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Linear Factor Models in Finance, 1st Edition

 
Linear Factor Models in Finance, 1st Edition,John Knight,Stephen Satchell,ISBN9780750660068
 
 
 

Knight   &   Satchell   

Butterworth-Heinemann

9780750660068

9780080455327

304

234 X 156

Concentrates on the most widely used modelling technique: Linear Factor Modelling

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Key Features

* Covers the latest methods in this area.
* Combines actual quantitative finance experience with analytical research rigour
* Written by both quantitative analysts and academics who work in this area

Description

The determination of the values of stocks, bonds, options, futures, and derivatives is done by the scientific process of asset pricing, which has developed dramatically in the last few years due to advances in financial theory and econometrics. This book covers the science of asset pricing by concentrating on the most widely used modelling technique called: Linear Factor Modelling.

Linear Factor Models covers an important area for Quantitative Analysts/Investment Managers who are developing Quantitative Investment Strategies. Linear factor models (LFM) are part of modern investment processes that include asset valuation, portfolio theory and applications, linear factor models and applications, dynamic asset allocation strategies, portfolio performance measurement, risk management, international perspectives, and the use of derivatives.

The book develops the building blocks for one of the most important theories of asset pricing - Linear Factor Modelling. Within this framework, we can include other asset pricing theories such as the Capital Asset Pricing Model (CAPM), arbitrage pricing theory and various pricing formulae for derivatives and option prices.

As a bare minimum, the reader of this book must have a working knowledge of basic calculus, simple optimisation and elementary statistics. In particular, the reader must be comfortable with the algebraic manipulation of means, variances (and covariances) of linear combination(s) of random variables. Some topics may require a greater mathematical sophistication.

Readership

This book is aimed at Quantitative Analysts and Investment Managers in Investment Firms and Banks. In addition the book will also appeal to those following Quantitative Finance; Quantitative Investment Strategy; Financial Engineering; Valuation and Portfolio Management; Finance Theory; and Financial Modeling courses at Masters Level.

John Knight

Affiliations and Expertise

FCIBSE (Haden Young Ltd), UK

View additional works by John Knight

Stephen Satchell

Stephen Satchell is a Fellow of Trinity College, the Reader in Financial Econometrics at the University of Cambridge and Visiting Professor at Birkbeck College, City University Business School and University of Technology, Sydney. He provides consultancy for a range of city institutions in the broad area of quantitative finance. He has published papers in many journals and has a particular interest in risk.

Affiliations and Expertise

Consultant to financial institutions and Reader in Financial Econometrics at Trinity College, Cambridge, Stephen Satchell is Editor-in-Chief of the Journal of Asset Management and Derivatives, Use, Trading, and Regulation. He has edited or authored over 20 books on finance.

View additional works by Stephen Satchell

Linear Factor Models in Finance, 1st Edition

Review of the literature on multifactor asset pricing, M.Pitsillis. Estimating UK factor models using multivariate skew normal distribution, C. Adcock. Misspecification in the Linear Pricing Model, I. Lo. Bayesian estimation of Risk-Premia in an APT context, T. Darsinos and S. Satchell. Sharpe Style Analysis in the MSCI Sector Portfolios, G. Christodoulakis. Implication of the method of portfolio formation on asset pricing tests, I. Lo. The Small Noise Arbitrage Pricing Theory, S.Satchell. Risk Attribution in a Global Country Sector, A. Scowcroft and J. Sefton. Predictability of Fund of Hedge Fund Returns Using Dynaporte, G. Gregoriou and F. Rouah. Estimating a Combined Linear Model, A. Stroyny. Attributing Equity Risk with a Statistical Factor Model, T. Wilding Making Covariance-based Portfolio Risk Models Sensitive to the rate at which markets reflect new information, D. Di Bartolomeo and S. Warrick. Decomposing Factor Exposure for Equity Portfolios, D. Tien et al.
 
 
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