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Metal Model for High Frequency prediction Variables Used For gold the international interbank spot data is taken in ($/Oz), silver data taken from international interbank spot in ($/Oz), copper prices are taken from LME 3 months forward ($/ton), crude oil taken from NYMEX ($/bbl), euro/usd is taken form spot interbank refrence rate, Dow taken as INDEX data declared by NYSE. dow Dow Index euro_usd Euro/USD Crude_nymex Crude Oil Light sweet copper_usd Copper silver Silver gold Gold Variables Name
Correlation Matrix Correlation analysis shows that there is a good correlation between gold and euro_usd and then gradually sloping towards silver and then dow, copper and crude_nymex. Crude has shown the least correlation to gold and then copper. 1.0 0.0 0.9 0.3 0.6 0.7 dow 0.7 1.0 0.3 0.5 0.0 0.4 crude_nymex 0.9 0.3 1.0 0.5 0.6 0.8 euro_usd 0.3 0.5 0.5 1.0 0.4 0.6 Copper 0.6 0.0 0.6 0.4 1.0 0.8 silver 0.7 0.4 0.8 0.6 0.8 1.0 Gold dow crude_nymex euro_usd Copper silver Gold
Regression results R 2 shows a high degree of fit, and SD for residual is well into range.
Analysis and conclusion The model shows a greater degree of fit i.e R2 of 0.9 and hence can be considered as a good model for gold price prediction. The scatter diagram for residual fits shows that the predictor maximum deviation ranges from -40 to +40 but well remain in the median range of $2-4. The below scatter chart plots the residual with date.
The selection of independent variables was subjected to availability of high frequency data (daily) so more independent variables should be introduced.
Dow and euro_usd shows high correlation and hence may pose a problem of multicolinearity but P (Probability of hypothesis) has not eliminated either of the variables.
Elimination of one of the variables changes the model, but eventually elimination of Dow from the model keeps the fitness intact, hence dow can be significantly removed, future work will include addition of such not correlated exogenous independent variable in the equation.
The model may be fitted with Indian data for making it relevant to Indian market.
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