Report

QUANTITATIVE TRADING STRATEGIES ON THE SHORT-TERM PREDICTABILITY OF EXCHANGE RATES:A BVAR TIME-VARYING PARAMETERS APPROACH -NICHOLAS SARANTIS by Benziger Alice Priyanka Snehal Khair Prakash SuseendranVigeendharan Tiwari Ashutosh PROCEDURES USED AND IMPLEMENTATION METHODOLOGIES APPLIED 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 IMPROVISATIONS 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. TRADING STRATEGY The daily excess returns over the period (t, t+1), it, from this trading strategy are obtained as follows: where zt= +1 for long (buy signal) FC position and zt = -1 for short (sell signal) FC RESULTS –GBP /USD ( 1991 – 2000) Measure With transaction cost Without Transaction Cost 1 bp 2 bp 3 bp Daily return 0.1627% 0.1527% 0.1427% 0.1327% Annualized return 41.0110% 38.4910% 35.9710% 33.4510% Annualized vol 21.9895% 21.9895% 21.9895% 21.9895% 792.3320913 743.6456913 694.9592913 646.2728913 1.865028187871280 1.750427871462690 1.635827555054100 1.521227238645500 Maximum daily profit 0.053053754 0.052953754 0.052853754 0.052753754 Maximum daily loss -0.033799175 -0.033899175 -0.033999175 -0.034099175 % winning trades 53.36438923 53.05383023 52.95031056 52.69151139 % losing trades 46.63561077 46.94616977 47.04968944 47.30848861 cumulative return Sharpe ratio FORECASTING ACCURACY PERFORMANCE FOR GBP /USD ( 1991 – 2000) Model RMSE LS* BVAR-TVP 0.029884 Random Walk 0.049023 MSE-T -0.39649 ENC-T 20.90143 • 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. 27.9397 RESULTS –JPY/USD ( 1991 – 2000) Measure Without Transaction Cost With transaction cost 1 bp 2 bp 3 bp Daily return 0.0611% 0.0511% 0.0411% 0.0311% Annualized return 15.3903% 12.8703% 10.3503% 7.8303% Annualized vol 24.4108% 24.4108% 24.4108% 24.4108% 285.644367 238.873167 192.101967 145.330767 0.630472317044453 0.527239240395165 0.424006163745878 0.320773087096594 Maximum daily profit 0.074769383 0.074669383 0.074569383 0.074469383 Maximum daily loss -0.05107331 -0.05117331 -0.05127331 -0.05137331 % winning trades 51.83189655 51.67025862 51.45474138 51.34698276 % losing trades 48.16810345 48.32974138 48.54525862 48.65301724 cumulative return Sharpe ratio FORECASTING ACCURACY PERFORMANCE JPY/USD ( 1991 – 2000) Model RMSE LS* BVAR-TVP 0.000232 Random Walk 0.037633 MSE-T -0.79703 ENC-T 41.17992 • 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. 41.13595 REFERENCES 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.ubc.ca/~murphyk/Software/Kalma n/kalman_download.html http://en.pudn.com/downloads158/sourcecode/o thers/detail706436_en.html