The change or the first difference of Y is c. I accurate prediction of the consequent references were possible from the series, ten the next period would be an increment to the current level to achieve the next level. Riterations for all higher levels takes place. Te constant model expansion yieldsFinancial data are very volatile due to the many factors that affect its behaviour (Franses 1998). Tis makes it very unpredictable. Smetimes the first differences of the series have different variances, a the volatility diagram below depicts. Te variance almost stationary throughout the sample period, athough there is a slight change in the differences around 2002 to 2003.
Ater that, tey are stationary and stable until the end of the period. Gven the stationary appearance of the first difference, aconstant model is the appropriate model for use in this series. Te model forecasts the changes of the Rolls Royce share prices. Aplication of the constant model for the logged series of the first difference is an equivalent of the estimation of a random walk model for the original Te data below originates from fitting a geometric randomwalk on DLOG (RR) on the period between 1/03/2000 and 12/31/2007.
I this geometric randomwalk with growth, te constant term is 0. Tis is a representative of the percentage average returns of 0.049% for the sample period between 1/03/2000 and 12/31/2007. Tis is also an increase in the daily value of the share price by 0. I most financial forecasting series, te geometric randomwalk model is in use, b default. Is usefulness has limits to forecasting the mean of the returns.
is because it only takes into account the first moment of the series during analysis. Te trading that takes place between buyers and sellers in the market makes up the financial time series at the financial market. De to the many exogenous factors that influence the patterns and behaviour of the market, pice series are not the preferential variables to work with (Mills 1999). Fr better analysis, te variables that are in use are the series of returns and the first difference of log price series. I the series returns, vlatility tends to happen in clusters.
Tis is volatility clustering. Tis occurs due to the tendency of larger changes occurring whether positive or negative, wich are always followed by clusters of changes of the complement sign. Tis observation takes place...
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