The development of information technology for modeling and evaluating the financial and economic risks with accounting of the uncertainties of different nature (based on Bayesian models)
A new technique of the data mining was proposed that combines the causal networks and methods for risk assessment in the form of stochastic volatility models. The approach includes the following stages: (1) definition and classification process critical elements under study, in order to identify and characterize risk factors; (2) constructing causal model in the form of believe Bayesian networks; (3) create a set of candidates scenarios process; (4) modeling and evaluating the risks of the critical factors based on Bayesian stochastic volatility models using methods of optimal filtering. Method is implemented as a computer program for correct parameter estimation of nonlinear stochastic volatility models and optimal estimation for state financial processes, forecasting the conditional variance and calculation of parameters of financial risks.
The algorithms tested on examples of actual volatility forecasting optimization of financial processes, presented statistics of exchange rates of various currencies as well as for predicting the degree of risk associated with the implementation of these processes.
For estimation of the model parameters of stochastic volatility and VaR values established specification among OpenBUGS, which represents an effective tool for performing Bayesian analysis. The proposed specification is highly flexible and practical use possibility of functional expansion.
Based on the developed software and algorithmic support an information processing system of data in real time, which is different from the known possibility of using any operating system and allows you to build mathematical models of nonlinear nonstationary financial processes to assess whether variables and their conditional variance and determine the level of potential losses.