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Applied probabilistic calculus for financial engineering : an introduction using R
Illustrates how R may be used successfully to solve problems in quantitative finance
Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R provides R recipes for asset allocation and portfolio optimization problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. It examines probabilistic calculus for modeling financial engineering—walking the reader through building an effective financial model from the Geometric Brownian Motion (GBM) Model via probabilistic calculus, while also covering Ito Calculus. Classical mathematical models in financial engineering and modern portfolio theory are discussed—along with the Two Mutual Fund Theorem and The Sharpe Ratio. The book also looks at R as a calculator and using R in data analysis in financial engineering. Additionally, it covers asset allocation using R, financial risk modeling and portfolio optimization using R, global and local optimal values, locating functional maxima and minima, and portfolio optimization by performance analytics in CRAN.
* Covers optimization methodologies in probabilistic calculus for financial engineering
* Answers the question: What does a \"Random Walk\" Financial Theory look like?
* Covers the GBM Model and the Random Walk Model
* Examines modern theories of portfolio optimization, including The Markowitz Model of Modern Portfolio Theory (MPT), The Black-Litterman Model, and The Black-Scholes Option Pricing Model
Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R s an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.
eBook
Applied probabilistic calculus for assets allocation and portfolio optimization in financial engineering using R
Illustrates how R may be used successfully to solve problems in quantitative finance Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R provides R recipes for asset allocation and portfolio optimization problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. It examines probabilistic calculus for modeling financial engineering-walking the reader through building an effective financial model from GBM via probabilistic calculus, while also covering Ito Calculus. Classical mathematical models in financial engineering and modern portfolio theory are discussed-along with the Two Mutual Fund Theorem and The Sharpe Ratio. The book also looks at R as a calculator and using R in data analysis in financial engineering. Additionally, it covers asset allocation using R, financial risk modeling and portfolio optimization using R, global and local optimal values, locating functional maxima and minima, and portfolio optimization by performance analytics in CRAN. Covers optimization methodologies in probabilistic calculus for financial engineering Answers the question: What does a \"Random Walk\" Financial Theory look like? Covers The Geometric Brownian Motion (GBM) Model and the Random Walk Model Examines modern theories of portfolio optimization, including The Markowitz Model of Modern Portfolio Theory (MPT), The Black-Litterman Model, and The Black-Scholes Option Pricing Model Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R is an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.
eBook
New Trends in Financial Engineering
Financial engineering is defined as the application of mathematical methods to the solution of problems in finance. The recent financial crisis raised many challenges for financial engineers: not only were financially engineered products such as collateralized debt obligations and credit default swaps implicated in causing the crisis, but the risk management techniques developed by financial engineers appeared to fail when they were most desperately needed. This book is the first in a series describing research by a multidisciplinary team of economists, mathematicians and control theorists exploring new research directions in financial engineering. It is broadly divided into three parts. The first part of the book reviews recent developments of real options; an application of the theory of financial options to capital investments with the emphasis on flexibility. Topics covered include the technique of variational inequalities, the use of forward and backward stochastic differential equations and the application of a real option approach to a consumption and portfolio selection problem. The second part of the book presents new topics, including simultaneous control of dividend payments and risk management, risk measures and non-linear probability models and a survey of recent studies on market microstructure. The last part of the volume proposes a new perspective. The availability and success of mathematical tools has attracted many talented people to the financial services industry. This examination of the way in which they are approaching current and future challenges will be of interest to all those working in the field of financial engineering.
eBook
Financial Engineering and Computation
Students and professionals intending to work in any area of finance must master not only advanced concepts and mathematical models but also learn how to implement these models computationally. This comprehensive text, first published in 2002, combines the theory and mathematics behind financial engineering with an emphasis on computation, in keeping with the way financial engineering is practised in capital markets. Unlike most books on investments, financial engineering, or derivative securities, the book starts from very basic ideas in finance and gradually builds up the theory. It offers a thorough grounding in the subject for MBAs in finance, students of engineering and sciences who are pursuing a career in finance, researchers in computational finance, system analysts, and financial engineers. Along with the theory, the author presents numerous algorithms for pricing, risk management, and portfolio management. The emphasis is on pricing financial and derivative securities: bonds, options, futures, forwards, interest rate derivatives, mortgage-backed securities, bonds with embedded options, and more.
eBook
Applied probabilistic calculus for financial engineering
Intro -- Title Page -- Copyright -- Dedication -- Preface -- About the Companion Website -- Chapter 1: Introduction to Financial Engineering -- 1.1 What Is Financial Engineering? -- 1.2 The Meaning of the Title of This Book -- 1.3 The Continuing Challenge in Financial Engineering -- 1.4 \"Financial Engineering 101\": Modern Portfolio Theory -- 1.5 Asset Class Assumptions Modeling -- 1.6 Some Typical Examples of Proprietary Investment Funds -- 1.7 The Dow Jones Industrial Average (DJIA) and Inflation -- 1.8 Some Less Commendable Stock Investment Approaches -- 1.9 Developing Tools for Financial Engineering Analysis -- Review Questions -- Chapter 2: Probabilistic Calculus for Modeling Financial Engineering -- 2.1 Introduction to Financial Engineering -- 2.2 Mathematical Modeling in Financial Engineering -- 2.3 Building an Effective Financial Model from GBM via Probabilistic Calculus -- 2.4 A Continuous Financial Model Using Probabilistic Calculus: Stochastic Calculus, Ito Calculus -- 2.5 A Numerical Study of the Geometric Brownian Motion (GBM) Model and the Random Walk Model Using R -- Review Questions and Exercises -- Chapter 3: Classical Mathematical Models in Financial Engineering and Modern Portfolio Theory -- 3.1 An Introduction to the Cost of Money in the Financial Market -- 3.2 Modern Theories of Portfolio Optimization -- 3.3 The Black-Litterman Model -- 3.4 The Black-Scholes Option Pricing Model -- 3.5 The Black-Litterman Model -- 3.6 The Black-Litterman Model -- 3.7 The Black-Scholes Option Pricing Model -- 3.8 Some Worked Examples -- Review Questions and Exercises -- Solutions to Exercise 3: The Black-Scholes Equation -- Chapter 4: Data Analysis Using R Programming -- 4.1 Data and Data Processing -- Review Questions for Section 4.1 -- 4.2 Beginning R -- Review Questions for Section 4.2 -- 4.3 R as a Calculator.
Publication
Zero lower bound term structure modeling : a practitioner's guide
Nominal yields on government debt in several countries have fallen very near their zero lower bound (ZLB), causing a liquidity trap and limiting the capacity to stimulate economic growth. This book provides a comprehensive reference to ZLB structure modeling in an applied setting.
eBook
Agricultural Water Allocation by Integration of Hydro-Economic Modeling with Bayesian Networks and Random Forest Approaches
Sustainable utilization of water resources requires preventive measures that must be taken to promote optimal use of water resources together with consideration of stakeholder interests and the economic value of water. The main objective of this study is to present an integrated hydro-economic model for allocating agricultural water based on its economic value. The study region covered six irrigation networks downstream of the Zayandeh Rood Dam in Iran. In fact, this study addresses questions of how to allocate scarce water to different consumers, in order to achieve the highest efficiency and economic benefits. To gain this goal, the existing agricultural activities in each irrigation network were simulated by applying the Positive Mathematical Programming (PMP) economic model and then by coupling the economic model with a water allocation planning model of the basin (MODSIM), the hydro-economic framework was generated. These tools helped to allocate water based on its economic value, maximize net profit by determining the optimal cultivating area and analyze the effects of various allocation scenarios on employment, economic productivity, and reliability indicators. Moreover, Bayesian Networks and Random Forest algorithms were developed as an automated substitute to simplify the process and reduce computational complexity. The results showed that the Nekoabad Network enjoys top priority followed by the Barkhar, Mahyar, Sonati, Abshar, and Rodasht Networks. After implementing the Bayesian Network, the four criteria of MAE, MAPE, R2, and the Nash-Sutcliffe index for the irrigation networks were 9 MCM, 24%, 0.738, and 0.644 respectively, which indicated the model has good accuracy. Random Forest method was also employed as a novel technique in automated allocation, and the values obtained for the four mentioned criteria were 7 MCM, 15%, 0.861, and 0.80, which showed it is more accurate. The results indicated the capability of the presented hydro-economic model as well as the intelligent models substituting it in allocating agricultural water.
Journal Article