Volatility Depends on Market Trades and Macro Theory

TL;DR

This paper explores how market trade randomness influences price and return volatility, emphasizing the need for advanced econometric models and macroeconomic theories of second order to improve volatility predictions and forecasting accuracy.

Contribution

It introduces a framework linking trade value and volume dependencies to market-based volatilities, highlighting the lack of second-order macroeconomic theories for better predictions.

Findings

Market trade randomness affects price and return volatility.

Current macroeconomic models lack second-order theories for volatility prediction.

Developing second-order macroeconomic theories can improve forecasting accuracy.

Abstract

We consider the randomness of market trade as the origin of price and return stochasticity. We look at time series of trade values and volumes as random variables during the averaging interval {\Delta} and describe the dependences of market-based volatilities of price and return on the volatilities and correlations of market trade values and volumes. We describe the market-based origin of the lower boundaries of the accuracy of macroeconomic variables and consider, as an example, the accuracy of macroeconomic investments. We highlight that current macroeconomic models describe relations between the 1st order variables determined by sums of trade values or volumes. To predict market-based volatilities of price, return, and volatilities of macroeconomic variables, one should develop econometric methodologies, collect data, and elaborate macroeconomic theories of the 2nd order that model the mutual dependence of the 1st and 2nd order economic variables. The absence of macroeconomic theories of the 2nd order means no economic basis for predictions of market-based volatilities of price and return, as well as volatilities of any macroeconomic variables. In turn, that limits the accuracy of forecasting probabilities of price, return, and the accuracy of macroeconomic variables in the best case by Gaussian distributions.