Фондовые рынки в условиях смены условий торговли

Abstract

Stock indices are among the indicators of the state of the economy, that among the first to respond to both the positive and the negative economic phenomena. It makes the understanding of mechanisms influencing them very important. Structural Vector Autoregression model (SVAR) approach is widely used for this purpose. These models allow us to estimate impulse responses of indices to the impact of different economic variables. A slightly different Smooth Transition Autoregression model (STAR) approach that allows identifying differences in responses due to economic conditions is used in this paper for the estimating of responses of stock indices. More specifically we apply Smooth Transition Vector Error Correction model (STVECM) approach. We use oil prices as the characteristic of the Russian economy defining changes in economic conditions and as a proxy defining changes in terms of trade, since oil is one of the major export goods for Russia. Other macroeconomic factors used in the paper are state budget expenses, consumer price index (CPI), the exchange rate of the dollar against the ruble, ratio of the exchange rates of dollar and euro against the ruble, LIBOR interest rate, and the S&P500 index. Obtained results show that the responses differ significantly depending on the level of oil prices. These results are also useful for the design of mechanisms affecting stock market.