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 Klemela, 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.
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