Statistical Analysis of Stock Indices Research Paper

Statistical Analysis of Stock Indices - Research motif ExampleOn the other hand, according to Pelaez (1999, 232) there be many ways to forecast economic series, including extrapolation, econometric models, time-series models, and leading indicator models. For the issue under analysis in this report, the test for social unit root is considered as the most appropriate tool for evaluating the given data series from the Stock indices. The regularityology applied has been considered as most appropriate after a thorough consideration of the specific subject involved.A proficient overview on the nuances of the unit root test is presented followed by the analysis of the Stock indices given in SPSS v14.0. This method will enable the presentation of both the theories and the practical application using current softwargon to relaxation the process and eliminate errors.Guido (2001, 164) says that the composite intrinsic value measure does not appear to be an satisfactory measure of a sto cks or portfolios value in his experiment to compare the US and the Australian markets. Several affirmable reasons are offered for this difference, including the differing market structures, the give of a different index or the use of alternate statistical tests. In the light of the higher up arguments, it is clear that for the data set under analysis it is essential to use a strong statistical tool to identify the relationship between the given stock indices.Dickey-Fuller statistic tests for the unit root in the time series data. Pt is regressed against Pt-1 to test for unit root in a time series haphazard walk model, which is given asPt = r Pt-1 + ut (1)If r is significantly mates to 1, then the stochastic variable Pt is said to be having unit root. A series with unit root is said to be un-stationary and does not follow random walk. There are three most popular Dickey-Fuller tests used for testing unit root in a series.The above equation can be rewritten asD Pt = d Pt-1 + ut (2)Here d = (r - 1) and here it is tested if d is equal to zero. Pt is a random walk if d is equal to zero.It is possible that the time series could behave as a random walk with a drift. This mode that the value of Pt may not center to zero and thus a unbroken should be added to the random walk equation. A linear trend value could also be added along with the constant to the equation, which results in a null hypothesis reflecting stationary deviations from a trend. To test the validity of market efficiency, random walk hypothesis has been tested. Unit root test has been conducted on Pt, natural log values of indices worth data by running the regression equations of the following typeD Pt = d Pt-1 + ut (3)D Pt = a + d Pt-1 + ut (4)D Pt = a + dPt-1 + b t + ut (5)where, a is constant term and b is the coefficient of trend term. The null hypothesis for each isH0 d = 0 (viii)The null hypothesis that Pt is a random walk can be rejected if calculated t is greater than the tabulated t. F rom the aforementioned it is clear that the test for unit root is a reliable analytical tool to test the consistency of the data series. In case of the stock market indices we are analysing, the test for unit root is a reliable tool to test the extent to which the index is speculating. The widening from the autoregressive analysis for unit root test reveals that the behaviour of the stock indices it is clear that OMXCOPENHAGEN and MADRIDSEGENERAL have