On the Applicability of Maximum Likelihood Methods: From Experimental to Financial Data

Abstract

This paper addresses whether and to what extent econometric methods used in experimental studies can be adapted and applied to financial data to detect the best-fitting preference model. To address the research question, we implement a frequently used nonlinear probit model in the style of Hey and Orme (1994) and base our analysis on a simulation stud. In detail, we simulate trading sequences for a set of utility models and try to identify the underlying utility model and its parameterization used to generate these sequences by maximum likelihood. We find that for a very broad classification of utility models, this method provides acceptable outcomes. Yet, a closer look at the preference parameters reveals several caveats that come along with typical issues attached to financial data, and that some of these issues seems to drive our results. In particular, deviations are attributable to effects stemming from multicollinearity and coherent under-identification problems, where some of these detrimental effects can be captured up to a certain degree by adjusting the error term specification. Furthermore, additional uncertainty stemming from changing market parameter estimates affects the precision of our estimates for risk preferences and cannot be simply remedied by using a higher standard deviation of the error term or a different assumption regarding its stochastic process. Particularly, if the variance of the error term becomes large, we detect a tendency to identify SPT as utility model providing the best fit to simulated trading sequences. We also find that a frequent issue, namely serial correlation of the residuals, does not seem to be significant. However, we detected a tendency to prefer nesting models over nested utility models, which is particularly prevalent if RDU and EXPO utility models are estimated along with EUT and CRRA utility models.