1. by Benziger Alice Priyanka Snehal Khair Prakash SuseendranVigeendharan Tiwari Ashutosh

2.  implemented BVAR-TVP parameters in matlab  Kalman implementation – Kalman toolbox in matlab  Data – Bloomberg  Optimization done for two parameters out of six (due to computation constraints), rest 4 parameters best fit value is used as per recommendation in paper

3.  The BVAR TVP parameters are regressed against recent data points ( last 1 month ) instead of the entire data points.  Advantages  Less Computations. Faster results.  More importance to recent Trends  For GBP/USD  This approach gives rise to higher annualized returns and less RMSE  GBP/USD returns obtained are 41% and is better than the 5.7% returns obtained by using the approach mentioned in paper by author.

4.  The daily excess returns over the period (t, t+1), it, from this trading strategy are  obtained as follows:  where z t = +1 for long (buy signal) FC position and z t = -1 for short (sell signal) FC

5. Measure Without Transaction Cost With transaction cost 1 bp2 bp3 bp Daily return0.1627%0.1527%0.1427%0.1327% Annualized return41.0110%38.4910%35.9710%33.4510% Annualized vol21.9895% cumulative return792.3320913743.6456913694.9592913646.2728913 Sharpe ratio1.8650281878712801.7504278714626901.6358275550541001.521227238645500 Maximum daily profit0.0530537540.0529537540.0528537540.052753754 Maximum daily loss-0.033799175-0.033899175-0.033999175-0.034099175 % winning trades53.3643892353.0538302352.9503105652.69151139 % losing trades46.6356107746.9461697747.0496894447.30848861

6. ModelRMSELS*MSE-TENC-T BVAR-TVP0.029884 Random Walk0.049023-0.3964920.9014327.9397 RMSE obtained by BVAR-TVP model is less than random walk. Hence the prediction using this model is more accurate than a random walk model. RMSE Less than the RMSE obtained by the Author Returns obtained by using the trading strategy mentioned earlier are substantial, suggesting model is accurate in prediction of FX rates.

7. MeasureWithout Transaction Cost With transaction cost 1 bp2 bp3 bp Daily return0.0611%0.0511%0.0411%0.0311% Annualized return15.3903%12.8703%10.3503%7.8303% Annualized vol24.4108% cumulative return285.644367238.873167192.101967145.330767 Sharpe ratio0.6304723170444530.5272392403951650.4240061637458780.320773087096594 Maximum daily profit0.0747693830.0746693830.0745693830.074469383 Maximum daily loss-0.05107331-0.05117331-0.05127331-0.05137331 % winning trades51.8318965551.6702586251.4547413851.34698276 % losing trades48.1681034548.3297413848.5452586248.65301724

8. ModelRMSELS*MSE-TENC-T BVAR-TVP0.000232 Random Walk0.037633-0.7970341.1799241.13595 RMSE obtained by BVAR-TVP model is less than random walk. Hence the prediction using this model is more accurate than a random walk model. Returns obtained by using the strategy are low but substantial.

9.  Financial Econometrics Kalman Filter: some applications to Finance University of Evry - Master 2  Modelling and forecasting exchange rates with a Bayesian time-varying coefficient model Fabio Canova*  http://www.cs.unc.edu/~welch/kalman/ http://www.cs.unc.edu/~welch/kalman/  http://www.cs.ubc.ca/~murphyk/Software/Kalma n/kalman_download.html http://www.cs.ubc.ca/~murphyk/Software/Kalma n/kalman_download.html  http://en.pudn.com/downloads158/sourcecode/o thers/detail706436_en.html