This thesis investigates the predictive accuracy of several statistical models have in sealed-bid auctions under different market conditions. This paper studies all such models through comprehensive literature review and empirical study, including Friedman’s model, Gates’ model, Skitmore’s multivariate model, Hanssmann and Rivett’s Model, Knode and Swanson’s stochastic dynamic programming model, option pricing approach, and basic mean models (i.e., arithmetic, geometric, harmonic). The adopted research method relied on computer simulation to develop synthetic datasets representing real auctions settings. This has in turn been able to give a comparative evaluation of the effectiveness of each model. The results show that predictive accuracy depends on the model considered, the number of bidders, and the variability of bids.