A multivariate skew-garch model
Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news. As a result, we assume that European stock market returns are affected by endogenous and exogenous shocks. The former raise in the market itself, the latter come from the US market, because of its most influential role in the world. Under standard assumptions, the distribution of the European market index returns conditionally on the sign of the one-day lagged US return is skew-normal. The resulting model is denoted Skew-GARCH. We study the properties of this new model and illustrate its application to time-series data from three European financial markets.
In this paper, a new method is proposed to adaptively estimate the number of transmit antennas in cognitive radios and surveillance systems. It employs Schur complement to test the linearly independence of the new coming data with the previous data set. ?2 test is conducted based on the statistics of Schur complement variable. Last, Monte Carlo simulation is employed to test the performance of the estimator under different scenarios.