A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns - Endres, Sylvia; Stübinger, Johannes (Quantitative Finance, 2019)
This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifically, the highly flexible algorithm automatically determines the number of regimes for any stochastic process and provides a complete set of parameter estimates. We demonstrate its performance in a simulation study-the algorithm achieves promising results for the general class of Levy-driven Ornstein-Uhlenbeck processes with regime switches. In our empirical back-testing study, we apply our regime classification algorithm to propose a high-frequency pair selection and trading strategy. The results show statistically and economically significant returns with an annualized Sharpe ratio of 3.92 after transaction costs-results remain stable even in recent years. We compare our strategy with existing quantitative trading frameworks and find its results to be superior in terms of risk and return characteristics. The algorithm takes full advantage of its flexibility and identifies various regime patterns over time that are well-documented in the literature.
CRIS-Team WoS Importer:2019-04-02

Optimal trading strategies for Levy-driven Ornstein-Uhlenbeck processes - Endres, Sylvia; Stübinger, Johannes (Applied economics, 2019)
This study derives an optimal pairs trading strategy based on a Levy-driven Ornstein-Uhlenbeck process and applies it to high-frequency data of the S&P 500 constituents from 1998 to 2015. Our model provides optimal entry and exit signals by maximizing the expected return expressed in terms of the first-passage time of the spread process. An explicit representation of the strategy's objective function allows for direct optimization without Monte Carlo methods. Categorizing the data sample into 10 economic sectors, we depict both the performance of each sector and the efficiency of the strategy in general. Results from empirical back-testing show strong support for the profitability of the model with returns after transaction costs ranging from 31.90% p.a. for the sector 'Consumer Staples' to 278.61% p.a. for the sector 'Financials'. We find that the remarkable returns across all economic sectors are strongly driven by model parameters and sector size. Jump intensity decreases over time with strong outliers in times of high market turmoil. The value-add of our Levy-based model is demonstrated by benchmarking it with quantitative strategies based on Brownian motion-driven processes.
CRIS-Team WoS Importer:2019-04-12

Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. - Stübinger, Johannes; Endres, Sylvia (Quantitative Finance, 2018)

Review of stochastic differential equations in statistical arbitrage pairs trading - Endres, Sylvia; et al. (Managerial and Decision Economics, 2019)
The use of stochastic differential equations offers great advantages for statistical arbitrage pairs trading. In particular, it allows the selection of pairs with desirable properties, e.g., strong mean-reversion, and it renders traditional rules of thumb for trading unnecessary. This study provides an exhaustive survey dedicated to this field by systematically classifying the large body of literature and revealing potential gaps in research. From a total of more than 80 relevant references, five main strands of stochastic spread models are identified, covering the ‘Ornstein–Uhlenbeck model’, ‘extended Ornstein–Uhlenbeck models’, ‘advanced mean-reverting diffusion models’, ‘diffusion models with a non-stationary component’, and ‘other models’. Along these five main categories of stochastic models, we shed light on the underlying mathematics, hereby revealing advantages and limitations for pairs trading. Based on this, the works of each category are further surveyed along the employed statistical arbitrage frameworks, i.e., analytic and dynamic programming approaches. Finally, the main findings are summarized and promising directions for future research are indicated.
Keywords: statistical arbitrage; pairs trading; stochastic models; mean-reversion; stochastic differential equations