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Forecasting Crude Oil (Spot Price) Volatility
Finance & Accounting
Pages 20 (5020 words)
Forecasting Crude Oil (Spot Price) Volatility Name Course Institution Date Table of Contents METHODOLOGY AND DATA 2 Introduction 2 Volatility clustering 4 Data for GARCH Models 6 Estimation 10 Models Used in the Study 11 GARCH (1,1) Model 12 EWMA is considered to be a special type of GARCH(1,1) 15 EGARCH (1,1) Model 15 Data and Sample Size Selection 17 There are four main benchmarks within the global arena in respect to international trading: West Texas Intermediate (WTI), Brent, Dubai, and Tapis.
Daily prices for crude oils are effective in volatility forecasting. 17 It was also imperative to use the two cluster analysis in the paper. 17 In the case of GARCH to obtain the unknowns the formula was applied where the initial value Xk was taken to be 25.56 where a= 0.001 (fixed) 17 b= 0.00 18 c= 0.00 18 In using the same 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 factors about the GARCH family models and answer important questions arising from the same. These are 19 The data should be within range in order to get rid of outlier values.The data is reliable since the projection/ 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. ...
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