of Asset Pricing 430 \u003c\/p\u003e \u003cp\u003e13.2.1 Discrete Time Markets 431 \u003c\/p\u003e \u003cp\u003e13.2.2 Wealth and Value Processes 432 \u003c\/p\u003e \u003cp\u003e13.2.3 Arbitrage and Martingale Measures 436 \u003c\/p\u003e \u003cp\u003e13.2.4 European Contingent Claims 448 \u003c\/p\u003e \u003cp\u003e13.2.5 Completeness 451 \u003c\/p\u003e \u003cp\u003e13.2.6 American Contingent Claims 454 \u003c\/p\u003e \u003cp\u003e13.3 Evaluation of Pricing and Hedging Methods 456 \u003c\/p\u003e \u003cp\u003e13.3.1 The Wealth of the Seller 456 \u003c\/p\u003e \u003cp\u003e13.3.2 The Wealth of the Buyer 458 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Pricing by Arbitrage 459\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e14.1 Futures and the Put–Call Parity 460 \u003c\/p\u003e \u003cp\u003e14.1.1 Futures 460 \u003c\/p\u003e \u003cp\u003e14.1.2 The Put–Call Parity 464 \u003c\/p\u003e \u003cp\u003e14.1.3 American Call Options 465 \u003c\/p\u003e \u003cp\u003e14.2 Pricing in Binary Models 466 \u003c\/p\u003e \u003cp\u003e14.2.1 The One-Period Binary Model 467 \u003c\/p\u003e \u003cp\u003e14.2.2 The Multiperiod Binary Model 470 \u003c\/p\u003e \u003cp\u003e14.2.3 Asymptotics of the Multiperiod Binary Model 475 \u003c\/p\u003e \u003cp\u003e14.2.4 American Put Options 484 \u003c\/p\u003e \u003cp\u003e14.3 Black–Scholes Pricing 485 \u003c\/p\u003e \u003cp\u003e14.3.1 Call and Put Prices 485 \u003c\/p\u003e \u003cp\u003e14.3.2 Implied Volatilities 495 \u003c\/p\u003e \u003cp\u003e14.3.3 Derivations of the Black–Scholes Prices 498 \u003c\/p\u003e \u003cp\u003e14.3.4 Examples of Pricing Using the Black–Scholes Model 501 \u003c\/p\u003e \u003cp\u003e14.4 Black–Scholes Hedging 505 \u003c\/p\u003e \u003cp\u003e14.4.1 Hedging Errors: Nonsequential Volatility Estimation 506 \u003c\/p\u003e \u003cp\u003e14.4.2 Hedging Frequency 508 \u003c\/p\u003e \u003cp\u003e14.4.3 Hedging and Strike Price 511 \u003c\/p\u003e \u003cp\u003e14.4.4 Hedging and Expected Return 512 \u003c\/p\u003e \u003cp\u003e14.4.5 Hedging and Volatility 514 \u003c\/p\u003e \u003cp\u003e14.5 Black–Scholes Hedging and Volatility Estimation 515 \u003c\/p\u003e \u003cp\u003e14.5.1 Hedging Errors: Sequential Volatility Estimation 515 \u003c\/p\u003e \u003cp\u003e14.5.2 Distribution of Hedging Errors 517 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Pricing in Incomplete Models 521\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e15.1 Quadratic Hedging and Pricing 522 \u003c\/p\u003e \u003cp\u003e15.2 Utility Maximization 523 \u003c\/p\u003e \u003cp\u003e15.2.1 The Exponential Utility 524 \u003c\/p\u003e \u003cp\u003e15.2.2 Other Utility Functions 525 \u003c\/p\u003e \u003cp\u003e15.2.3 Relative Entropy 526 \u003c\/p\u003e \u003cp\u003e15.2.4 Examples of Esscher Prices 527 \u003c\/p\u003e \u003cp\u003e15.2.5 Marginal Rate of Substitution 529 \u003c\/p\u003e \u003cp\u003e15.3 Absolutely Continuous Changes of Measures 530 \u003c\/p\u003e \u003cp\u003e15.3.1 Conditionally Gaussian Returns 530 \u003c\/p\u003e \u003cp\u003e15.3.2 Conditionally Gaussian Logarithmic Returns 532 \u003c\/p\u003e \u003cp\u003e15.4 GARCH Market Models 534 \u003c\/p\u003e \u003cp\u003e15.4.1 Heston–Nandi Method 535 \u003c\/p\u003e \u003cp\u003e15.4.2 The Monte Carlo Method 539 \u003c\/p\u003e \u003cp\u003e15.4.3 Comparison of Risk-Neutral Densities 541 \u003c\/p\u003e \u003cp\u003e15.5 Nonparametric Pricing Using Historical Simulation 545 \u003c\/p\u003e \u003cp\u003e15.5.1 Prices 545 \u003c\/p\u003e \u003cp\u003e15.5.2 Hedging Coefficients 548 \u003c\/p\u003e \u003cp\u003e15.6 Estimation of the Risk-Neutral Density 551 \u003c\/p\u003e \u003cp\u003e15.6.1 Deducing the Risk-Neutral Density from Market Prices 552 \u003c\/p\u003e \u003cp\u003e15.6.2 Examples of Estimation of the Risk-Neutral Density 552 \u003c\/p\u003e \u003cp\u003e15.7 Quantile Hedging 554 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Quadratic and Local Quadratic Hedging 557\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e16.1 Quadratic Hedging 558 \u003c\/p\u003e \u003cp\u003e16.1.1 Definitions and Assumptions 559 \u003c\/p\u003e \u003cp\u003e16.1.2 The One Period Model 562 \u003c\/p\u003e \u003cp\u003e16.1.3 The Two Period Model 569 \u003c\/p\u003e \u003cp\u003e16.1.4 The Multiperiod Model 575 \u003c\/p\u003e \u003cp\u003e16.2 Local Quadratic Hedging 583 \u003c\/p\u003e \u003cp\u003e16.2.1 The Two Period Model 583 \u003c\/p\u003e \u003cp\u003e16.2.2 The Multiperiod Model 587 \u003c\/p\u003e \u003cp\u003e16.2.3 Local Quadratic Hedging without Self-Financing 593 \u003c\/p\u003e \u003cp\u003e16.3 Implementations of Local Quadratic Hedging 595 \u003c\/p\u003e \u003cp\u003e16.3.1 Historical Simulation 596 \u003c\/p\u003e \u003cp\u003e16.3.2 Local Quadratic Hedging Under Independence 599 \u003c\/p\u003e \u003cp\u003e16.3.3 Local Quadratic Hedging under Dependence 604 \u003c\/p\u003e \u003cp\u003e16.3.4 Evaluation of Quadratic Hedging 610 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Option Strategies 615\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e17.1 Option Strategies 616 \u003c\/p\u003e \u003cp\u003e17.1.1 Calls, Puts, and Vertical Spreads 616 \u003c\/p\u003e \u003cp\u003e17.1.2 Strangles, Straddles, Butterflies, and Condors 619 \u003c\/p\u003e \u003cp\u003e17.1.3 Calendar Spreads 621 \u003c\/p\u003e \u003cp\u003e17.1.4 Combining Options with Stocks and Bonds 623 \u003c\/p\u003e \u003cp\u003e17.2 Profitability of Option Strategies 625 \u003c\/p\u003e \u003cp\u003e17.2.1 Return Functions of Option Strategies 626 \u003c\/p\u003e \u003cp\u003e17.2.2 Return Distributions of Option Strategies 634 \u003c\/p\u003e \u003cp\u003e17.2.3 Performance Measurement of Option Strategies 644 \u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Interest Rate Derivatives 649\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003e18.1 Basic Concepts of Interest Rate Derivatives 650 \u003c\/p\u003e \u003cp\u003e18.1.1 Interest Rates and a Bank Account 651 \u003c\/p\u003e \u003cp\u003e18.1.2 Zero-Coupon Bonds 653 \u003c\/p\u003e \u003cp\u003e18.1.3 Coupon-Bearing Bonds 656 \u003c\/p\u003e \u003cp\u003e18.2 InterestRateForwards 659 \u003c\/p\u003e \u003cp\u003e18.2.1 Forward Zero-Coupon Bonds 659 \u003c\/p\u003e \u003cp\u003e18.2.2 Forward Rate Agreements 661 \u003c\/p\u003e \u003cp\u003e18.2.3 Swaps 663 \u003c\/p\u003e \u003cp\u003e18.2.4 Related Fixed Income Instruments 665 \u003c\/p\u003e \u003cp\u003e18.3 Interest Rate Options 666 \u003c\/p\u003e \u003cp\u003e18.3.1 Caplets and Floorlets 666 \u003c\/p\u003e \u003cp\u003e18.3.2 Caps and Floors 668 \u003c\/p\u003e \u003cp\u003e18.3.3 Swaptions 668 \u003c\/p\u003e \u003cp\u003e18.4 Modeling Interest Rate Markets 669 \u003c\/p\u003e \u003cp\u003e18.4.1 HJM Model 670 \u003c\/p\u003e \u003cp\u003e18.4.2 Short-Rate Models 671 \u003c\/p\u003e \u003cp\u003eReferences 673 \u003c\/p\u003e \u003cp\u003eIndex 681\u003c\/p\u003e   \u003cp\u003e \u003cstrong\u003eJussi Klemelä, PhD,\u003c\/strong\u003e 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 \u003cem\u003eSmoothing of Multivariate Data: Density Estimation and Visualization\u003c\/em\u003e and \u003cem\u003eMultivariate Nonparametric Regression and Visualization: With R and Applications to Finance.\u003c\/em\u003e      \u003c\/p\u003e\u003cp\u003e \u003cstrong\u003eAn Introduction to Machine Learning in Finance, with Mathematical Background, Data Visualization, and R\u003c\/strong\u003e   \u003c\/p\u003e\u003cp\u003e Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. \u003cem\u003eNonparametric Finance\u003c\/em\u003e 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.   \u003c\/p\u003e\u003cp\u003e 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.  \t \u003c\/p\u003e\u003cp\u003eWritten for the leading edge of finance, \u003cem\u003eNonparametric Finance:\u003c\/em\u003e  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eIntroduces basic statistical finance concepts, including univariate and muvltivariate data analysis, time series analysis, and prediction\u003c\/li\u003e \u003cli\u003eProvides risk management guidance through volatility prediction, quantiles, and value-at-risk\u003c\/li\u003e \u003cli\u003eExamines portfolio theory, performance measurement, Markowitz portfolios, dynamic portfolio selection, and more\u003c\/li\u003e \u003cli\u003eDiscusses fundamental theorems of asset pricing, Black-Scholes pricing and hedging, quadratic pricing and hedging, option portfolios, interest rate derivatives, and other asset pricing principles\u003c\/li\u003e \u003cli\u003eProvides supplementary R code and numerous graphics to reinforce complex content\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e  \u003cp\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989696987365,"sku":"NP9781119409106","price":144.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119409106.jpg?v=1761785144","url":"https:\/\/k12savings.com\/es\/products\/nonparametric-finance-isbn-9781119409106","provider":"K12savings","version":"1.0","type":"link"}