Stochastic Portfolio Theory Vs Modern Portfolio Theory And The Implications For The Capital Asset Pricing Model
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Stochastic Portfolio Theory Vs. Modern Portfolio Theory and the Implications for the Capital Asset Pricing Model
This paper contrasts the perspectives provided by the traditional Modern Portfolio Theory (MPT) analysis, which uses arithmetic returns, and the Stochastic Portfolio Theory (SPT) analysis, which uses continuous returns. The MPT analysis implies that an efficient portfolio's reward is proportional to its risk and that its information ratio is independent of its risk. The SPT analysis implies that an efficient portfolio's reward is not proportional to its risk, first rising with risk and then declining with risk, and that its information ratio declines as its risk increases. The analysis also has implications for the Capital Asset Pricing Model (CAPM). According to the MPT analysis, a stock's expected excess return is equal to its beta times the market's expected excess return. The SPT analysis shows that a stock's expected excess arithmetic return is equal to its beta times the market's expected excess arithmetic return plus one-half the market's variance of return times the excess of the stock's beta over 1. Compared to the MPT version of CAPM, the SPT version of CAPM shows that high beta stocks offer more expected excess arithmetic return and low beta stocks offer less expected excess arithmetic return.
Modern Portfolio Theory and Investment Analysis
An excellent resource for investors, Modern Portfolio Theory and Investment Analysis, 9th Edition examines the characteristics and analysis of individual securities as well as the theory and practice of optimally combining securities into portfolios. A chapter on behavioral finance is included, aimed to explore the nature of individual decision making. A chapter on forecasting expected returns, a key input to portfolio management, is also included. In addition, investors will find material on value at risk and the use of simulation to enhance their understanding of the field.
Handbook of Quantitative Finance and Risk Management
Author: Cheng-Few Lee
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-14
Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This two-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From "arbitrage" to "yield spreads," the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.