QUANTITATIVE TRADING STRATEGIES

ON THE SHORT-TERM PREDICTABILITY OF EXCHANGE RATES:A BVAR TIME-VARYING PARAMETERS APPROACH -NICHOLAS SARANTIS by

Benziger Alice PriyankaSnehal Khair PrakashSuseendranVigeendharanTiwari 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 Without Transaction Cost

With 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%

cumulative return 792.3320913 743.6456913 694.9592913 646.2728913

Sharpe ratio 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

FORECASTING ACCURACY PERFORMANCE FOR GBP /USD ( 1991 – 2000)

Model RMSE LS* MSE-T ENC-T

BVAR-TVP 0.029884

Random Walk 0.049023 -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

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%

cumulative return 285.644367 238.873167 192.101967 145.330767

Sharpe ratio 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

FORECASTING ACCURACY PERFORMANCE JPY/USD ( 1991 – 2000)

Model RMSE LS* MSE-T ENC-T

BVAR-TVP 0.000232

Random Walk 0.037633 -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/Kalm

an/kalman_download.html http://en.pudn.com/downloads158/

sourcecode/others/detail706436_en.html

Thank You