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.

 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

### RESULTS –GBP /USD ( 1991 – 2000)

Measure Without Transaction Cost With transaction cost Daily return Annualized return Annualized vol

0.1627%

41.0110%

21.9895%

1 bp

0.1527%

38.4910%

21.9895%

2 bp

0.1427%

35.9710%

21.9895%

3 bp

0.1327%

33.4510%

21.9895%

cumulative return Sharpe ratio Maximum daily profit

792.3320913

743.6456913

694.9592913

646.2728913

1.865028187871280

1.750427871462690

1.635827555054100

1.521227238645500

0.053053754

0.052953754

0.052853754

0.052753754

-0.033799175

53.36438923

46.63561077

-0.033899175

53.05383023

46.94616977

-0.033999175

52.95031056

47.04968944

-0.034099175

52.69151139

47.30848861

FORECASTING ACCURACY PERFORMANCE FOR GBP /USD ( 1991 – 2000) RMSE Model BVAR-TVP Random Walk 0.029884

0.049023

LS* MSE-T ENC-T -0.39649

20.90143

27.9397

• 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.

### RESULTS –JPY/USD ( 1991 – 2000)

Measure Without Transaction Cost With transaction cost Daily return Annualized return Annualized vol cumulative return Sharpe ratio Maximum daily profit Maximum daily loss % winning trades % losing trades

0.0611%

15.3903%

24.4108% 285.644367

0.630472317044453

0.074769383

-0.05107331

51.83189655

48.16810345

1 bp

0.0511%

12.8703%

24.4108% 238.873167

0.527239240395165

0.074669383

-0.05117331

51.67025862

48.32974138

2 bp

0.0411%

10.3503%

24.4108% 192.101967

0.424006163745878

0.074569383

-0.05127331

51.45474138

48.54525862

3 bp

0.0311%

7.8303%

24.4108% 145.330767

0.320773087096594

0.074469383

-0.05137331

51.34698276

48.65301724

FORECASTING ACCURACY PERFORMANCE JPY/USD ( 1991 – 2000) RMSE Model BVAR-TVP Random Walk 0.000232

0.037633

LS* MSE-T ENC-T -0.79703

41.17992

41.13595

• • 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.

### 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