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This book is an introduction to the mathematical analysis of
probability theory and provides some understanding of how
probability is used to model random phenomena of uncertainty,
specifically in the context of finance theory and applications. The
integrated coverage of both basic probability theory and finance
theory makes this book useful reading for advanced undergraduate
students or for first-year postgraduate students in a quantitative
finance course.The book provides easy and quick access to the field
of theoretical finance by linking the study of applied probability
and its applications to finance theory all in one place. The
coverage is carefully selected to include most of the key ideas in
finance in the last 50 years.The book will also serve as a handy
guide for applied mathematicians and probabilists to easily access
the important topics in finance theory and economics. In addition,
it will also be a handy book for financial economists to learn some
of the more mathematical and rigorous techniques so their
understanding of theory is more rigorous. It is a must read for
advanced undergraduate and graduate students who wish to work in
the quantitative finance area.
This book provides a basic grounding in the use of probability to
model random financial phenomena of uncertainty, and is targeted at
an advanced undergraduate and graduate level. It should appeal to
finance students looking for a firm theoretical guide to the deep
end of derivatives and investments. Bankers and finance
professionals in the fields of investments, derivatives, and risk
management should also find the book useful in bringing probability
and finance together. The book contains applications of both
discrete time theory and continuous time mathematics, and is
extensive in scope. Distribution theory, conditional probability,
and conditional expectation are covered comprehensively, and
applications to modeling state space securities under market
equilibrium are made. Martingale is studied, leading to
consideration of equivalent martingale measures, fundamental
theorems of asset pricing, change of numeraire and discounting,
risk-adjusted and forward-neutral measures, minimal and maximal
prices of contingent claims, Markovian models, and the existence of
martingale measures preserving the Markov property. Discrete
stochastic calculus and multiperiod models leading to no-arbitrage
pricing of contingent claims are also to be found in this book, as
well as the theory of Markov Chains and appropriate applications in
credit modeling. Measure-theoretic probability, moments,
characteristic functions, inequalities, and central limit theorems
are examined. The theory of risk aversion and utility, and ideas of
risk premia are considered. Other application topics include
optimal consumption and investment problems and interest rate
theory.
This book is an introduction to financial valuation and financial
data analyses using econometric methods. It is intended for
advanced finance undergraduates and graduates. Most chapters in the
book would contain one or more finance application examples where
finance concepts, and sometimes theory, are taught.This book is a
modest attempt to bring together several important domains in
financial valuation theory, in econometrics modelling, and in the
empirical analyses of financial data. These domains are highly
intertwined and should be properly understood in order to correctly
and effectively harness the power of data and statistical or
econometrics methods for investment and financial
decision-making.The contribution in this book, and at the same
time, its novelty, is in employing materials in basic econometrics,
particularly linear regression analyses, and weaving into it
threads of foundational finance theory, concepts, ideas, and
models. It provides a clear pedagogical approach to allow very
effective learning by a finance student who wants to be well
equipped in both theory and ability to research the data.This is a
handy book for finance professionals doing research to easily
access the key techniques in data analyses using regression
methods. Students learn all 3 skills at once - finance,
econometrics, and data analyses. It provides for very solid and
useful learning for advanced undergraduate and graduate students
who wish to work in financial analyses, risk analyses, and
financial research areas.
This book brings together domains in financial asset pricing and
valuation, financial investment theory, econometrics modeling, and
the empirical analyses of financial data by applying appropriate
econometric techniques. These domains are highly intertwined and
should be properly understood in order to correctly and effectively
harness the power of data and methods for investment and financial
decision-making. The book is targeted at advanced finance
undergraduates and beginner professionals performing financial
forecasts or empirical modeling who will find it refreshing to see
how forecasting is not simply running a least squares regression
line across data points, and that there are many minefields and
pitfalls to avoid, such as spurious results and incorrect
interpretations.
This book will provide a firm foundation in the understanding of
financial economics applied to asset pricing. It carries the real
world perspective of how the market works, including behavioral
biases, and also wraps that understanding in the context of a
rigorous economics framework of investors' risk preferences,
underlying price dynamics, rational choice in the large, and market
equilibrium other than inexplicable irrational bubbles. It
concentrates on analyses of stock, credit, and option pricing.
Existing highly cited finance models in pricing of these assets are
covered in detail, and theory is accompanied by rigorous
applications of econometrics. Econometrics contain elucidations of
both the statistical theory as well as the practice of data
analyses. Linear regression methods and some nonlinear methods are
also covered. The contribution of this book, and at the same time,
its novelty, is in employing materials in probability theory,
economics optimization, econometrics, and data analyses together to
provide a rigorous and sharp intellect for investment and financial
decision-making. Mistakes are often made with far too often
sweeping pragmatism without deeply knowing the underpinnings of how
the market economics works. This book is written at a level that is
both academically rigorous for university courses in investment,
derivatives, risk management, as well as not too mathematically
deep so that finance and banking graduate professionals can have a
real journey into the frontier financial economics thinking and
rigorous data analytical findings.
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