Pairs Trading Strategy for A and H Shares Based on Kalman-HMM Approach

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.

A Cooperative Dynamic Approach to Pairs Trading

Based on recent works on stocks comovement, Pairs Trading’s strategy is enhanced by reducing the stock universe to the stocks with the lower volatility on a given date. From this universe of low volatility stocks, pairs are selected by looking for pairs whose series present a high degree of antipersistence. Finally, a “reversion to the mean” strategy is applied to these pairs. It is shown that, with this approach to Pairs Trading, positive results can be obtained for stock from the Nasdaq stock exchange, mainly during bull markets and low volatility periods.

On optimal thresholds for pairs trading in a one-dimensional diffusion model

We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed. doi:10.1017/S1446181121000298

ON OPTIMAL THRESHOLDS FOR PAIRS TRADING IN A ONE-DIMENSIONAL DIFFUSION MODEL

We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed.

Optimal pairs trading with dynamic mean-variance objective

Pairs trading is a typical example of a convergence trading strategy. Investors buy relatively under-priced assets simultaneously, and sell relatively over-priced assets to exploit temporary mispricing. This study examines optimal pairs trading strategies under symmetric and non-symmetric trading constraints. Under the assumption that the price spread of a pair of correlated securities follows a mean-reverting Ornstein-Uhlenbeck(OU) process, analytical trading strategies are obtained under a mean-variance(MV) framework. Model estimation and empirical studies on trading strategies have been conducted using data on pairs of stocks and futures traded on China’s securities market. These results indicate that pairs trading strategies have fairly good performance.

Composition of portfolios by pairs trading with volatility criteria in the Brazilian market,

ABSTRACT The objective of this study was to understand how the shares’ volatility affects the portfolios’ dynamics formed using the model of pairs trading in the Brazilian stock market. This article distinguished itself by bringing new evidence about the effects of volatility in the pairs trading model not covered by previous studies, expanding the sample size analyzed in the Brazilian stock market. The chosen theme’s relevance is that investors can use pairs trading or long-short models to build their portfolios. The use of cointegration concepts probabilistically contributes to portfolios’ formation weakly correlated to the market indexes with superior performance. This article impacts the area by contributing new evidence for better use of the model in the analysis of investments. From January 2016 to December 2018, the 90 most liquid assets of Bolsa, Brasil, Balcão (B3) were analyzed, totaling 5,927,400 possible pairs. The Augmented Dickey-Fuller test and subsequent backtesting of the pairs in the proposed period were used to evaluate the cointegration criteria. Statistical analysis was performed by parametric and non-parametric tests and Pearson and Spearman correlation analyses. The results found indicated that the formation of portfolios by pairs trading with dependent assets with the criterion of higher levels of volatility (20 periods) presented a superior performance. These findings can be justified by a better risk and return ratio for the portfolio, measured by the Sharpe Index of the returns obtained concerning the portfolio’s volatility, compared to a portfolio formation based on a random selection of the pairs. In addition, the results also showed a low correlation of returns concerning the market index. Therefore, the application of the statistical cointegration analysis methodology alone does not guarantee results that are different from the market average.