Economic Complexity Limits Accuracy of Price Probability Predictions by Gaussian Distributions

TL;DR

This paper argues that economic factors fundamentally limit the accuracy of Gaussian-based predictions for asset prices and returns, especially regarding their moments, impacting the reliability of financial models.

Contribution

It provides an economic explanation for the inherent limitations in predicting price and return moments using Gaussian distributions, highlighting the absence of models for certain market variables.

Findings

Predictions are limited by economic variables of higher order.

Uncertainty in volatility predictions due to lack of models.

Forecast accuracy impacts asset pricing and portfolio theories.

Abstract

We discuss the economic reasons why the predictions of price and return statistical moments in the coming decades, in the best case, will be limited by their averages and volatilities. That limits the accuracy of the forecasts of price and return probabilities by Gaussian distributions. The economic origin of these restrictions lies in the fact that the predictions of the market-based n-th statistical moments of price and return for n=1,2,.., require the description of the economic variables of the n-th order that are determined by sums of the n-th degrees of values or volumes of market trades. The lack of existing models that describe the evolution of the economic variables determined by the sums of the 2nd degrees of market trades results in the fact that even predictions of the volatilities of price and return are very uncertain. One can ignore existing economic barriers that we highlight but cannot overcome or resolve them. The accuracy of predictions of price and return probabilities substantially determines the reliability of asset pricing models and portfolio theories. The restrictions on the accuracy of predictions of price and return statistical moments reduce the reliability and veracity of modern asset pricing and portfolio theories.