Open AccessPairs Trading Strategy for A and H Shares Based on Kalman-HMM Approach
Author(s) -
Ming Zhang
Publication year - 2021
Publication title -
proceedings of business and economic studies
Language(s) - English
Resource type - Journals
eISSN - 2209-265X
pISSN - 2209-2641
DOI - 10.26689/pbes.v4i5.2606
Subject(s) - statistical arbitrage , hidden markov model , kalman filter , pairs trade , trading strategy , cointegration , profitability index , econometrics , computer science , algorithmic trading , financial market , arbitrage , high frequency trading , index arbitrage , order (exchange) , economics , artificial intelligence , financial economics , finance , alternative trading system , risk arbitrage , capital asset pricing model , arbitrage pricing theory
Pairs trading is a statistical arbitrage strategy that takes advantage of unbalanced financial markets. A common difficulty for quantitative trading participants is the detection of market institutional changes in financial markets. In order to solve this issue, the hidden Markov model (HMM) is applied for status detection. The research objective is to use Kalman filter to predict and the hidden Markov model (HMM) to identify state transitions on the basis of screening transaction pairs with obvious co-integration relationship. This research would prove the profitability of the strategy and the ability to resist risk through the combination of these two methods with real data. The empirical results showed that compared with the traditional cointegration strategy, the holding yield increased from 1.6% to 16.2% and the maximum pullback reduced to 0.02%. Further research is required to improve trading rules.