Forecasting Crude Oil (Spot Price) Volatility Dissertation

Forecasting Crude Oil (Spot Price) Volatility - Dissertation ExampleDaily prices for sodding(a) crude colours argon effective in volatility forecasting. 17 It was also imperative to use the two cluster epitome in the paper. 17 In the case of GARCH to obtain the unknowns the formula was applied where the initial value Xk was interpreted to be 25.56 where a= 0.001 (fixed) 17 b= 0.00 18 c= 0.00 18 In using the akin formula the values for a, b and c were P-GARCH established to be 18 a= 0.001(fixed) 18 b= 0.394 18 c= 0.050 18 Xk= 25.56 18 For GARCH GJR, the values were found to be 18 a=0.001 (fixed) 18 b= 0.488 18 c= 0.110 18 Xk= 25.56 18 for E GARCH a=0.001 (fixed) 18 b= 0.488 18 c= 0.11 18 From the findings captured in the spread sheet, we can derive various important circumstanceors about the GARCH family models and settle important questions arising from the same. These are 19 The data should be within range in order to own rid of outlier values.The data is reliable since the p rojection/ forecasted values are within limit. There are no outlier values as a result of projection. 19 The null hypothesis Garch models predict uniformly 19 Alternative hypothesis- GARCH models predictions differ. Based on the results, it is clear that there exists variations among the four models. Thus it is rational to conclude that the pick hypothesis holds. 19 The best model should be as closer to the baseline as possible. GARCH is a replica of the baseline and and so cannot be taken to be the best.Of the four GJR GARCH varies the least from the baseline hence is the best. 19 EGARCH has the largest variation from the baseline hence is the worst. 19 20 BIBLIOGRAPHY 20 APPENDICES.20 METHODOLOGY AND DATA knowledgeableness Volatility is a concept that refers to the spread of all possible outcomes within an uncertain variable. Finance rail has various uncertain variables such as prices of products, returns on assets, and share prices amongst others (Olowe, 2010). Modeling and forecasting of volatility bring in been attributed to increasing uncertainty in financial aspects and components (Day & Lewis, 1993). Oil price fluctuations in the global arena finger significant uncertainties thereby invoking interests amongst financial and market participants (Kang, Kang, & Yoon, 2009). The main reasons explaining such significant interest include the fact that oil price fluctuations affect decision making process for both producers and consumers in addition to investors decisions. Whereas oil price fluctuations affect strategic planning and appraisal of projects for producers and consumers, investors continue to face challenges in investment, allocation of portfolios, and worry of risks decisions (Campbell, Lo, & MacKinlay, 1997). Policy and decision making within the oil markets require accurate forecasting of the crude oil prices (Olowe, 2010). Attaining accurate and adequate forecasting require adequate and accurate data. In most cases, daily prices of cru de oil are used to predict or forecast volatility (uncertainty) for purposes of developing effective policies and decision making processes (Campbell, Lo, & MacKinlay, 1997). Forecasting volatility of crude oil prices have been done for a