Geodesic
Possibilities of construction economic-mathematical models of financial market dynamics using local level components
Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika, no. 4 (2018), pp. 76-86 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

This paper is devoted to presentation of application local level and local level with drift models in order to solve a problem of developing common methods and techniques for creating economic-mathematical models for forecating dynamics of financial market time series. In paper, basing on initially introduced forecasting model that takes into account phenomenon of price deviation from its equilibrium value, as one of the basic technical analysis tool, there were proposed two additional economic-mathematical models, containing local level and local level with drift components. Complicated by the corresponding components, additional models were evaluated by classical Kalman filtering, using data of three exchange rate markets, US dollar against ruble (USDRUB), euro (EURUSD) and Swiss franc (USDCHF) for the year of 2017, and demonstrated higher forecasting capabilities, judging by percentage of correct forecast directions, in comparison with the initial model, which in turn confirms potential possibility of their real economic use, in particular, on binary options market, presented in paper as example.
Keywords: financial market, forecasting, economic-mathematical models, local-level and local level with drift models. 
@article{VTPMK_2018_4_a5,
     author = {A. R. Musin and A. S. Sorokin},
     title = {Possibilities of construction economic-mathematical models of financial market dynamics using local level components},
     journal = {Vestnik Tverskogo gosudarstvennogo universiteta. Seri\^a Prikladna\^a matematika},
     pages = {76--86},
     year = {2018},
     number = {4},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VTPMK_2018_4_a5/}
}
TY  - JOUR
AU  - A. R. Musin
AU  - A. S. Sorokin
TI  - Possibilities of construction economic-mathematical models of financial market dynamics using local level components
JO  - Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika
PY  - 2018
SP  - 76
EP  - 86
IS  - 4
UR  - http://geodesic.mathdoc.fr/item/VTPMK_2018_4_a5/
LA  - ru
ID  - VTPMK_2018_4_a5
ER  - 
%0 Journal Article
%A A. R. Musin
%A A. S. Sorokin
%T Possibilities of construction economic-mathematical models of financial market dynamics using local level components
%J Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika
%D 2018
%P 76-86
%N 4
%U http://geodesic.mathdoc.fr/item/VTPMK_2018_4_a5/
%G ru
%F VTPMK_2018_4_a5
A. R. Musin; A. S. Sorokin. Possibilities of construction economic-mathematical models of financial market dynamics using local level components. Vestnik Tverskogo gosudarstvennogo universiteta. Seriâ Prikladnaâ matematika, no. 4 (2018), pp. 76-86. http://geodesic.mathdoc.fr/item/VTPMK_2018_4_a5/

[1] Ajvazyan S. A., Mkhitaryan V. S., Applied Statistics and Econometrics Basics, Uchebnik dlya vuzov, Yunity Publ., Moscow, 1998, 1030 pp. (in Russian)

[2] Kijima M., Stochastic Processes with Applications to Finance, Second Edition, Chapman and Hall/CRC, London, 2013 | MR | Zbl

[3] Schannep J., Dow theory for the 21th century: Technical indicators for improving your investment results, John Wiley and Sons, New York, 2008

[4] Murphy J. J., Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications, Prentice Hall Press, New Jersey, 1999

[5] Boswijk P., Hommes C., Manzan S., “Behavioral heterogeneity in stock prices”, Journal of Economic Dynamics and Control, 31 (2007), 1938–1970 | DOI | MR | Zbl

[6] Lux T., Marchesi M., “Scaling and criticality in a stochastic multi-agent model of a financial market”, Nature, 397 (1999), 498–500 | DOI

[7] Romanovskij M. Yu., Introduction to Econophysics. Statistical and Dynamic Models, IKI Publ., Moscow, 2007, 280 pp. (in Russian)

[8] Tsypin A. P., Sorokin A. S., “Statistical software packages in socio-economic research”, The Azimuth of Scientific Research: Economics and Management, 5:4 (2016), 379–384 (in Russian)

[9] Young A. R., Expert Advisor Programming: Creating Automated Trading Systemsin MQL for MetaTrader 4, Edgehill Publishing, Nashville, TN, 2010

[10] Harvey A. C., “Analysis and generalisation of a multivariate exponential smoothing model”, Management Science, 32 (1986), 374–380 | DOI | MR | Zbl

[11] West M., Harrison P. J., Bayesian Forecasting and Dynamic Models, 2nd edition, Springer-Verlag, New York, 1997 | MR | Zbl

[12] Kalman R. E., “A New Approach to Linear Filtering and Prediction Problems”, Journal of Basic Engineering. Transactions of the American Society of Mechanical Engineers, 82:1 (1960), 35–45

[13] Commandeur J. J. F., Koopman S. J., Ooms M., “Statistical Software for State Space Methods”, Journal of Statistical Software, 41:1 (2011), 1–18 | DOI

[14] Musin A. R., “Comparison of the quality of forecast models of the foreign exchange market using Kalman filtering and traditional time series models”, Online Journal “Naukovedenye”, 2017, no. 3, 1–11 (in Russian)