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Business Forecasting Using eViews - Essay Example

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Business Forecasting Using eViews

The company had a major crisis in the last quarter of 2002 until mid-2003. Some of their equipment failed in the market and they faced a multi-million pound lawsuit. This led to a huge deficit in their pension fund. This was the cause of the major fall of their share prices in that period. From the analysis, the prices were performing better from that point onward and rose in an almost steady gradient. This was until the first quarter of 2005 when the share price fell slightly, then another steady rise to mid-2006. Subsequently, there was a drop of the index by about 50. The graph then rises and a number of more falls follow before the end of the sample period. Due to the noise of the graph, trend identification is difficult (Brooks 2002). A histogram illustrates a slightly clearer representation of the data, this helps with the noise problem. Returns Due to the non-linearity and volatility of financial data, it is difficult to predict the level of the series at each period (Mills 1999). The figure below is stationary, which makes it more predictable. It also depicts the first differences in the series. It shows the first differences of the Rolls Royce shares during the period 01/01/2000 and ending 31/12/2007. The differences in this period are stationary relative to their mean; their variance is relatively stable as well. There is a slight increase in the middle of the graph between mid-2002 and mid-2003. The first difference of the logarithms is the returns to share in the financial market. This is the representation of the value. Returns = dlog (RR). A constant model should represent a data series with stationary a mean (Brooks 2002). This is the appropriate model for this series. The stationery mean’s representation is: dYt = c+et The change or the first difference of Y is c. If accurate prediction of the consequent references were possible from the series, then the next period would be an increment to the current level to achieve the next level. Reiterations for all higher levels takes place. The constant model expansion yields Yt = c+Yt-1+et This is the representation of an actual randomwalk model with a drift. This model captures, or gets the data generation of a financial series. Volatility Financial data are very volatile due to the many factors that affect its behaviour (Franses 1998). This makes it very unpredictable. Sometimes the first differences of the series have different variances, as the volatility diagram below depicts. The variance is almost stationary throughout the sample period, although there is a slight change in the differences around 2002 to 2003. After that, they are stationary and stable until the end of the period. Given the stationary appearance of the first difference, a constant model is the appropriate model for use in this series. The model forecasts the changes of the Rolls Royce share prices. Randomwalk Application 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 series. The data below originates from fitting a geometric randomwalk on DLOG (RR) on the period between 1/03/2000 and 12/31/2007. In this geometric randomwalk with growth, the constant term is 0.000498. This is a representative of the percentage average returns of 0.049% for the sample period between 1/03/2000 and 12/31/2007. This is also an increase in the daily value of the share price by 0.049%. In most financial forecasting series, ...Show more

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Course: Year: Subject: Business Forecasting Using eViews Student: Tutor: Introduction The use of eViews is an essential tool in the forecasting and analysis of econometric data. The presentation of data is in the form of graphs and models. These graphs and models relay a lot of information on the business under analysis…
Author : wunschathena
Business Forecasting Using eViews essay example
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