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Portfolio Optimization with R/Rmetrics

Portfolio Optimization with R/Rmetrics PDF Author:
Publisher: Rmetrics
ISBN:
Category :
Languages : en
Pages :

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Book Description

Portfolio Optimization with R/Rmetrics

Portfolio Optimization with R/Rmetrics PDF Author:
Publisher: Rmetrics
ISBN:
Category :
Languages : en
Pages :

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Book Description


Financial Risk Modelling and Portfolio Optimization with R

Financial Risk Modelling and Portfolio Optimization with R PDF Author: Bernhard Pfaff
Publisher: John Wiley & Sons
ISBN: 111847712X
Category : Mathematics
Languages : en
Pages : 376

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Book Description
Introduces the latest techniques advocated for measuringfinancial market risk and portfolio optimization, and provides aplethora of R code examples that enable the reader to replicate theresults featured throughout the book. Financial Risk Modelling and Portfolio Optimization withR: Demonstrates techniques in modelling financial risks andapplying portfolio optimization techniques as well as recentadvances in the field. Introduces stylized facts, loss function and risk measures,conditional and unconditional modelling of risk; extreme valuetheory, generalized hyperbolic distribution, volatility modellingand concepts for capturing dependencies. Explores portfolio risk concepts and optimization with riskconstraints. Enables the reader to replicate the results in the book using Rcode. Is accompanied by a supporting website featuring examples andcase studies in R. Graduate and postgraduate students in finance, economics, riskmanagement as well as practitioners in finance and portfoliooptimization will find this book beneficial. It also serves well asan accompanying text in computer-lab classes and is thereforesuitable for self-study.

The Use of Risk Budgets in Portfolio Optimization

The Use of Risk Budgets in Portfolio Optimization PDF Author: Albina Unger
Publisher: Springer
ISBN: 3658072598
Category : Business & Economics
Languages : en
Pages : 424

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Book Description
Risk budgeting models set risk diversification as objective in portfolio allocation and are mainly promoted from the asset management industry. Albina Unger examines the portfolios based on different risk measures in several aspects from the academic perspective (Utility, Performance, Risk, Different Market Phases, Robustness, and Factor Exposures) to investigate the use of these models for asset allocation. Beside the risk budgeting models, alternatives of risk-based investment styles are also presented and examined. The results show that equalizing the risk across the assets does not prevent losses, especially in crisis periods and the performance can mainly be explained by exposures to known asset pricing factors. Thus, the advantages of these approaches compared to known minimum risk portfolios are doubtful.

Portfolio Construction and Analytics

Portfolio Construction and Analytics PDF Author: Frank J. Fabozzi
Publisher: John Wiley & Sons
ISBN: 1118445597
Category : Business & Economics
Languages : en
Pages : 624

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Book Description
A detailed, multi-disciplinary approach to investment analytics Portfolio Construction and Analytics provides an up-to-date understanding of the analytic investment process for students and professionals alike. With complete and detailed coverage of portfolio analytics and modeling methods, this book is unique in its multi-disciplinary approach. Investment analytics involves the input of a variety of areas, and this guide provides the perspective of data management, modeling, software resources, and investment strategy to give you a truly comprehensive understanding of how today's firms approach the process. Real-world examples provide insight into analytics performed with vendor software, and references to analytics performed with open source software will prove useful to both students and practitioners. Portfolio analytics refers to all of the methods used to screen, model, track, and evaluate investments. Big data, regulatory change, and increasing risk is forcing a need for a more coherent approach to all aspects of investment analytics, and this book provides the strong foundation and critical skills you need. Master the fundamental modeling concepts and widely used analytics Learn the latest trends in risk metrics, modeling, and investment strategies Get up to speed on the vendor and open-source software most commonly used Gain a multi-angle perspective on portfolio analytics at today's firms Identifying investment opportunities, keeping portfolios aligned with investment objectives, and monitoring risk and performance are all major functions of an investment firm that relies heavily on analytics output. This reliance will only increase in the face of market changes and increased regulatory pressure, and practitioners need a deep understanding of the latest methods and models used to build a robust investment strategy. Portfolio Construction and Analytics is an invaluable resource for portfolio management in any capacity.

Introduction to R for Quantitative Finance

Introduction to R for Quantitative Finance PDF Author: Gergely Daróczi
Publisher: Packt Publishing Ltd
ISBN: 1783280948
Category : Computers
Languages : en
Pages : 164

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Book Description
This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

Hands-on Matrix Algebra Using R

Hands-on Matrix Algebra Using R PDF Author: Hrishikesh D. Vinod
Publisher: World Scientific
ISBN: 9814313688
Category : Mathematics
Languages : en
Pages : 329

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Book Description
Teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R. This book provides an overview of matrix theory without being bogged down in proofs or tedium.

Biostatistics for Human Genetic Epidemiology

Biostatistics for Human Genetic Epidemiology PDF Author: Bertram K. C. Chan
Publisher: Springer
ISBN: 331993791X
Category : Medical
Languages : en
Pages : 355

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Book Description
The book illustrates how biostatistics may numerically summarize human genetic epidemiology using R, and may be used successfully to solve problems in quantitative Genetic Epidemiology Biostatistics for Human Genetic Epidemiology provides statistical methodologies and R recipes for human genetic epidemiologic problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related human genetic epidemiology, with R codes illustrations for various examples. This clear and concise book covers human genetic epidemiology, using R in data analysis, including multivariate data analysis. It examines probabilistic and statistical theories for modeling human genetic epidemiology – leading the readers through an effective epidemiologic model, from simple to advanced levels. Classical mathematical, probabilistic, and statistical theory are thoroughly discussed and presented. This book also presents R as a calculator and using R in data analysis. Additionally, it covers Advanced Human Genetic Data Concepts, the Study of Human Genetic Variation, Manhattan Plots, as well as the Procedures for Multiple Comparison. Numerous Worked Examples are provided for illustrations of concepts and real-life applications. Biostatistics for Human Genetic Epidemiology is an ideal reference for professionals and students in Medicine (particularly in Preventive Medicine and Public Health Medical Practices), as well as in Genetics, Epidemiology, and Biostatistics.

Applied Probabilistic Calculus for Financial Engineering

Applied Probabilistic Calculus for Financial Engineering PDF Author: Bertram K. C. Chan
Publisher: John Wiley & Sons
ISBN: 1119387612
Category : Business & Economics
Languages : en
Pages : 536

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Book Description
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 is an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.

Computational Finance

Computational Finance PDF Author: Argimiro Arratia
Publisher: Springer Science & Business Media
ISBN: 9462390703
Category : Computers
Languages : en
Pages : 301

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Book Description
The book covers a wide range of topics, yet essential, in Computational Finance (CF), understood as a mix of Finance, Computational Statistics, and Mathematics of Finance. In that regard it is unique in its kind, for it touches upon the basic principles of all three main components of CF, with hands-on examples for programming models in R. Thus, the first chapter gives an introduction to the Principles of Corporate Finance: the markets of stock and options, valuation and economic theory, framed within Computation and Information Theory (e.g. the famous Efficient Market Hypothesis is stated in terms of computational complexity, a new perspective). Chapters 2 and 3 give the necessary tools of Statistics for analyzing financial time series, it also goes in depth into the concepts of correlation, causality and clustering. Chapters 4 and 5 review the most important discrete and continuous models for financial time series. Each model is provided with an example program in R. Chapter 6 covers the essentials of Technical Analysis (TA) and Fundamental Analysis. This chapter is suitable for people outside academics and into the world of financial investments, as a primer in the methods of charting and analysis of value for stocks, as it is done in the financial industry. Moreover, a mathematical foundation to the seemly ad-hoc methods of TA is given, and this is new in a presentation of TA. Chapter 7 reviews the most important heuristics for optimization: simulated annealing, genetic programming, and ant colonies (swarm intelligence) which is material to feed the computer savvy readers. Chapter 8 gives the basic principles of portfolio management, through the mean-variance model, and optimization under different constraints which is a topic of current research in computation, due to its complexity. One important aspect of this chapter is that it teaches how to use the powerful tools for portfolio analysis from the RMetrics R-package. Chapter 9 is a natural continuation of chapter 8 into the new area of research of online portfolio selection. The basic model of the universal portfolio of Cover and approximate methods to compute are also described.

Computational Actuarial Science with R

Computational Actuarial Science with R PDF Author: Arthur Charpentier
Publisher: CRC Press
ISBN: 1466592591
Category : Business & Economics
Languages : en
Pages : 656

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Book Description
A Hands-On Approach to Understanding and Using Actuarial Models Computational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance. Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets).