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US Dollar Index (DXY): Modeling against Domestic and Global Macro-Economic Factors

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US Dollar is considered one of the safest currencies in the world and used as a benchmark for all practical purposes. DXY (US Dollar Index) is an index to measure the proxy strength of US Dollar against world currencies.

The model is built to measure the factors impacting the value of the Index. Several data mining frameworks have been used and predictive analytics methods applied to improve the accuracy of the model

Published in: Economy & Finance, Technology
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US Dollar Index (DXY): Modeling against Domestic and Global Macro-Economic Factors

  1. 1.   Modeling against Domestic and Global Macro-Economic Factors 3/9/2013 Rama Kappagantula Kiran Sankuru Global Equity Fund
  2. 2.  US Dollar Index – DXY  Domestic & Global Macroeconomic Factors o Euro o Yen o VIX o S&P o Gold o BDI o Unemployment o Inflation o LEI o GDP o Money Supply o Current Acct 3/9/2013 Kiran Sankuru | Rama Kappagantula 2
  3. 3. US Dollar Index – DXY o Started in 1973 o Proxy for US $ o Strength against world currencies Euro, Yen, GDP, Canadian $, Swedish Krona, Swiss Franc 3/9/2013 Kiran Sankuru | Rama Kappagantula 3 History & Performance 50 60 70 80 90 100 110 120 130 DXY
  4. 4. Data Exploration Analysis DM Methods Apply & Evaluate Interpretation & Performance Conclusion 3/9/2013 Kiran Sankuru | Rama Kappagantula 4
  5. 5. CIA.gov Dept. of Labor US Dept. of Treasury Conference Board 3/9/2013 Kiran Sankuru | Rama Kappagantula 5 Collection
  6. 6. Timeline Frequency Missing values 3/9/2013 Kiran Sankuru | Rama Kappagantula 6 Exploration Predictor(s) Frequency Adjusted Frequency VIX, S&P,OIL, Gold, 10YrTres,Euro, UKSterling, BDI, DXY and JPYen Daily Daily UnEmployment, PMI, Inflation, LEI, Debt, MoneySupply, TradeBalance Monthly Daily GDP, CurrentAccount Quarterly Daily
  7. 7. Scatterplot matrix Normalization Summary Statistics Correlation Matrix PCA Analysis Regression Trees 3/9/2013 Kiran Sankuru | Rama Kappagantula 7 Analysis & Cleanup
  8. 8. Normalization o Xnorm = (X – Min)/(Max - Min) Summary Stats 3/9/2013 Kiran Sankuru | Rama Kappagantula 8 Analysis & Cleanup
  9. 9. Correlation Matrix 3/9/2013 Kiran Sankuru | Rama Kappagantula 9 Analysis & Cleanup
  10. 10. PCA Analysis 3/9/2013 Kiran Sankuru | Rama Kappagantula 10 Analysis & Cleanup
  11. 11. Regression Tree 3/9/2013 Kiran Sankuru | Rama Kappagantula 11 Analysis & Cleanup
  12. 12.  Predictors 3/9/2013 Kiran Sankuru | Rama Kappagantula 12 Dependent Variable DXY Predictors Euro JPYen VIX S&P Gold BDI Unemployment Inflation LEI Money Supply GDP Current Acct
  13. 13. Neural Networks 3/9/2013 Kiran Sankuru | Rama Kappagantula 13 Methodology
  14. 14. Network Diagram 3/9/2013 Kiran Sankuru | Rama Kappagantula 14 Neural Networks 1 2 4 5 3 6 Input Layer Hidden Layer Output Layer S&P GDP DXY W13 O3 O4 O5 O6W14 W15 W23 W24 W25 W36 W46 W56 Outputj = 1 / (1 + e-(Oj + ∑ Wij * Xi ))
  15. 15. Training the Model o Input nodes: 12 predictors o # of Hidden layers: 1 o # of Hidden layer nodes: 12 o # of Epochs: 30 o Output nodes: 1 3/9/2013 Kiran Sankuru | Rama Kappagantula 15 Neural Networks
  16. 16.  Data Partition: Training – 80%, Validation – 20%  Runs 3/9/2013 Kiran Sankuru | Rama Kappagantula 16 Neural Networks - Implementation #Run #Hidde n layer #Hidden Layer Nodes #No of epochs/iteratio ns RMSE - Training RMSE – Validation RMSE Chg Return 1 1 12 30 0.107477474 0.111156007 3.423% 2 1 24 30 0.121486949 0.126022016 3.733% 3 1 6 30 0.099382581 0.102438743 3.075% 4 1 4 30 0.103504299 0.107414073 3.777% 5 1 6 45 0.066935626 0.067200881 0.396% 6 1 6 150 0.019869902 0.019022021 -4.267% 7 1 6 300 0.01733016 0.01701691 -1.808% -5.000% -4.000% -3.000% -2.000% -1.000% 0.000% 1.000% 2.000% 3.000% 4.000% 5.000% 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 12:30 24:30 6:30 4:30 6:45 6:150 6:300 RMSE RMSE - Training RMSE - Validation RMSE Chg Rt
  17. 17. Final Design o Input nodes: 12 predictors o # of Hidden layers: 1 o # of Hidden layer nodes: 6 o Output nodes: 1 Xactual = (Xnorm)*(b - a) + a  for each predicted period Note: a – minimum in the original range & b – maximum in the original range 3/9/2013 Kiran Sankuru | Rama Kappagantula 17 Neural Networks
  18. 18. 3/9/2013 Kiran Sankuru | Rama Kappagantula 18 Results -5.000% -4.000% -3.000% -2.000% -1.000% 0.000% 1.000% 2.000% 3.000% 4.000% 0 20 40 60 80 100 120 140 1/3/1994 7/3/1994 1/3/1995 7/3/1995 1/3/1996 7/3/1996 1/3/1997 7/3/1997 1/3/1998 7/3/1998 1/3/1999 7/3/1999 1/3/2000 7/3/2000 1/3/2001 7/3/2001 1/3/2002 7/3/2002 1/3/2003 7/3/2003 1/3/2004 7/3/2004 1/3/2005 7/3/2005 1/3/2006 7/3/2006 1/3/2007 7/3/2007 1/3/2008 7/3/2008 1/3/2009 7/3/2009 1/3/2010 7/3/2010 1/3/2011 7/3/2011 1/3/2012 7/3/2012 Training Data Regular Value - Predicted Regular Value - Actual % Prediction Error -4.000% -3.000% -2.000% -1.000% 0.000% 1.000% 2.000% 3.000% 4.000% 0 20 40 60 80 100 120 140 1/7/1994 6/7/1994 11/7/1994 4/7/1995 9/7/1995 2/7/1996 7/7/1996 12/7/1996 5/7/1997 10/7/1997 3/7/1998 8/7/1998 1/7/1999 6/7/1999 11/7/1999 4/7/2000 9/7/2000 2/7/2001 7/7/2001 12/7/2001 5/7/2002 10/7/2002 3/7/2003 8/7/2003 1/7/2004 6/7/2004 11/7/2004 4/7/2005 9/7/2005 2/7/2006 7/7/2006 12/7/2006 5/7/2007 10/7/2007 3/7/2008 8/7/2008 1/7/2009 6/7/2009 11/7/2009 4/7/2010 9/7/2010 2/7/2011 7/7/2011 12/7/2011 5/7/2012 10/7/2012 Validation Data Regular Value - Predicted Regular Value - Actual % Prediction Error
  19. 19. Model Performance 3/9/2013 Kiran Sankuru | Rama Kappagantula 19 Validation -1.200% -1.000% -0.800% -0.600% -0.400% -0.200% 0.000% 0.200% 0.400% 77.5 78 78.5 79 79.5 80 80.5 81 1/1/2013 1/2/2013 1/3/2013 1/4/2013 1/5/2013 1/6/2013 1/7/2013 1/8/2013 1/9/2013 1/10/2013 1/11/2013 1/12/2013 1/13/2013 1/14/2013 1/15/2013 1/16/2013 1/17/2013 1/18/2013 1/19/2013 1/20/2013 1/21/2013 1/22/2013 1/23/2013 1/24/2013 1/25/2013 1/26/2013 1/27/2013 1/28/2013 1/29/2013 1/30/2013 1/31/2013 DXY - Test Data 01/01/2013 - 01/31/2013 DXY - Actual DXY - Predicted % Prediction Error
  20. 20. Q1 2013 & Q2 2013 3/9/2013 Kiran Sankuru | Rama Kappagantula 20 Prediction Date DXY_Norm Actual DXY_Norm Predicted DXY Actual Time Series DXY Predicted Q1 2013 0.175558586 0.174875784 79.765 79.731 Q2 2013 0.187186009 0.234279779 80.345 82.693

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