The generator of almost all economic crises in modern history are financial crises, the trigger of which are exchange crashes. Currently, the situation is exacerbated and the risks are increasing. This is because the bulk of transactions are now made by computers. Working strictly according to algorithms aimed mainly at achieving quick results, they guarantee the absence of even minimal losses. They act almost synchronously, which can cause a chain reaction of collapse on the exchanges in isolation from the real state of affairs in the economy, and from the real value of assets. Meanwhile, regulators have no meaningful or reliable tools to monitor or manage any particularly volatile situations in the financial markets.
This is particularly valid in organized markets or exchanges where the prices of all global goods and assets are largely measured. All these management processes are currently reliant on tools using the analysis of accumulated historical experience and the use of empirical parametric models [Intriligator, 1971]. Therefore, overcoming the obvious stagnation in the development of theoretical finance is a long-overdue global task. The main challenge now is to overcome the near complete absence of a mathematical apparatus with which to describe the functioning of the exchange as an asset-pricing mechanism. Financial econometrics can do this qualitatively, but also required is the ability to calculate the temporal fine structure of the price and trade volume dynamics within short time intervals, such as during a single trading session.
Using a parallel with the physics theory of scattering, we can look at this differently. Econometrics focuses on solving the so-called «inverse problem», namely, the problem of extracting information from experimental data about the system under study. Conversely, we aim to solve a direct problem: the creation of a near-universal method of calculation from the first principles (ab initio) of the temporal exchange microstructures. These, with a characteristic time size of several temporal seconds, can be directly compared with the corresponding experimental fine structures of trading dynamics. This method could serve as a powerful tool for building a quantitative theory of exchanges.
We hope that in future, the probabilistic theory of exchanges developed in this study can serve as a basis for building a more general probabilistic financial theory. In doing so, a deeper understanding will be gained of how our global world of finance works.
It is obvious that organized markets are complex, multi-agent, non-equilibrium probabilistic systems, the description of which requires the application of adequate mathematical methods and apparatuses. The only suitable source of such methods and apparatuses is physics, where the experience of theoretical work with multiparticle systems with similar, formal structures has long been accumulated. In addition, quite a lot of experience has already been gathered in the application of the physical method in economics, namely, the use of formal methods and approaches of theoretical physics in solving economic problems.
In particular, probabilistic economic theory was developed [Kondratenko, 2005, 2015], a new theory of market economy. Initially, this theory was modeled on quantum mechanics with the derivation of economic equations of motion. Unfortunately, we are not yet able to accurately solve these equations for multi-agent markets. Because of this, a simpler version of the theory was later developed. It uses only the probabilistic method without solving equations of motion, namely, probability economics. It is used in this work as a basic theory for constructing probabilistic theory of exchanges. Although it contains no equations of motion, there is a mathematical apparatus that has proven very adequate and fruitful for describing exchange processes and structures.