Abstract
In this paper, the problem of estimating the current value of financial instruments using multidimensional statistical analysis is considered. The research considers various approaches to constructing regression computational schemes using quotes of financial instruments correlated to the data as regressors. An essential feature of the problem is the chaotic nature of its observation series, which is due to the instability of the probabilistic structure of the initial data. These conditions invalidate the constraints under which traditional statistical estimates remain non-biased and effective. Violation of experiment repeatability requirements obstructs the use of the conventional data averaging approach. In this case, numeric experiments become the main method for investigating the efficiency of forecasting and analysis algorithms of observation series. The empirical approach does not provide guaranteed results. However, it can be used to build sufficiently effective rational strategies for managing trading operations.
Highlights
The principal problem of managing any objects in conditions of non-stationarity, nonuniformity, and/or chaotic dynamics is the lack of repeatability, which obstructs the use of conventional statistical research techniques
In a number of formulations, it turns out that the system of correlations between components has a known inertia, which allows it to be used with some time lag to adjust the current values of the working component
One such formulation of the problem of estimating the current value of a financial instrument is considered in this paper
Summary
The principal problem of managing any objects in conditions of non-stationarity, nonuniformity, and/or chaotic dynamics is the lack of repeatability, which obstructs the use of conventional statistical research techniques. In this case, statistical extrapolation, which is the basis of automatic generation of management decisions, turns out to be ineffective, or even inapplicable [1,2,3]. The current market value of an asset is monitored based on the readings of the corresponding instrument. These readings can be adjusted, taking into account other similar instruments. The hypothesis is that the system of correlations between instruments has a significant degree of inertia [4,5,6] and is approximately preserved with a small time lag
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