Pairs trading with wavelet transform
研究发现，对标准普尔500成分股价格应用小波变换能滤除共同噪声，使配对交易策略的参数估计更稳定、价差更均值回归，从而显著提高收益并降低下行风险。
We show that applying the wavelet transform to S&P 500 constituents' prices generates a substantial increase in the returns of the pairs-trading strategy. Pairs trading strategy is based on finding prices that move together, but if there is shared noise in the asset prices, the co-movement, on which one base the trades, might be caused by this common noise. We show that wavelet transform filters away the noise, leading to more profitable trades. The most notable change occurs in the parameter estimation stage, which forms the weights of the assets in the pairs portfolio. Without filtering, the parameters estimated in the training period lose relevance in the trading period. However, when prices are filtered from common noise, the parameters maintain relevance much longer and result in more profitable trades. Particularly, we show that more precise parameter estimation is reflected on a more stationary and conservative spread, meaning more mean reversion in opened pairs trades. We also show that wavelet filtering the prices reduces the downside risk of the trades considerably.