A Primer on Applying Monte Carlo Simulation, Real Options Analysis, Knowledge Value Added, Forecasting, and Portfolio Optimization

Technology projects are inherently risky; research shows that large IT projects succeed as originally planned only 28 percent of the time. Building flexibility, or real options, into a project can help manage this risk. Furthermore, the management flexibility of options has value, as the downside risk is reduced and the upside is increased. The case is based upon real options analysis for an enterprise data warehouse (EDW) and analytic customer relationship management (CRM) program at a major U.S. firm. The firm has been disguised as Global Airlines for confidentiality reasons. The data mart consolidation or EDW marginally meets the hurdle rate for the firm as analyzed using a traditional net present value (NPV) analysis. However, different tactical deployment strategies help mitigate the risk of the project by building options into the project, and the traditional NPV is expanded by the real option value. Students analyze the different deployment strategies using a binomial model compound option Excel macro, and calculate the volatility using Monte Carlo analysis in Excel. A step-by-step tutorial is provided to teach students how to accomplish the real options analysis for a simplified project, and this tutorial is easily generalized by students to the case scenario. In addition to the tactical options, the case also has the strategic growth option of analytic CRM. Students must therefore analyze both the tactical and strategic growth options and make a management recommendation on funding the project and also recommend an optimal deployment strategy to manage the project risk.The case teaches real options for technology projects. Students learn how to calculate real option values, where the key input of volatility is obtained by Monte Carlo analysis in Excel. Students also learn that the real option value is “real,” resulting from active management mitigating the risk of the project and improving the upside. Most important, students understand the difference between tactical vs. strategic growth options and the important management issues to consider.

Get full-text (via PubEx)

Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation

Journal of Economic Dynamics and Control ◽

10.1016/j.jedc.2016.01.001 ◽

2016 ◽

Vol 64 ◽

pp. 23-38 ◽

Cited By ~ 29

Author(s):

F. Cong ◽

C.W. Oosterlee

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Portfolio Optimization ◽

Mean Variance Portfolio ◽

Mean Variance

Get full-text (via PubEx)

Real options analysis in the engineering company practice

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis ◽

10.11118/actaun201361072303 ◽

2013 ◽

Vol 61 (7) ◽

pp. 2303-2309

Author(s):

Václav Klepáč ◽

Petr Kříž ◽

David Hampel

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Real Options ◽

Net Present Value ◽

Simulation Software ◽

Real Options Analysis ◽

The Real ◽

Investment Projects ◽

Research Problems ◽

Path Dependent

In this paper, we deal with the real options analysis of selected investment projects. This approach is supplemented and compared to calculations of the net present value (NPV). Two research problems are analyzed: acquisition of the simulation software for the foundry industry in the sense of the expansive options and options on leaving the project in the case of acquisition of the spectrometer. For the option valuation, there were used analytical and numerical methods like the Black-Scholes model, binomial model and Monte Carlo simulations. In the case of binomial pricing model we used modification describing the behavior of the project’s cash-flow (CF) due to capacity of the company, path-dependent addiction and embedded option barrier. To extend the application of the real options analysis, we propose procedures for sensitivity analysis and option pricing based on Monte Carlo simulations for particular case of stochastic volatility.

Get full-text (via PubEx)