Overview

Portfolio optimization problems involve identifying portfolios that satisfy three criteria: Minimize a proxy for risk, Match or exceed a proxy for return and Satisfy basic feasibility requirements. Portfolios are points from a feasible set of assets that constitute an asset universe. A portfolio specifies either holdings or weights in each individual asset in the asset universe. The convention is to specify portfolios in terms of weights, although the portfolio optimization tools work with holdings as well. The set of feasible portfolios is necessarily a nonempty, closed, and bounded set. The proxy for risk is a function that characterizes either the variability or losses associated with portfolio choices. The proxy for return is a function that characterizes either the gross or net benefits associated with portfolio choices. The terms risk and risk proxy and return and return proxy are interchangeable. The fundamental insight of Markowitz is that the goal of the portfolio choice problem is to seek minimum risk for a given level of return and to seek m ximum return for a given level of risk. Portfolios satisfying these criteria are efficient portfolios and the graph of the risks and returns of these portfolios forms a curve called the efficient frontier. Financial Toolbox has three objects to solve specific types of portfolio optimization problems: - The Portfolio object (Portfolio) supports mean-variance portfolio optimization. This object has either gross or net portfolio returns as the return proxy, the variance of portfolio returns as the risk proxy, and a portfolio set that is any combination of the specified constraints to form a portfolio set. - The Portfolio CVaR object (PortfolioCVaR) implements what is known as conditional value-at-risk portfolio optimization, which is generally referred to as CVaR portfolio optimization. CVaR portfolio optimization works with the same return proxies and portfolio sets as mean-variance portfolio optimization but uses conditional value-at-risk of portfolio returns as the risk proxy. - The Portfolio MAD object (PortfolioMAD) implements what is known as meanabsolute deviation portfolio optimization, which is referred to as MAD portfolio optimization. MAD portfolio optimization works with the same return proxies and portfolio sets as mean-variance portfolio optimization but uses mean-absolute deviation portfolio returns as the risk proxy.

Full Product Details

9781983487255

Table of Contents

Reviews

Author Information

Tab Content 6

Customer Reviews

Recent Reviews

Countries Available