50 research outputs found
Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models
This chapter gives a survey on the use of statistical designs for what-if analysis in simula- tion, including sensitivity analysis, optimization, and validation/verification. Sensitivity analysis is divided into two phases. The first phase is a pilot stage, which consists of screening or searching for the important factors among (say) hundreds of potentially important factors. A novel screening technique is presented, namely sequential bifurcation. The second phase uses regression analysis to approximate the input/output transformation that is implied by the simulation model; the resulting regression model is also known as a metamodel or a response surface. Regression analysis gives better results when the simu- lation experiment is well designed, using either classical statistical designs (such as frac- tional factorials) or optimal designs (such as pioneered by Fedorov, Kiefer, and Wolfo- witz). To optimize the simulated system, the analysts may apply Response Surface Metho- dology (RSM); RSM combines regression analysis, statistical designs, and steepest-ascent hill-climbing. To validate a simulation model, again regression analysis and statistical designs may be applied. Several numerical examples and case-studies illustrate how statisti- cal techniques can reduce the ad hoc character of simulation; that is, these statistical techniques can make simulation studies give more general results, in less time. Appendix 1 summarizes confidence intervals for expected values, proportions, and quantiles, in termi- nating and steady-state simulations. Appendix 2 gives details on four variance reduction techniques, namely common pseudorandom numbers, antithetic numbers, control variates or regression sampling, and importance sampling. Appendix 3 describes jackknifing, which may give robust confidence intervals.least squares;distribution-free;non-parametric;stopping rule;run-length;Von Neumann;median;seed;likelihood ratio
Perfect simulation, monotonicity and finite queueing networks
International audienceTutorial on perfect sampling with applications to queueing network
Stock Price Dynamics and Option Valuations under Volatility Feedback Effect
According to the volatility feedback effect, an unexpected increase in
squared volatility leads to an immediate decline in the price-dividend ratio.
In this paper, we consider the properties of stock price dynamics and option
valuations under the volatility feedback effect by modeling the joint dynamics
of stock price, dividends, and volatility in continuous time. Most importantly,
our model predicts the negative effect of an increase in squared return
volatility on the value of deep-in-the-money call options and, furthermore,
attempts to explain the volatility puzzle. We theoretically demonstrate a
mechanism by which the market price of diffusion return risk, or an equity
risk-premium, affects option prices and empirically illustrate how to identify
that mechanism using forward-looking information on option contracts. Our
theoretical and empirical results support the relevance of the volatility
feedback effect. Overall, the results indicate that the prevailing practice of
ignoring the time-varying dividend yield in option pricing can lead to
oversimplification of the stock market dynamics.Comment: 23 pages, 7 figures, 2 table
Perfect simulation, monotonicity and finite queueing networks
International audienceTutorial on perfect sampling with applications to queueing network
Vector computers, Monte Carlo simulation, and regression analysis: An introduction (Version 2)
Monte Carlo Technique;Supercomputer;computer science