Trofymchuk O.M.

Forecasting Volatility of Financial Processes with Alternative Models

An analysis of modern approaches to modeling of conditional variance for nonstationary heteroscedastic processes is performed. A stochastic volatility model structure is proposed for multidimensional case and the methodology is considered for its parameter estimation with the use of Markov chain Monte Carlo technique. The use of this approach provides a possibility for parameter estimation of linear and nonlinear models in conditions of stochastic disturbance influence with various distributions of random variables.

Informational Decision Support System for Forecasting Financial and Economic Processes Based on Structural and Parametric Adaptation of Models

A concept is proposed for solving the problem of adaptive forecasting that is based on the system analysis methodology and combined use of preliminary data processing techniques, mathematical and statistical modeling, forecasting and optimal state estimation of the processes under study. The cyclical adaptation of a structure and model parameters on the basis of a set of statistical characteristics of a process under study provides a possibility for reaching high quality estimates of forecasts with condition that data is informative.