Code covered by the BSD License  

Highlights from
Analyzing Investment Strategies with CVaR Portfolio Optimization

image thumbnail

Analyzing Investment Strategies with CVaR Portfolio Optimization


Bob Taylor (view profile)


18 Dec 2012 (Updated )

Scripts and data to demonstrate the new PortfolioCVaR object in Financial Toolbox.

%% Analyzing Investment Strategies with CVaR Portfolio Optimization in MATLAB
% Robert Taylor
% The MathWorks, Inc.

% Copyright (C) 2012 The MathWorks, Inc.

%% Introduction

% This script is a "superscript" that organizes the scripts for the webinar "Analyzing Investment
% Strategies with CVaR Portfolio Optimization in MATLAB." It describes what each script does and
% shows the order in which the scripts should be examined.

%% Instructions

% The file structure for these scripts has a top-level folder that contains these scripts and should
% have two folders 'data' and 'source' with data and analytics. To add these folders to the path,
% start in the folder with the scripts and execute the commands


% The script cvarwebinar_scenarios.m, which simulates scenarios, must be run before subsequent
% scripts can be run because it generates a file BuyWriteScenarios.mat that is needed for these
% scripts. The script to generate scenarios can take about one hour on a typical computer and
% requires that the computer be a 64-bit machine. It creates a 12MB file in the ./data folder. Make
% sure that the script to generate scenarios is run in the folder that contains this script so that
% the scenarios file ends up in the correct data folder.

%% Theory

% This script illustrates basic features of covered-call strategies and provides a theoretical
% analysis of issues regarding slippage due to assignment and re-investment.


%% From Theory to Reality

% This script illustrates an event-driven simulation of total returns for uncovered and covered
% positions based on a single realization of an underlying stock. It moves from the simplicity of
% theory to the messiness of reality as it models and simulates various contributions to slippage.


%% Calibration

% This script is the first of the sequence of scripts to illustrate a complete workflow to analyze a
% covered-call strategy for a universe of 26 stocks. Given total return price data, this script
% illustrates maximum likelihood calibration of the assumed geometric Brownian motion process for
% the universe of stocks.


%% Scenario Generation

% This script is the second of the sequence of scripts that generates scenarios for uncovered and
% covered positions to be used for subsequent portfolio optimization and analysis. This is the
% slowest script since it generates scenarios by simulation of investment actions during the course
% of an investment period.


%% Normality Tests

% This script is the third of the sequence of scripts that examines the statistical properties of
% the scenarios. As the probability of early exercise increases during an investment period, the
% distribution of the log of covered-call returns becomes increasingly non-normal.


%% Optimization

% This script is the final of the sequence of scripts that performs several portfolio optimization
% steps with both CVaR and mean-variance portfolio optimization. The difference in results between
% the two types of optimization is examined to provide greater insights into the normative
% implications of covered-call strategies.


%% References
% # P. Bernstein (1998), _Against the Gods: The Remarkable Story of Risk_, Wiley.
% # F. Black (1975), "Fact and Fantasy in the Use of Options," _Financial Analysts Journal_, Vol. 31,
% No. 4, pp. 36-41 and 61-72.
% # P. Glassermann (1991), _Monte Carlo Methods in Financial Engineering_, Springer.
% # I. Karatzas and S. Shreve (1991), _Brownian Motion and Stochastic Calculus_, 2nd ed., Springer.
% # H. Markowitz (1952), "Portfolio Selection," _Journal of Finance_, Vol. 7, No. 1, pp. 77-91.
% # R. Merton, M. Scholes, and M. Gladstein (1978), "The Returns and Risk of Alternative Call Option 
% Portfolio Investment Strategies," _Journal of Business_, Vol. 51, No. 2, pp. 183-242.
% # R. T. Rockafellar and S. Uryasev (2002). "Conditional Value-at-Risk for General Loss
% Distributions," _Journal of Banking and Finance_, Vol. 26, pp. 1443-1471.

Contact us