Constricting the risk-management model on FOREX market and portfolio optimization in terms of orders volume

N.A. Ionova

Abstract


This paper identifies the new method in setting the volumes for trades in suggestion that summary profit of the company is maximum. The method consists of two parts: 1. Building a prognosis for volume and rate 2. Optimizing summary profit according to built prognosis. In this article prognosis is constructing using the Garch/Egarch models ,because of the heteroscedasticity of using data. The best prognoses method is determining by given data. Optimization model is building by L - BFGS – B, Nelder – Mead, Conjugate Gradient methods.

The article begins with the description of the research area. The mathematical substantiation of the problem is given in the second part. The third part describes program realization and results of analysis on the given dataset. In the last part of the article summarizes the results and gives the generalization of the problem for other datasets.


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References


Philip Hans Franses, Dick van Dijk, Nonlinear time series models in empirical finance, Cambridge University Press, 2000

Tim Bollerslev. A conditionally heteroskedastic time series model for speculative prices and rates of return, JSTor, 1987

Robert F. Engle, Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, Vol. 50, No. 4, Jul. 1982

Predtechenskij A.G., Postroenie modelej avtoregressionnoj uslovnoj geteroskedastichnosti (ARCH) nekotoryh indikatorov rossijskogo finansovogo rynka, diplomnaja rabota, NGU, 2000

Pengfei Sun, Chen Zhou, Erasmus University Rotterdam. How to apply GARCH model in risk management? Model diagnosis on GARCH innovations, De Nederlandsche Bank, 2008

Murav'ev D. G., Matematicheskie metody razrabotki i ocenki strategij torgovli na mezhbankovskom valjutnom rynke Forex, dissertacija, Samara, 2006

Jeduardo Rossi, Jekonometricheskij likbez: volatil'nost'. Odnomernye GARCH - modeli: obzor, universitet Pavii, Italija, 2010

S.Je. Mastickij, V.K. Shitikov, Statisticheskij analiz i vizualizacija dannyh s pomoshh'ju R, Hajdel'berg – London –Tol'jatti, 2014

Varfolomeeva E.D., Analiticheskij modul' dlja prognozirovanija dohodnosti finansovyh instrumentov na osnovanii GARCH-modeli volatil'nosti, diplomnaja rabota, Sankt-Peterburg, 2009

Anan'ev M.A., Mitin N.A., Sravnenie linejnyh i nelinejnyh avtoregressionnyh modelej uslovnoj geteroskedastichnosti na primere dohodnosti indeksa RTS, IPM im.M.V.Keldysha RAN, 2013


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