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. For the selected processes of stock price dynamics a set of mathematical models for conditional variance has been constructed with simplified and complex structure. It is shown that the best short term forecasting results could be achieved with the exponential autoregression model with conditional heteroscedasticity and with the stochastic volatility model. It can explained with the fact that both models take into consideration influence of random disturbances with different signs. The results of numerical modeling can be used in computer based decision support systems for financial process control, making decisions regarding stock trading, forming the financial instruments portfolio and so on.

Publication year: 
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