John M. Charnes

REAL OPTIONS VOLATILITY ESTIMATION WITH CORRELATED INPUTS

Real Options Analysis (ROA) provides a framework for valuing reactive and proactive managerial flexibility in investment decisions. Estimating the volatility parameter for a real options model is challenging because there are typically no historical returns for the underlying asset and no current market prices. A previously developed method of using simulation to estimate the volatility parameter for a real investment is demonstrated. The effects of serial price correlation and price-demand cross-correlation on volatility parameters developed with this method are explained. Finally, managerial implications of these findings are discussed.

Using simulation for option pricing

Monte Carlo simulation is a popular method for pricing financial options and other derivative securities because of the availability of powerful workstations and recent advances in applying the tool. The existence of easy-to-use software makes simulation accessible to many users who would otherwise avoid programming the algorithms necessary to value derivative securities. This paper presents examples of option pricing and variance reduction, and demonstrates their implementation with Crystal Ball 2000, a spreadsheet simulation add-in program.

Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation

The main goal of this paper is to describe a new multistage Monte Carlo (MMC) simulation method for solving influence diagrams using local computation. Global methods have been proposed by others that sample from the joint probability distribution of all the variables in the influence diagram. However, for influence diagrams having many variables, the state space of all variables grows exponentially, and the sample sizes required for good estimates may be too large to be practical. In this paper, we develop a MMC method, which samples only a small set of chance variables for each decision node in the influence diagram. MMC is akin to methods developed for exact solution of influence diagrams in that we limit the number of chance variables sampled at any time. Because influence diagrams model each chance variable with a conditional probability distribution, the MMC method lends itself well to influence diagram representations.

Analyzing multivariate output

This paper gives an overview of multivariate statistical techniques that can be useful for analyzing discrete-event simulation output, and describes some of the latest directions in research on multivariate output analysis. A general discussion is given of constructing joint confidence regions on the mean vector of multivariate output from independent replications of terminating models. The multivariate batch means method of simultaneous estimation of means from one long run of steady-state simulation models is described. References are also given for autoregressive, spectral analysis and regenerative methods of inference, as well as variance-reduction and sequential techniques.

How top management steers fast cycle teams to success

Every merger and acquisition deal presents a different goal and a different mix of critical issues to manage. Making, consummating, and integrating a deal puts pressure on chief executives to play multiple leadership roles and switch quickly from one role to another throughout the merger process. The roles employed vary dramatically with the type of deal and how ambitious the strategy. As the rationales for transactions have changed, new challenges have evolved, especially for those leading the deals: leaders must establish and communicate the strategic vision for the merger ‐‐ they need to explain the top four or five sources of value in the deal and what the core values and culture of the new organization should be; leaders must cheer on the stakeholders to generate enthusiasm for the merger or acquisition, and to confront fear and uncertainty in its various forms; leaders must close the deal; leaders captain change by managing the integration of the two entities; and leaders crusade for the new entity. These five roles are essential to all transactions, but leaders need to employ each at different times. The strategic rationale behind the deal, and the inherent risks and opportunities that it presents, determines which roles a leader needs to play and when.

Using Control Charts to Corroborate Bribery in Jai Alai

Abstract Statistical process control charts were used in the State of Florida District Court to help establish the guilt of an individual who was alleged to have affected the outcome of jai alai contests by bribing some of the contestants to lose. By placing wagers on the nonbribed contestants the briber gains an increased chance of winning, which is to the detriment of the other bettors. This paper gives an example of how statistical process control techniques can be employed to detect the unusually high bets that generally accompany bribery of the contestants. If the management of the jai alai gaming facility had been using control charts on a regular basis, the game fixing might have been detected much sooner.

Approximating free exercise boundaries for American-style options using simulation and optimization

Monte Carlo simulation can be readily applied to asset pricing problems with multiple state variables and possible path dependencies because convergence of Monte Carlo methods is independent of the number of state variables. This paper applies Monte Carlo simulation to the problem of determining free exercise boundaries for pricing American-style options. We use a simulation-optimization method to identify approximately optimal exercise thresholds that are defined by a minimal number of parameters. We demonstrate that asset prices calculated using this method are comparable to those found using other numerical asset pricing methods.

Safety Stock Determination With Serially Correlated Demand in a Periodic-Review Inventory System

We consider a periodic-review inventory replenishment model with an order-up-to-R operating doctrine for the case of deterministic lead times and a covariance-stationary stochastic demand process. A method is derived for setting the inventory safety stock to achieve an exact desired stockout probability when the autocovariance function for Gaussian demand is known. Because the method does not require that parametric time-series models be fit to the data, it is easily implemented in practice. Moreover, the method is shown to be asymptoticaly valid when the autocovariance function of demand is estimated from historical data. The effects on the stockout rate of various levels of autocorrelated demand are demonstrated for situations in which autocorrelation in demand goes undetected or is ignored by the inventory manager. Similarly, the changes to the required level of safety stock are demonstrated for varying levels of autocorrelation.

Vector-autoregressive inference for equally spaced, time-averaged, multiple queue length processes

This paper investigates the performance of the vector-autoregressive method of analyzing multivariate output data (numbers in subsystem) from queueing network models vis-a-vis three other methods of multivariate analysis-Bonferroni batch means, multivariate batch means, and spectral analysis. Differences in performance for all methods are found when time averages of numbers in subsystem are used rather than discretized observations taken at equally spaced points in simulated time. Further investigation is made into the effect of varying the spacing of averaging times for the methods. The results show that the analysis of time averages rather than discretized observations leads to slightly improved performance for all methods considered but that there is little difference in the relative performance of the methods considered.

Output analysis research: why bother? A panel discussion

In the last two decades, many different techniques have been developed and investigated by researchers for summarizing the output and drawing conclusions from simulation experiments. The lack of practitioner interest in using these techniques has been ignored by some researchers but has become a source of great existential angst among others. Some have suggested that the root cause of this disinterest is a lack of communication between researchers and practitioners. It seems appropriate to look at the different views of output analysis research. This paper is an attempt to (i) determine the level of practitioner usage in the methods developed by output analysis researchers, (ii) find out how the output analytic research is perceived by practitioners of simulation, and (iii) find out what topics simulation practitioners think are important for further investigation or development by researchers. The intent is to discuss these three main issues as well as any other relevant issues.