Pairs trading with wavelet transform
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Routledge Journals, Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Pairs Trading, Wavelet Transform, Minimum Distance Method, Cointegration Method, Statistical Arbitrage, >, Statistical Arbitrage, Cointegration, Strategies, Risk, Predictability, Returns
Kaynak
Quantitative Finance
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
23
Sayı
7.Ağu
